Monte Carlo Simulation in Financial Risk
Management
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
Monte Carlo simulation is a method used to estimate risk involved in the occurrence of an
event. Ideally one will be presented with several alternatives to perform certain task, and by
the use of Monte Carlo simulation informed decision can be made choosing the correct or the
most convenient course of actions. The mechanism of Monte Carl simulation is a system model
will be generated multiple time based on probability models that best suite the key factors.
Each time the input value will be changed, on random basis. The model is mostly appropriate
on trying to reach a rounded opinion on decision problems like a company profit that is derived
from set of factors as associated costs, advertisement and market share. The idea of this
simulation tool is to treat basic variables with probabilistic nature instead of the deterministic
approach that may misrepresent the real life. Those variables will be assigned a random number
that will differ on each run. On performing many runs, probability distributions can fitted on
the outcome to study and analysis it. The simulation process helps the decision maker to
enumerate various instances where the involved factors may be interrelate. This paper will look
into the literature and how scholars made use of simulation tools, in general and Monte Carlo
simulation in specific, to help on accessing, mitigating and even reducing the financial risks.
On the paper published by Bandaly, and his colleagues (2014) on financial risk management
and how it is considered as an important tool for visualizing and recognizing the real nature
and integrative scope of supply chain management; the authors defined supply chain risk
management (SCRM) is an evolving discipline in which holistic solutions across the various
sectors typically do not harmonize. Since organizational risk management encompasses many
areas, such as sourcing, accounting, sales and advertising, risks mitigation strategies in the
supply chain, and as it needs to address a lot of the involved fields, Bandaly research aims to
reduce the gap between these fields by creating an integrated SCRM methodology utilizing
financial instruments and operative processes. Authors investigated an aluminum supply chain,
a manufacturer and a retailer. Aluminum cost volatility and speculation about the product
market were related to the supply chain. In order to manage the risk along the supply chain, a
stochastic optimization framework is created.
Using this optimization framework as a guide, Bandaly and his colleagues compared the
performance of an adaptive risk management system with the sequences of a design, under
which decisions on financial risk management are taken after decisions on operational risk
management are finalized. Through modeling on a simulated basis and using simulations and
statistical analyses, they evaluate the output of both models in order to minimize the total
expected supply chain opportunity cost. They look at supply chain performance based on three
considerations, at different levels: risk tolerance, uncertainty in demand and fluctuations in
aluminum prices, the commodity of study. In most instances, they find that their findings on a
unified system beats a series model. While the findings often show that its reliability is
increased by the conventional supply chain, a criterion is possible that does not require the need
of higher levels of risk. Of various business situations evaluated, management lessons are given
from past experts opinions on how to build a better holistic approach.
in the fifth chapter of his book "Supply chain risk management", Olson (2014) described
particularly how nature is uncertain; hence, whatever model they are trying to build based on
deterministic approach may be unrealistic. So every model that they build incorporates some
level of risk due uncertainty. In addition, the more they are aware of the associated risk in their
models and accounting for it, the more accurate their work will be. Risk in supply chain models
can arise from unpredicted weather conditions like a natural disaster that can hinder certain
logistical operation. Political unrest, economic situations, and major industrial accidents are
other factors that can affect well-established supply chain models. Mostly, to account for these
risks, they have to use probability to change the deterministic nature of their models. This
mandates the use of Monte Carlo simulation in supply chain professionals work.
One of the topics discussed by Oslon (2014) is the idea of an inventory. He began with a brief
description of what inventory can be; which was defined as, the resources that are stored that
is exceeding current usage to be used in the future. Inventory is important because demand and
supply are not always matching. The probability of a stock out event decreases with a higher
inventory level. Stocking out is not favorable because its cost is not just represented in lost
sales. It can lead to customers' dissatisfaction and for them to start looking after alternative
products, a matter that can put sales mission under unfavorable risk. On the other hand, building
inventory means more cost and tied up capital, this is a risk on its own, because now the chances
of bankruptcy, given that sales isn't meeting their targets, is higher due to the large amount of
money invested in keeping higher inventory levels. The problem is like a balancing problem
that should be maintained to a certain level with the introduced variability. Inventory costs can
be broken down, for further analysis, into holding costs, ordering cost, shortage cost, and
purchasing cost. Holding cost is the cost associated with keeping commodities in my inventory
and special treatment expenses that should be accounted for, like refrigeration. Ordering cost
represented in the administrative work and expenses associated with requesting an order from
the suppliers. Shortage cost, is represented in the amount of back order that can be accumulated
due to inability to satisfy all the demand in a given period, or worse it can be the cost of lost
sales, which will typically be identified on qualitative basis by sales experts. Finally.
purchasing cost is the cost will be paid to the suppliers to pay for certain materials. So, Monte
Carlo simulation can be used to quantify the risk involved here with the four different
parameters. The trade off in the model will be to reach the optimal level minimizing the
expected cost and the probability of stocking out.
The built hybrid SCRM system describes by Bandaly showed how the supply chain risk
management mechanism involving the cooperation of supply chain stakeholders and the
coordination of these members operational units. The system incorporates functional and
financial risk management actions to reduce the expected total expense of a beer, the
commodity of study, supply chain. The authors results show that the integrated model's value
efficiency is better than the sequential model as decisions are made separately by functional
units, and it is more resilient as well, if exposed to evolving business environments. The results
further illustrate the business environment where the unified design led to better results. A less
volatile supply chain, for instance, can have some merit over a conservative supply chain while
working with low demand volatility or low price volatility. The unified model is more
convincing for conservative supply chains as the reduction in overall opportunity cost relative
to the sequential template is important.
Nonetheless, a more dynamic supply chain can still leverage a hybrid model by increasing the
opportunity cost for high price volatility events. The form of risk management approach used
is relatable to how conservative the supply chain model and the volatility in request. When
faced with increased demand volatility, the analyzed supply chain handled risks more using
operative functions methods and less using financial tradeoffs. But since the supply chain is
more aggressive or dynamic in nature, it uses organizational and financial instruments less to
manage risk. The model has been improved by taking into account stochastic simulation lead
times for null outputs and variations in the exchange rates in foreign currencies when a foreign
supplier buys aluminum as per the stock market spot rates. In different organizational and
financial context of risk management, the integrated supply chain risk management model can
be further expanded. The design can include a large range of commodity items and several
suppliers. The decisions incorporated can be formed as a dynamic process. Applying this
approach, it may be more appropriate to use future or forward contracts rather than options.
