Research paper on An analytical overview of the techniques in quantitative risk management.

Running Head: CURRENT TECHNIQUES IMPLEMENTED IN CONSTRUCTION

INDUSTRY TO ELIMINATE SECURITY RISKS 1

Current Techniques Implemented in the Construction Industry to Eliminate Security Risks

Group 4

Balaram Chekuri

Laxmi Sravani Vallurpallis

Mohan Kadali

Shivasai Pabba

Vaagdevi Jali

University of the Cumberlands

ITS835-41 Enterprise Risk Management

Residency Assignment Research Paper

Professor Dr. James C. Hyatt

10/20/2019

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Statement of the Problem and it is Setting

Risk management has been one of the breakthroughs for the modern world, and at the same

one of the intellectual achievements is the identification, transformation, or risk and going from a

world that described risk as fate to a world that looks at risk as an area of study. Risk management

is the utilization of risk analysis to come up with management strategies used to reduce risk.

Whereby generally in project management there are the two techniques categories qualitative; that

involves impact and probability assessment, expected value calculations and influence diagrams

and quantitative; that typically focuses on the overall risk which is managed more with numerical

approach, and has techniques such as decision trees, Monte Carlo analysis and sensitivity analysis

(McNeil et al. 2015). In various fields, there is enormous risk faced, and at the same time, there is

a corresponding quantitative technique that can be used to address the risk. The focus is on the

quantitative risk management techniques; they are based on scientific, mathematical and statistical

background, that promise to give thorough and detailed management and quantification of risk that

is imperative for designing the response (Teixeira et al. 2015).

This paper, based on the construction industry, provides an overview of the analytic

overview of different quantitative risk analysis techniques. There is a focus on building on the

existing quantitative techniques that are best for the construction industry around the world when

it comes to utilizing relevant techniques after a qualitative risk analysis. In addition to this, there

is looking further into the techniques and their details.

Guiding Questions

 Does Your Risk Management Process Address Root Cause of Failure?

 Are there gaps in this field?

 What Does Your Business Performance Tell You About Risk?

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 What Do Controls Tell You About Your Risks?

 The main focus is looking at the practitioners and researchers looking: at the reason why they

should simplify the existing techniques?

 Which, at the same looks at the research gaps in this field and propose areas of further research

for project risk management in construction, which will improve the existing techniques.

Assumptions

Project Risk management will foresee and reduce the impact of risk on management.

Schedule, Time, and budget risk are the point of focus in construction.

Project Risk Management has benefitted the construction industry through the use of quantitative

risk management techniques for the analysis of risk (Baker et al.1999).

Construction projects have a complex working environment, thus having the risks.

Delimitations and Limitations

Some apparent limitations and delimitations are presented in this research based on

construction and quantitative risk management techniques that are, according to Bowles &

Chauhan 2004.

Delimitations

 It provides quantification of construction safety aspects, through the utilization of systematic

process based on a risk metric.

 Points toward the direction of better understanding, identification, and communication of

safety issues in construction.

 Assures that critical issues have addressed after consideration.

 Estimated uncertainties have been considered.

 A more in-depth supporting analysis is included.

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Fig1: The basis of formulating the interview questions

Limitations

 Estimation of probabilities is encountered with uncertainty.

 Cost

 Terminology that is new and complex.

 Decision-makers are encountering communication uncertainties.

 Experienced practitioners and standardized procedures are missing.

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Fig2:Risk Management approach at site level

Importance of the Study

The significance of this study is to look at the present qualitative management techniques

and at the same time building on existing quantitative methods used in construction globally.

Looking at the research gaps in quantitative risk management techniques and propose further

research. This will involve practitioners and researchers to stick to the construction of quantitative

risk management and work harder to improve on the present methods and build new quantitative

risk management techniques.

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Fig3: Decision making tree analysis

Review of Literature

According to Thaheem et al. 2012, through a survey, the following revelations were made.

First of all, the study was aimed at getting to know the degree of penetration, acceptance, and usage

of the project management techniques, processes, and software tools in the field of construction.

Among other findings, some revelations were unearthed based on quantitative analysis techniques

whereby 64% of the respondents preferred expert judgment, the rest preferred interviewing as a

technique as a quantitative analysis technique, at the same tim2% of the respondents did not use

any methods of quantitative analysis. Which shows the industry trends in terms of project risk

management. This indicates that there is a problem in the techniques of risk management, that is

why the following literature will look at the various techniques and why they might not be

considered by professionals and what can be improved. The following literature was carried out in

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a comprehensive manner that looked at 12 techniques of qualitative risk analysis, which are

described in the following sections.

Bayesian Method

Reverend Thomas Bayes developed this method in 1764, whereby it was based on the

Bayesian theory that offers “the possibility to use personal and objective probability estimates

changing as new data appear as elements of uncertainty are numerous, subjective and may be

revised, following the acquisition of information." There has been the development of procedures

using the Bayesian models, which are utilized to change the initial values of an aspect-based ion

the results of an experiment. According to (Anderson et al. 1999), the probability of an event to

occur/happen is conditional to the unknown/uncertain event.

Belief Functions Method

This is based on the Dempster-Shafer theory, which is a generalized Bayesian theory that

focuses on subjective probability. This method gives the green light of combining the evidence

sources and results in a degree of belief that takes notes of all evidence available even in cases

whereby sufficient information is inaccessible. Belief Function in a hypothesis is by adding all the

masses of the sets enclosed by the hypothesis. Denoted as ‘Bel,' it measures the evidence strength

in favor of various propositions and ranges from 0 to 1, which is no evidence to certainty,

respectively (Shafer, 1976).

Decision-Making Matrix

This is also known as a risk matrix; it is a graphical tool which is used quantifying risk as

a product of both occurrence and consequences of an event. The information on both the

implications and occurrence of an event is combined with the risk matrix, and that is if the vent

occurs. The main aim of the risk matrix is to prioritize after evaluation of the list of options by

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creating a weighted criteria list for the evaluation of every choice. It is principal based and simple,

at the same time based on the expert judgment in addition to calculations used to obtain the results

of the matrix. There is a lot of subjectivity in this model, which requires prepared and experienced

participants (Barringer, 2008).

Decision Tree Analysis

It is a technique that utilizes graphics and contains the consideration of different situations

and what implications each scenario holds, which compares these after the best option in each case

is chosen. It involves the probability of occurrence, cost of each choice, which helps to assign an

outcome and value. Decision trees are highly efficient in terms of the structure that is used to lay

all the options and the conceivable outcomes each option might be investigated. This technique

helps in creating a picture that is balanced of the regards and risks that are associated with each

possible action taken. The decision tree analysis is flexible, but at the same time, can be applied to

different project situations in risk management. Whereby there are types of risk that the decision

tree can handle, and at the same time, there are types of risks that cannot be handled by the decision

tree analysis (Olivas 2007).

Failure Mode and Effect Analysis (FEMA)

The Failure Mode and Effect analysis is a tool that evaluates risk priorities, evaluates

potential product failures, and helps in the assessment and determination of the impact of the cause

of the risks so that the identified problems might be avoided. After the risk is identified, the

members of the project team assess the extent of the current control mechanism that can detect the

cause of failure before its occurrence, which creates time for corrective action. Therefore, this

technique is utilized for two reasons; prevent operational deficiencies, error reduction throughout

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project development, and to avoid design modification extra cost through discovering the problem

in the initial design (Snee & Rodebaugh, 2008).

Fault Tree Analysis (FTA)

This technique was developed in 1962 by Bell Telephone Laboratories for the US Airforce

to be utilized for the Minuteman system. It is structured a graphic model that has pathways in the

system, which leads to undesirable loss and foreseeable events. The paths in this system

interconnect conditions and events that are contributory by using the standards logic symbols.

Therefore, this system represents a sequential and parallel combination of interrelated logic of

events and possible failures that leads to the undesired event (Clemens 2003). It is imperative to

establish that a fault tree is not suitable for all probable causes of a system failure or a model for

all possible system failures (Ortmeier & Schellhorn, 2006).

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Fig4: Fault Tree Analysis (FTA)

Interviewing

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This technique is used to assess the impact and possibilities of achieving specific aims

based on the input of relevant subject matter experts and stakeholders. The interview is held in a

way that there is an excellent way to get pessimistic, optimistic, and most possible scenarios for

the given objective. Risk practitioners have some risk that has been used to come up with an

estimate which the project manager can use to identify almost 80% of risk through an approach of

a structured interview (Hillson, 2004).

Monte Carlo Method

This method got its code name from the work of Ulam and Von Neuman during world war

II for the atom bomb, where this method was used for integrating mathematical functions. This

means that this technique was approximately developed since the 1940s, with a modern example

of the roulette game, which was a perfect example for random number generation, which was well

known in the Monte Carlo Casinos in Monaco. Whereby until technological advancement and the

rise of computers, this technique was not used as much as before (Pengelly, 2002). This method

mainly is based on the application of the laws of statistics and probability to physical and natural

sciences, and the secret is the multiple distributions of random numbers that show a particular

process inside a chain process. Therefore, this method is perfect for investigating the effect on

strategy the initial risks as non-linear and simultaneous interaction that may have an impact on the

theoretical results.

Probability Distributions

This technique describes how probabilities are spread out during an event. Probability,

according to Simon et al. 1997, can be described as “a measure of the relative frequency or

likelihood of occurrence of an event, whose values lie between zero (impossibility) and one

(certainty)." The graphical illustration is the way probability is used to illustrate risk probability

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that represents the density functions of likelihood. For each distribution of probability, the

horizontal axis shows the impact of the risk event, which may include time or cost, and the vertical

axis shows the relative likelihood and risk event (Evans et al., 2000).

Program Evaluation and Review Technique (PERT)

This tool is handy for organizing, scheduling, and the coordination of various tasks in a

project. This technique was built to simplify the scheduling and planning of large and complex

projects, which are most likely to be construction projects. The main objective of this technique is

to determine the probability, statistically estimate, and establish a range of values for the tasks of

the uncertain project and the time they will take. (ADEAK, 2011). This technique is most useful

when there is uncertainty in the project activities duration.

Scenario Analysis

This is a process of analyzing and estimating future events and looking at possible

alternatives. This technique is used to develop robust strategic plans and manage risks during the

time a project is under uncertainty. The scenario analysis is mostly used for the financial or

economic evaluation of a project. In other worst, it can be described as "the estimation of the

expected value of a portfolio after a given period, assuming specific changes in the values,"

according to McBurney & Parsons, 2003. This technique assists in risk management, augmenting

understanding about the unforeseen future, building consensus used for change; and scanning the

environmental changes and monitoring progress (Maack, 2001).

Break-Even Analysis

This is the most common tool in project management; it is used to study the relationship

between variable costs, fixed costs, and returns. Also known as cost-volume-profit analysis, it is

widely used by management accountants and production management. It is used throughout the

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organization by people as it is a continuous process of thinking because it involves various

decisions. It is mathematical as thee is the breakeven point, which is determined by the number of

units sold (Cafferty & Wentworth, 2011).

Outline of the Proposed Study

All the 12 techniques analyzed are instrumental when it comes to decision making, points

at the consideration of all the alternatives' risks. In some methods, the risk is better quantified that

other techniques, but all the methods require experience that ranges from medium to high level,

detailed data, and rime resources. Another aspect is that the quantitative methods are more complex

and uses more resources than qualitative techniques; the best part is that in terms of risks, the

results are well analyzed and detailed.

From the techniques discussed above, probability distribution, EMV, and interviewing as

transitional, as they are needed to implement the other techniques. There is also a possible lack of

knowledge/complexity of these techniques among the professionals in construction management,

and that is the reason they opt for easily performable or qualitative techniques. This causes a lack

of efficiency in the project risk management processes, even if risk management is done frequently.

Therefore, such revelations point towards increasing the applicability, improving the

existing techniques, or the creation of new techniques. This means that there can be the

implementation of some methods that work well for other real problems; there is a need to improve

the state of knowledge and to develop advanced technologies in quantitative methods, at the same

time considering usability and understandability.

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References

ADEAK, 2011. What is the PERT Method? http://www.adeak.com

Anderson, R., Sweeney, J., and Williams, A., 1999. Statistics for Business and Economics.

Cincinnati, OH: South-Western College Publishing.

Baker, S., Ponniah, D., & Smith, S. (1999). Survey of risk management in major UK

companies. Journal of Professional Issues in Engineering Education and

Practice, 125(3), 94-102.

Barringer, P., 2008. Risk Matrix. Know when to accept the risk. Know when to reject the risk.

Barringer & Associates.

Cafferty, M., and Wentworth, J., 2011. Break-Even Analysis - The Definitive Guide to Cost-

Volume-Profit. (K. A. Merchant, Ed.) Managerial Accounting Collection.

Clemens, P.L., 2003. Fault Tree Analysis, Tutorial, www.sverdrup.com, 4th Edition.

Chauhan, S. S., & Bowles, D. S. (2004). Dam safety risk assessment with uncertainty analysis.

Ancold Bulletin, 73-88.

Evans, M., Hastings, N., and Peacock, B., 2000. Statistical Distributions. John Wiley and Sons.

Fig3 Retrieved from https://www.toolshero.com/decision-making/decision-tree-analysis/

Fig4 Retrieved from https://www.palisade.com/risk/monte_carlo_simulation.asp

Hillson, D., 2004. Effective Opportunity Management for Projects: Exploiting Positive Risk.

Marcel Dekker, Inc.

Kajsa Simu (2006). [Fig1, Fig2] Retrieved from

https://pdfs.semanticscholar.org/504b/67ad668b80f8db0a7c170772c44e9b1754c0.pdf

Maack, J., 2001. Scenario analysis: a tool for task managers. In: Social Development Paper no.

36. Social Analysis: Selected Tools and Techniques. World Bank, Washington, D.C.

McNeil, A. J., Frey, R., & Embrechts, P. (2015). Quantitative Risk Management: Concepts,

Techniques and Tools-revised edition. Princeton university press.

Olivas, R., 2007. Decision Trees. A Primer for Decision-making Professionals.

Ortmeier, F., and Schellhorn, G., 2006. Formal Fault Tree Analysis: Practical Experiences,

Proceedings of AVoCS 2006.

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Pengelly, J., 2002. Monte Carlo methods.

http://www.cs.otago.ac.nz/cosc453/student_tutorials/monte_carlo.pdf.

PMBOK, 2008. Project Management Institute.

Simon, P., Hillson, D., and Newland, K., 1997. Project Risk Analysis and Management Guide.

The Association for Project Management.

Snee, R., and Rodebaugh, W., 2008. Failure Modes and Effects Analysis. Encyclopedia of

Statistics in Quality and Reliability.

Teixeira, A., Sou, K. C., Sandberg, H., & Johansson, K. H. (2015). Secure control systems: A

quantitative risk management approach. IEEE Control Systems Magazine, 35(1), 24-45.

Thaheem, M. J., De Marco, A., & Barlish, K. (2012, June). A review of quantitative analysis

techniques for construction project risk management. In Proceedings of the Creative

Construct Conference (pp. 656-667).

Comment Summary Page 2

1. Do not start out a section using bulleted text. Write out these guiding questions in sentences. 2. Should these be called guiding questions? Are these more hypotheses or something else?

Page 3 3. What other assumptions could be included? 4. Make sure this reference is in APA style 5. Please further define these delimitations. Make these into sentences and paragraphs instead of bullets--and

again, if you do use bullet points, do not start a section with them. Page 4

6. Is this APA style for size and format for Fig. 1? Please refer to the APA manual. Page 7

7. This is not an adequate literature review. Do more research to add quantitative analysis aspects.