RISK MITIGATION AND VALUE ADDITION IN THE CANE SUGAR
AGRO-INDUSTRY SUPPLY CHAIN
INTRODUCTION:
The cane sugar agro-industry is an effort to increase the added value of sugar cane
into sugar that is ready for consumption by the public or as an additive to the food and
beverage industry. The demand for sugar is predicted to continue to increase because it is
influenced by the price of sugar, the increase in population, lifestyle, welfare and income of
the people who are getting better (Marimin et al. 2011; Hairani et al. 2014; Yunitasari et al.
2015). This is also evidenced by the increase in sugar demand of up to 8.77%/year with the
total consumption of sugar. 6.4 kg/capita/year (Rusono et al. 2013; Ministry of Agriculture
2017). The cane sugar agro-industry plays a strategic role in Indonesia's economy,
This is indicated by its contribution of 3.46% to the Gross Domestic Product (GDP)
and involves many farmers and workers from upstream to downstream businesses (Elinur et
al. 2010; Yunitasari 2015; BPS 2017). The considerable contribution of the cane sugar
agroindustry to the economy and society needs to be maintained and continuously improved.
Various efforts have been made including increasing the productivity of sugar factories,
expanding plantation land, increasing land productivity, increasing yields through superior
seeds and factory revitalization efforts. Other efforts to strengthen the cane sugar agro-
industry need to be done from other sides, including by reviewing its supply chain
management and improving performance.
The business processes that occur in the cane sugar agro-industry have a strong
relationship between upstream and downstream activities (Fahrizal et al. 2014), so that
improvement efforts cannot be carried out partially but must be comprehensive. The
relationship between the upstream and downstream sectors requires identification,
measurement and evaluation of the supply chain to determine the direction of improvement.
There are three main aspects that need to be considered in supply chain management,
including performance, added value and supply chain risk. Supply chain performance
measurement needs to be done to create more effective and efficient upstream to
downstream integration (Marimin and Maghfiroh 2010), strategize and control efforts to
achieve goals (Agami et al. 2011) and create a competitive advantage (Rachman 2013).
Referring to Chopra and Meindl (2013), there are six drivers to determine supply chain
performance, namely facilities, inventory, transportation, information, sourcing and pricing
which are then decomposed into supply chain performance measurement metrics.
The enactment of the Plant Cultivation Law No. 12/1992 and Presidential Instruction
No. 5/1997 on the revocation of the Intensified Smallholder Sugarcane (TRI) provisions
pose risks to the upstream sector of the cane sugar agro-industry, including uncertainty in the
fulfillment of sugar factory capacity resulting in decreased production and efficiency. The
cane sugar agro-industry must also bear the risk of seasonal uncertainty and variability
which results in uncertainty in cane production in plantations, harvesting, transportation,
milling and marketing (Everingham et al. 2002; Nicol et al. 2007). Other risks that must also
be faced are a decrease in yield after the harvest period to milling due to transportation and
queuing as well as price risks and labor availability in the plantation. Supply chain
stakeholders also must face risks in the downstream sector, including the global excess
supply of sugar which also results in a domestic sugar price differential of up to 31.7%
(Anjak 2012) and an increase in imports of up to 40.83% (BPS 2017).
Risks in the upstream and downstream sectors of the cane sugar agro-industry require
management efforts so that risks can be minimized through risk mitigation. Supply chain risk
management aims to identify, assess and determine strategic steps to improve aspects that
are vulnerable to risk (Neiger et al. 2009). Vanany et al. (2009) have proven that systematic
risk management can improve company performance and competitiveness. Handling supply
chain risks needs to be done appropriately through a systematic and comprehensive risk
management approach (Septiani 2016).
performance measurement and risk management, also require analysis and
improvement of the added value of the supply chain. Added value is an increase in the value
of a commodity after experiencing additional inputs or further processing in a production
process (Yao et al. 2008). Value-added in the supply chain needs to be properly identified
because each actor has different value changes related to their inputs and production
processes (Ricketts et al. 2014; Deng et al. 2016). The unequal distribution of profits and
added value received along the cane sugar agro-industry supply chain has an impact on
farmers' profits of only 3.4% while traders are 21.4% above farmers' profits (Anjak 2012).
Fair distribution of added value along the supply chain can improve supply chain
performance and efficiency and ensure supply chain sustainability (Hidayat et al. 2012).
The cane sugar agro-industry supply chain as a whole must pay attention to upstream
and downstream integration in order to achieve supply chain objectives. The results of the
analysis have shown that the goal orientation of the cane sugar agro-industrial supply chain
is the improvement of effectiveness which consists of reliability, responsiveness and agility.
Situational analysis related to the current performance of sugar mills and related policies are
also important factors for improving supply chain performance. Performance measurement,
risk identification and mitigation as well as value-added supply chain improvement are
required in an effort to increase productivity and achieve sustainable cane sugar agro-
industry supply chain management.
Sugarcane and Cane Sugar
Sugarcane has long been utilized by humans as a beverage because of its sap content.
This type of plant is grouped as a monocotyledon grass in the order Glumaceae, family
Graminae, group Andropogoneae, and genus Saccaharum. Sugarcane plants can specifically
grow in tropical and sub-tropical climates up to the limit of the isotherm line 20o C, which is
between 19o LU - 35o LS. There are many factors that can affect the growth of sugarcane
including soil conditions, irrigation, rainfall, oxygen conditions in the soil, so that the
location of sugarcane planting greatly determines the quality of sugarcane produced
(Indrawanto et al. 2010).
Morphologically, the sugarcane plant consists of 5 parts, namely the stem, roots,
leaves, flowers and fruit. The stem is the part that is straight with branches and stands
upright and is the most utilized part. Sugarcane stems are generally 3-5 cm in diameter with
a height of 2-5 m. Sugarcane roots are a type of fibrous root that is not long which functions
as a nutrient absorber in the soil and as a support for the growth of sugarcane plants. The
leaves are frond-shaped like other types of grass plants that grow on the left and right of the
sugarcane stem. In the cultivation process, dry sugarcane leaves are always cleaned from the
stems and placed on the ground so that the leaves can grow. It facilitates the harvesting
process and adds nutrients to the soil for sugarcane. Sugarcane flowers are 50-80 cm long, in
which there are seeds, pistils, stigma and ovules. Flowers develop into fruits that have seeds
with large institutions 1/3 the length of the seed (Indrawanto et al. 2010). The seeds are
planted in experimental gardens to obtain new types of superior seeds through genetic
engineering.
Sugarcane plants in Indonesia have long been utilized by the community, especially
sugarcane species Saccarum officinarum. According to Prihandana and Hendroko (2008) the
type and composition of ingredients contained in sugarcane can be seen in Table 1.
Sugarcane can be utilized and produced into white crystal sugar (GKP) through physical and
chemical processes. GKP is one of the food sources of energy which is composed of
carbohydrate compounds. Chemically, sugar can be classified into 2 types, namely
monosaccharides and disaccharides. The type of sugar that is often consumed as an additive
to food is the disaccharide type of sugar, namely sucrose. Sugar production from sugar cane
is carried out through several important processes, namely the process of cleaning and
reducing the size of sugar cane, the process of extracting sap, the process of purifying sap,
the evaporation process, the crystallization process, the cooling process, the sugar separation
process and the packaging process.
White Crystal Sugar is characterized by an International Commission for Uniform
Method of Sugar Analysis (ICUMSA) value of approximately 81-300 IU (BSN 2010). In
more detail, the characteristics and standards of GKP are set for all sugar producers in the
Indonesian National Standard (SNI) for GKP. The SNI for GKP is set out in SNI 3140.3-
2010 in Table 2.
White Crystal Sugar (GKP) Production Process in sugar factory
The stages of the GKP production process in sugar factories (PG) include the stages
of raw material preparation, extraction process, sap purification process, evaporation process,
crystallization process, sugar separation process and packaging process. In this long series of
processes, the main raw material used is sugarcane with by-products in the form of bagasse,
blotong, wastewater and molasses. The flowchart of the GKP production process can be seen
in Figure 2.
The raw material preparation stage includes unloading sugarcane from transportation
equipment, preparing sugarcane to the sugarcane table, cleaning sugarcane and chopping
sugarcane. The process of unloading sugarcane from transportation equipment is carried out
with several auxiliary tools such as sling ropes, hillo unloading units and trucks that can
unload directly through tipping. Cleaning of sugarcane is necessary to minimize soil debris
before entering the milling and refining process, so that the process is more efficient. Direct
watering of the cane before chopping is one of the most common cane cleaning techniques is
carried out in the sugar factory. Shredding is an important process before the sugarcane
enters the milling process. Sugarcane shredding is important to expand the surface of the
sugarcane so as to maximize the juice that can be extracted in the milling process.
The sugarcane milling process is the main process that most determines the level of
efficiency and performance of the sugar factory (Zainuddin et al. 2016). At this stage, the
sucrose contained in the sugarcane is physically extracted through 4 sets of mills and assisted
with imbibition water. The added imbibition water is important to maximize the nira that can
be extracted with the optimal temperature at 800 C (Winata and Susanto 2015). Sugarcane
milling in the first mill can produce first juice (NPP) which is used as the basis for payment
of smallholder sugarcane farmers. The sugarcane in the first mill is sent to the second, third
and fourth mills which produce further juice. The percentage of juice produced at all mills
can be seen in Table 3.
The juice purification process aims to remove impurities and impuritic elements
contained in sugarcane juice that may become obstacles in the subsequent GKP production
process. The common purification processes in Indonesia are defecation, sulfitation and
carbonation, but sulfitation is the most widely used process (Fahrizal 2015). The defecation
purification process is carried out by adding lime to the nira to precipitate impurities and
accelerated by heating the nira. The negative impact of this refining process is that the sugar
produced in the defecation process can be brown in color which tends to be disliked by
consumers.
The sulfitation purification process separates impurities in the nira by utilizing
acid/base chemical reactions in excess and then neutralized. The chemicals used in the
sulfitation purification process are lime milk, SO2 and Ca(OH)2. The carbonation
purification process is carried out by mixing dirty nira with CO2 gas so that dirt clumps occur.
The carbonation refining process is able to remove a greater amount of impurities and the
quality of sugar produced is also better than the defecation and sulfitation processes
(Perwitasari 2010). Impurities that have clumped and separated with pure nira are then
filtered with a Rotary Vacuum Filter (RVF) and produce by-products in the form of blotong
of 2.5-3.8% sugar cane which can be used for fertilizer and cement raw materials (Supari et
al. 2015).
The evaporation process aims to evaporate the water content in sugarcane juice to
reach a concentration of 60-65 Brix. The evaporation process must take place as quickly as
possible, so that there is no damage to the juice due to the influence of high temperatures in
the evaporator. The nira evaporation system in sugar factories generally applies the
Quadraple Effect Evaporation Technique, which is that every 1 kg of steam can evaporate 4
kg of water with 4 series of evaporators (Sartika 2005). Condensate water from evaporation
can be recycled for the cooling process while the thick nira is forwarded to the cooking or
crystallization station. The mechanism of the evaporation process can be seen in Figure 4.
Cane Sugar Agro-Industry Supply Chain Management
Various studies related to supply chains and supply chain management have been
widely published and systematically researched. The field of supply chain management has
become a serious concern for many researchers in recent decades, driven by the ease of
access and availability of information (Chan and Chan 2005). The development of supply
chain research is also driven by the tendency of inter-connection between raw material
suppliers and factories that encourage more independent and higher levels of supply chain
complexity (Kamalahmadi and Parast 2016).
The supply chain can be defined as a large number of value chains connected in
inter-organizational relationships upstream and downstream to ensure the smooth flow of
money, materials, products and information from raw material providers to end consumers or
vice versa and involves all stakeholders (Oliveira et al. 2016). In the supply chain, an effort
is needed to organize all affairs in the chain, which is hereinafter referred to as supply chain
management. Supply chain management needs to be done well to ensure the achievement of
supply chain goals and minimize the potential for risks in the supply chain network.
Supply chain can be defined as a system that connects various stakeholders with each
unique characteristics and behaviors to achieve individual goals or supply chain goals.
Various applications of supply chain management have been widely applied to various types
of companies, such as construction companies, food industry, agriculture, health, e-
commerce and textile industry (Chan and Chan 2005; Ngai and Wat 2005; Faisal et al.
2012). Improving supply chain performance can start from the company's ability to improve
and maintain effectiveness and efficiency in direct contact with suppliers and consumers or
distributors.
The supply chain management model has its own characteristics in each application
in the company or industry. The real difference lies in the purpose of the chain and the
stakeholders involved in it. Supply chain management in agriculture has more complexity
than supply chain management in manufacturing and textiles (Ahmad and Jamshed 2015;
Septiani et al. 2016). The complexity of supply chain management in agriculture is related to
the transfer of raw materials from one location to another. Agricultural products that are
moved from suppliers or plantations have a high probability of damage and quality decline.
The complexity of the agricultural supply chain is also influenced by the properties of
agricultural products that areamba and seasonal so that better management efforts are needed
to optimize them.
The complexity of supply chain management and implementation in agriculture has not been
explored in much detail (Higgins et al. 2007), nor has supply chain management in the cane
sugar agro-industry. The challenge is that cane sugar agro-industry supply chain
management has a greater level of complexity than other supply chains, as described by
Chiadamrong and Kawtummachai (2008) below.
⮚
Supply chains in the cane sugar agro-industry are substantially related to climate and
variable uncertainties in the biology and physical properties of raw materials.
⮚
The supply chain in the cane sugar agro-industry is related to various decision-making
actors in each sector.
⮚
Cane sugar agro-industry supply chains vary in scale and sector (such as plantation,
factory and processing and marketing sectors) and have their own objectives with different
types of constraints.
In addition to the above constraints, the cane sugar agro-industry supply chain also has
several barriers and limitations when viewed from the input and output aspects of the supply
chain. Barriers in the input aspect found in the field include fertilizer problems and problems
in the transportation aspect. Fertilizers circulating in the market have low quality and high
prices, which can reduce the productivity of sugarcane fields. In the transportation aspect,
there is often a delay in the process of transporting sugarcane, which has the potential to
reduce yield after post-harvest. In the output aspect of the supply chain, the problems that
arise include products that are still below quality standards and the packaging process can
reduce the quality of sugar.
Following the definition of supply chain according to Chopra and Meindl (2013), cane
sugar agro-industry supply chain management not only involves processing factories, but
also involves plantations, raw material distributors, and product marketing. The cane sugar
agro-industry supply chain model in terms of raw material fulfillment consists of two
sources, namely the fulfillment of raw materials from company-owned plantations and the
fulfillment of raw materials from smallholder sugarcane farmers (Neves et al. 2010).
The sugarcane raw materials obtained from the two sources must comply with predetermined
quality standards which are then processed in the sugar factory according to a predetermined
time. The processed products can be sold through various channels, such as distributors, the
food and beverage industry, or the cosmetics industry.
Supply Chain Risk Management
Many studies prove that effective supply chain management can improve the
performance of a company or organization and increase the ability to compete (Vanany et al.
2009). Supply chain effectiveness can be influenced by many aspects, one of which is risk.
Research on supply chain risk management has evolved due to the development of
information technology, uncertain global economic conditions, increasingly complex supply
chain conditions due to company mergers and unique business operating models (Trkman
and McCormack 2009). These factors lead to the vulnerability of companies and their supply
chains to potential sources of risk. The company's vulnerability to risk is not only caused by
global factors, but is also influenced by local company factors that can be sourced from the
environment, organization and supply chain whose variables cannot be predicted with
certainty but greatly affect the results of supply chain activities. The risks that arise in a
company follow the increasingly complex supply chain model being implemented. Potential
risks do not only come from traditional sources of risk but also due to collaboration between
supply chain networks and business models.
Supply chain risk management is the identification of potential sources of risk and
the implementation of appropriate strategies through a coordinated approach between supply
chain members with the aim of reducing supply chain vulnerability. Supply chain risk
management seriously considers the sources of risk along the supply chain and how to deal
with or minimize these risks (Kamalahmadi and Parast 2016). Supply chain risk
management has caught the attention of many companies because it is proven to effectively
mitigate risks, avoid excessive use of costs, efficient reduction of disruptions and relatively
faster company recovery time when a risk cannot be avoided. Related to the coordination
between stakeholders in the supply chain, supply chain risk management is supply chain
management that minimizes risk through collaboration and coordination between
stakeholders in the chain with the aim of achieving profit and smoothness of the chain.
In relation to decision theory, risk can be defined as the distribution of the probability
of an event occurring, the value of which is subjective and the result can be a positive
deviation or negative deviation from the expected outcome (Suharjito 2011). Supply chain
risk generally involves more than two companies in a flow of cooperation that affects each
other. Internal risks in companies involved in the supply chain network can have an impact
on other companies. Thus, if the company is not able to handle its internal risks well, it can
be a threat to the smooth running of the supply chain. Chapman et al. (2002) state that the
focus of risk management in the supply chain is to understand and overcome the chain risks
that may occur at one point in the supply chain network. In the event of a disruption, it also
prepares companies or other actors in the supply chain to be able to return to normal and
continue their business processes. The high level of complexity and interdependence in
today's supply chains is a challenge to deal with the possibility of risks and their impacts. In
addition, risk management challenges in the agro-industry are higher than in the
manufacturing industry related to the nature and source of raw material procurement (Ahmad
and Jamshed 2015; Muchfirodin et al. 2015), uncertainty and complex and unpredictable
relationships between supply chain actors in the supply of raw materials.
Various research methodologies on supply chain management have been developed
to answer practical problems faced by companies. In general, supply chain risk management
follows steps to minimize risks in the supply chain by (1) risk identification, (2) risk analysis
( 3) risk management and (4) risk monitoring (Liu et al. 2007; Trkman and McCormack
2009). Risk identification is the first step in risk management that must be recognized. Risk
identification in the supply chain aspect is not only seen in the aspect of each actor involved,
but also pays attention to potential risks in the supply chain network and its environment.
Risk identification determines the steps of supply chain risk management, because if there
are risks that are not identified, the direction of risk management will not be on target.
The steps of supply chain risk management more specifically follow the steps of
identifying potential risks, assessing the probability and severity of risk impact on the supply
chain, determining priority risks that must be addressed first to develop mitigation and action
plans to reduce impact. In addition to the division of supply chain risk management into 4
steps above, Zsiding and Ritchie (2009) added the need for (5) organizational and personal
learning and knowledge transfer. This 5th step seeks to learn the risks that it accepts itself
and spread it to other stakeholders in its supply chain so that it can be quickly mitigated
properly and not cause greater losses.
Many methods have been developed in supply chain risk management to address
challenges in the industry or the development of new methods that are closer to real-world
problems. In fact, supply chain risk management methodologies are mostly developed
qualitatively (Vanany et al. 2009; Sherwin et al. 2016). Identification and mitigation of risks
in the supply chain should no longer rely entirely on qualitative models because according to
Fiksel et al. (2015) the ineffectiveness of supply chain management occurs due to the lack of
statistical and real information on the potential risks to be faced. Sáenz and Revilla (2014)
added that around 60% of company managers are unaware of the risks and the magnitude of
their impact on the company because the risks are only described without being shown in
real terms.
In some previous studies, there are several things that have not been considered,
including the use of technological aspects in risk mitigation efforts, efforts to use
information technology have not been maximized so that they can increase the posibility of
supply chain risks and there has been no research that refers to decision making in supply
chain risk management (Vanany et al. 2009). In fact, the supply chain decision-making
aspect of risk management is very important to consider because it is beneficial to risk
sharing, risk transfer and risk mitigation in an effort to improve performance and minimize
supply chain risks.
Supply Chain Value-Added Concept
Value-added analysis is one of the methods used in identifying supply chains from
upstream to downstream. In simple terms, value-added is an increase in the value of a
commodity because the commodity experiences additional inputs or further processing in a
production process. The concept of value-added can be analyzed in each constituent element
of the supply chain, because each element has different value changes depending on the
inputs in the production process (Marimin and Maghfiroh 2010). Changes and increases in
added value that occur along the supply chain are highly dependent on certain inputs,
processes and treatments that can improve quality.
Value-added analysis on agro-industrial products can be seen from the upstream
sector to the downstream sector. In the analysis of the added value of the upstream sector,
the added value obtained by raw material providers involving supply chain actors can be
identified in the first sector, such as farmers. Value-added analysis in the downstream sector
can identify the amount of added value obtained when processing raw materials into
products. Analysis in the downstream sector can involve agro-industry product producers.
Value-added analysis can also be seen in the retail sector, as a distributor that delivers
products from the downstream sector to the final consumer.
Value-added analysis in agro-industrial supply chains needs to be done to identify the
distribution of costs and benefits to each actor in the supply chain. This is related to the
guarantee of the sustainability of the supply chain will be better if the division and
distribution of costs and profits goes well in accordance with the agreed scenario and the
wages of the efforts that have been made (Bunte 2006). Value-added information in the
supply chain also needs to be well known as a basis for investors to invest in the field
(Hidayat et al. 2012).
Value-added and profit in the supply chain determine the financial condition and
competitive advantage of the business process (Frumkin and Keating 2011), making this
information very important to know. Value-added information is also needed to understand
the complex relationships in the supply chain and determine the performance of each
stakeholder in the supply chain (Rich et al. 2011; El-Sayed et al. 2015). In addition, value-
added information in the supply chain also needs to be well known to open up investment
opportunities, providing significant benefits to actors and supply chains (Hidayat and
Marimin 2014; Deng et al. 2016). Frumkin and Keating (2011) argue that value-
added/revenue analysis can provide information on the company's financial health and
competitive advantage position.
Supply Chain Performance Measurement and Improvement
Supply chain performance measurement is carried out to evaluate and monitor the
supply chain management policies carried out by an organization or company. This
evaluation and monitoring can be done by calculating the effectiveness and efficiency of a
company in implementing current supply chain management (Uysal 2012). Effectiveness is
broadly defined as how to meet consumer demand while efficiency can be defined as the use
of resources economically and being able to meet consumer satisfaction.
Good supply chain performance measurement is carried out at all levels and elements
that make up the industrial supply chain. This needs to be done because the supply chain is a
system that is coordinated and influences each other to achieve common goals. Supply chain
performance measurement can assist companies in achieving previously formulated goals
based on strategies to achieve the goals set by the company (Agami et al. 2011).
Furthermore, supply chain strategies can be formulated based on the results of supply chain
performance calculations, to minimize constraints in achieving supply chain goals (Uysal
2012).
Rachman (2013) explains that a supply chain performance measurement system is
needed to monitor and control, communicate and analyze the supply chain performance The
measurement of supply chain performance is a fundamental need in a company or
organization, from organizational goals to supply chain functions, knowing the position
relative to competitors and determining the direction of improvement to create
competitiveness. Indirectly, supply chain performance measurement is a comprehensive
evaluation tool of the company's productivity and then implement it to improve better
effectiveness and efficiency. Thus, the company's competitiveness is getting better by
producing better performance values. There are several things that need to be considered in
measuring supply chain performance, including 1) determining the areas to be measured and
monitored, 2) the time of measurement taken, 3) the relative importance of one measure to
another and 3) the party responsible for the measure taken.
Improving supply chain performance in several studies is done by formulating strategies to
improve supply chain performance, such as those conducted by Li et al. (2016); Purwani and
Nurcholis (2016) and Qi et al. (2017). The formulation of supply chain performance
improvement strategies departs from the results of the supply chain performance assessment
that has been carried out previously. The supply chain performance improvement strategy
should be appropriate and able to minimize the performance loss found in the supply chain
performance assessment results.
Analysis Support Techniques
Supply Chain Operation Reference (SCOR)
The cane sugar agro-industry supply chain involves multi-actors and multi-sectors, so
the scope of the analyzed system is complex. According to (Bittencourt and Rabelo 2008)
performance measurement in complex business processes requires performance
measurement metrics. In each business process, these performance measurement metrics
have different levels of importance (weight), depending on the characteristics of the business
process (Palma-mendoza et al. 2014).
The source of performance metrics used in this study is the SCOR (Supply Chain
Operation Reference) metric while the determination of the weight of performance
measurement metrics is obtained by fuzzy-AHP. The determination of the weight of the
supply chain performance metrics is also in accordance with the SCOR metrics which
consist of several levels of metrics in a hierarchical manner (Huan et al. 2005).
SCOR is a standardized guideline that can help companies evaluate performance
through the identification and calculation of supply chain performance metrics (Kasi 2005).
SCOR consists of 3 hierarchical levels that are mutually decomposed, namely the process
type level, the process category level, and the process activity level, whose work stages start
from the identification stage at each hierarchical level according to the business process to be
evaluated (Palma-mendoza 2014).
Fuzzy-Analytical Hierarchy Process (AHP)
Analytical Hierarchy Process (AHP) is one of the multiple criteria decision-making
tools introduced by (Saaty 1980). The AHP method can simplify complexity to be simpler
through elements of hierarchy. The elements of the hierarchy are then compared with each
other using the pair-wise comparison technique so that a nominal scale value is obtained for
each element (Bozbura and Beskese 2007). The nominal scale obtained is the relative value
of all factors in the hierarchy and can be used as a decision alternative by comparing it with
the weights of other decision alternatives (Torfi and Rashidi 2011).
The development of research methodologies makes the AHP method continue to be
criticized for its inability to solve uncertain decision-making problems. Patil and Kant
(2014) explain that there are 4 shortcomings of the AHP method developed by Saaty in
multiple criteria decision making, namely (1) the AHP method is mostly used in definite
decision-making applications, (2) the AHP method is built for unbalanced assessments (3)
the AHP method is unable to solve problems that are uncertain and ambiguous to explain the
assessment of an expert, (4) the ranking process built at the end of the AHP method decision
is uncertain and (5) the selection of the subject being assessed and the selection of experts
has a very large influence on AHP results. Fuzzy assessment is needed to anticipate the
shortcomings of AHP which is unable to solve decision-making problems that are uncertain
and ambiguous. The use of fuzzy assessment techniques in AHP is more in line with
ambiguous human linguistic language so that the decisions obtained are in accordance with
real situations (Dargi et al. 2014). Based on various problems and obstacles found in the
AHP method, the AHP method was then developed and combined it with a fuzzy approach.
Fuzzy AHP is a method used for solving complex problems through the combination of fuzzy
theory and hierarchical structure analysis. Problem solving that is uncertain and ambiguous
from an opinion, is considered to be resolved and translated through a fuzzy-AHP approach
through a fuzzy decision-making process according to El-Baz (2011) which can be seen in
Figure 5. The use of this method can make it possible to process qualitative and quantitative
data, so that decision makers feel more confident in their decisions (Marimin et al. 2013).
The Fuzzy-AHP approach requires a hierarchical model before pairwise comparisons
are made with conventional AHP. Once this hierarchical model is built, the expert is asked to
compare the various elements within the hierarchical model at each level. In a conventional
AHP assessment, using a rating scale of 1-9. This rating scale is further developed in the
fuzzy AHP approach into 5 triangular fuzzy numbers defined with associated membership
functions. The definition and membership of fuzzy numbers can be seen in Table 4.
Value-Added Analysis
Value-added analysis is one of the methods used in identifying supply chains from
upstream to downstream. In simple terms, value addition is an increase in the value of a
commodity because the commodity experiences additional inputs or further processing in the
production process, thus increasing the value or price of the commodity (Yao et al. 2008).
Increased value means increased utility in the form of the product, the place and time of
availability of the product, and the benefit of the product to the consumer (Mikkola 2008;
Taylor and Fearne 2009). Value in the supply chain is created specifically by a series of
activities and relationships of each stakeholder, the amount of value added will be
determined by how much the end consumer pays for it (Kirimi et al. 2011; Trienekens
2011).
Various value-added calculation methods have been developed to provide
information on the added value of a business. Value-added calculation models that are often
used to identify supply chains include the concept of value-added calculation by Sudiyono
(2010) and the Hayami value-added calculation model (Hayami et al. 1987). Hayami's value-
added calculation can analyze product information and profits obtained by the company. The
weakness of Hayami's value-added calculation method is that it can only be applied to one
type of product in one supply chain model or company, so modifications are needed. Hidayat
et al. (2012) modified Hayami's value-added calculation method to analyze the added value
in the palm oil agro-industry. The framework of the value-added analysis model by Hidayat
et al. (2012) can be seen in Table 5.
3
c. Value added per farmer Rp/month
Value-added calculation methods are also widely used in the manufacturing and
financial sectors, such as the Economic Value Added (EVA), Cash Value Added (CVA) and
Taylor and Heyes value-added calculation methods. The EVA value-added calculation
method was applied by Kyriazis and Anastassis (2007) to the financial sector, and proved
that the EVA method has a stronger relationship to stakeholder-based value-added analysis.
The Cash Value Added method is a modification of the combined calculation method
combining the Operationg Cash Flow (OCF) method and the operating cash flow demand
(OCFD) method (Ottosson and Weissenrieder 1996). The Taylor and Heyes value-added
calculation method is applied to the calculation of value-added in real estate (Brasington and
Haurin 2006).
In another study, Suharjito (2011) formulated value-added as a profit optimization
model that considers investment factors, operational costs, the cost of bearing the risk of
product selling prices and the number of products sold. Saputra (2012) calculated the value-
added gain for each product supply chain stakeholders by considering the fixed price of the
product plus the incentives they are entitled to receive. Incentives consist of performance
factors and risk weights of stakeholders in the supply chain.
House of risk (HOR)
The HOR model was first developed by Pujawan and Geraldin (2009) as a
modification of the Failure Mode and Effect Analysis (FMEA) model in order to prioritize
risks that need to be followed up effectively in order to reduce potential risks from risk
sources. House of risk is integratively able to identify and mitigate risk agents and factors
simultaneously and comprehensively. Risk identification and mitigation efforts are
specifically divided into the House of risk 1 model and the House of risk 2 model, but the
stages of completion are carried out in a coordinated manner. The house of risk model makes
extensive use of expert discussions and assessments as well as relevant stakeholders to verify
and validate the identified risks.
The House of risk 1 model is a risk identification model and determines the priority
risks that must be minimized in business processes in order to achieve business goals. Risk
identification in business processes can be done by field observation, literature study and
interviewing experts or stakeholders who are directly involved in the business process. The
HOR 1 model sees risk factors and agents as objects to be analyzed and identified. The HOR
1 assessment framework can be seen in Table 6.
Framework of Thought
Indonesia's cane sugar agro-industry has a strategic role in the economy and
community welfare. The strategic role of the cane sugar agro-industry can be seen from its
good contribution to GDP from the plantation and agriculture sectors. This strategic position
is also strengthened by an increase in demand influenced by an increase in population and
people's lifestyles. In fact, the condition of the cane sugar agro-industry is not entirely good
and has various problems that need to be resolved, including inefficient supply chain
management, poorly managed risk mitigation efforts that can threaten business sustainability
and the absence of real commitment and evaluation of increasing productivity and
production yields. These obstacles should be a serious concern because they can worsen the
condition of the domestic cane sugar agroindustry which can harm all stakeholders involved
in it.
A descriptive-quantitative situational analysis is needed to determine business
conditions, supporting and inhibiting factors for the progress of the cane sugar agro-industry
sector. Situational analysis of the cane sugar agro-industry supply chain pays attention to
regulatory aspects and supporting policies because this business sector is strongly influenced
by government regulations. Situational analysis can provide information on chain
performance and its competitive position as well as opportunities for performance
improvement. Through situational analysis of the cane sugar agro-industry supply chain, the
competitive position, potential for performance improvement and policy recommendations to
support risk mitigation and increase the added value of the cane sugar agro-industry supply
chain can be identified.
Supply chain performance efficiency is a key aspect in ensuring the smooth running
of the cane sugar agro-industry business process. The efficiency of cane sugar agro-industry
supply chain performance can be determined by identifying the supply chain model and
strategy implemented and knowing the points in the supply chain that can threaten the supply
chain business process. The main efforts that need to be made are improving supply chain
performance, increasing added value to each stakeholder and determining mitigation
measures in an effort to minimize the impact of risks along the supply chain.
Performance measurement, risk mitigation and value-added improvement of the cane
sugar agro-industry supply chain involve many stakeholders in different sectors and
objectives. This means that the object of study in this research is complex and requires an
appropriate research approach so that the conclusions produced are in accordance with the
actual situation. To address this, this research refers to the system approach, which observes
the object of research in accordance with the characteristics and behavior of the system and
produces conclusions in accordance with the needs of the system. A systems approach is
needed to solve problems that are complex, uncertain and involve multiactors and multi-
sectors (Suharjito and Marimin 2012).
The research framework begins with a situational analysis and continues by
answering the research questions that have been presented in the following sections
Introduction. The situational analysis relates to the policy and objective conditions of
Indonesia's cane sugar agro-industry. Then, the research continues by answering the research
questions accommodated in the research objectives. Finally, managerial implications and
recommendations for improving the productivity and efficiency of the cane sugar agro-
industry supply chain are generated. The research framework can be seen in Figure 6, the
research design based on objectives, variables, methods and research outputs can be seen in
Figure 7.
Research Stages
The research stage begins with a needs analysis, problem identification and system
identification. This stage is the initial stage in the system approach framework. The system
approach identifies problems based on the nature and characteristics of the system. The
system approach is characterized by an assessment of the influential factors in the system
and the design of the model needed as a solution in achieving goals (Eriyatno and Fadjar
2007). Needs analysis relates to the needs of elements in the system, which in this case are
stakeholders who are directly or indirectly involved in the cane sugar agro-industry supply
chain. Problem identification and formulation are related to the constraints that must be
faced in achieving system objectives. System identification is depicted in input-output
diagrams and cause-and-effect diagrams to describe factors and system requirements.
The next stage of the research is the identification of the cane sugar agro-industry
supply chain mechanism. This identification can be obtained from situational analysis,
literature review results and field observations. After situational analysis and supply chain
identification, the research continued with system analysis and modeling. The system
analysis and modeling stage is divided into 3 stages, namely supply chain performance
assessment, analysis and calculation of added value and identification and mitigation of
supply chain risks.
The identification of supply chain configuration was analyzed through Vorst's (2006)
approach, which reviewed chain resources, chain network structure, chain business processes
and chain performance. Next, the supply chain performance measurement was modeled by
adopting the SCOR framework and fuzzy-AHP technique. The value-added calculation stage
is calculated through the modified Hayami method. The modification is based on the initial
supply chain identification results and some assumptions and adjustments. The modeling
stage of risk identification and assessment begins with the identification of risk events and
risk sources based on the SCOR framework and the decomposition of chain activities with
hierarchy task analysis (HTA). Risk assessment was conducted by expert practitioners and
academics through the fuzzy-house of risk (HOR) model. Supply chain risk assessment with
the calculation of aggregate risk potential (ARP) can provide priority information on risks
that must be mitigated immediately.
The verification and validation stages of the model are needed to ensure that the
calculation results and the calculation model built are in accordance with the results of the
system identification carried out at the initial stage (Sargent 2013). Validation in this study
was carried out by face validation by confirming with practitioners and experts who have
knowledge and experience in the aspects of performance, added value and risk of the cane
sugar agro-industry supply chain. Verification is done by black box testing by looking at the
consistency and calculation errors in the model and in accordance with system requirements
(Sommerville 2011). In the last stage, synthesizing and formulating the managerial
implications of the research results for the company's managerial. Managerial implications
are needed to determine the operational implementation of strategies in accordance with the
recommendations in this study. The flowchart of the research stages can be seen in Figure 8
while the methods and outputs of each research stage can be seen in Table 8.
1. Needs analysis
Needs analysis is needed as the beginning of an assessment of a system, based on the
needs of each element in the system. At the needs analysis stage, identification is carried out
on the parties involved directly or indirectly in the cane sugar agro-industry supply chain.
2. Problem Formulation
Problem formulation is a statement that is built based on the difference between the
needs of the actors and the goals to be achieved when building the system (Fahrizal 2015).
To be able to provide a statement of the problem formulation of the sugar agro-industry
supply chain, sugar cane requires identification of the problems found in the system. The
following are some of the results of the initial identification of the problems that exist in the
sugar cane agro-industry supply chain.
a. The low quality of sugarcane entering the factory affects the quality of sugar
produced and the selling price of sugar.
b. The unpredictability of farmers' sugarcane productivity is directly related to the
uncertainty of weather, climate, pests and plant diseases.
c. The overall supply chain performance of the cane sugar agro-industry is still low.
d. Information distortions in the supply chain lead to instability in sugarcane prices or
sugar prices.
e. The number of supply chain risk events and sources that cane sugar agro-industry
supply chain stakeholders must face.
f. There is no structural and rapid mitigation effort in the event of a disruption at one
point of the cane sugar agro-industry supply chain.
g. The low bargaining position of sugarcane farmers is due to low land productivity and
limited access to technology and information.
h. The disproportionate sharing and distribution of risks and benefits between actors in
the cane sugar supply chain network so that farmers experience higher risks and uncertainties
because they are directly faced with natural disturbances, pests and plant diseases.
3. System identification
System identification is the stage of recognizing system needs and as a basis for
designing system requirements. System identification is a chain of relationships between
statements of needs and specific statements of problems that must be solved to meet the
needs described in the form of a causal loop diagram and input output diagram. In
identifying the system, it needs to be described using cause-and-effect diagrams and input-
output diagrams. The causal loop diagram and input-output diagram can be drawn on the
situational analysis of the cane sugar agro-industry supply chain obtained from the results of
previous research studies, supply chain stakeholder interviews, expert opinions and field
observations.
4. Identification of Cane Sugar Agroindustry Supply Chain
In order to answer research objective 1, it is necessary to identify the supply chain, as
a preliminary research step. The cane sugar agro-industry supply chain was identified using a
descriptive-qualitative method supported by the opinions of practitioner resource persons,
field observations, and literature studies. The cane sugar agro-industry supply chain was
identified descriptively according to the Vorst (2006) framework as can be seen in Figure 9.
The four main frameworks required and illustrated in the supply chain identification
according to the Vorst (2006) approach are:
1. The chain structure describes the scope of the chain and the roles of chain
members as well as the agreements that make up the chain.
2. A chain business process is a structured and measurable set of business
activities to produce certain outputs for consumers.
3. Management network and chain describe coordination to carry
out processes in the supply chain by members.
4. Source: chain used to produce products and
deliver them to consumers.
In addition, the business process activities and drivers measured in the cane sugar agro-
industry supply chain stakeholders are also described in business process modeling notation
(BPMN) 2.0.
5. Supply chain performance measurement
Supply chain performance measurement according to SCOR
The design of the SCOR performance measurement model is formulated and
organized into four levels of fuzzy-AHP decision hierarchy namely business processes,
supply chain objectives, performance attributes and performance metrics which can be seen
in Figure 10. The business process level consists of planning, procurement, cultivation,
delivery processing and management. Supply chain objectives consist of effectiveness
improvement objectives and internal efficiency improvement objectives. Supply chain
performance attributes consist of reliability, responsiveness, agility, cost and assets which
are then decomposed into 15 performance measurement metrics.
The responsiveness performance attribute consists of performance metrics of
procurement cycle time, processing/cultivation cycle time and delivery cycle time. All of
these responsiveness performance metrics reflect the average order fulfillment cycle time
required by supply chain stakeholders, starting from raw material procurement, sugarcane
cultivation or sugar processing and product delivery. Procurement cycle time for farmers
means the time it takes to provide sugarcane seeds and other production factors and then
start the cultivation process. Procurement cycle time at the sugar factory is the time needed
to meet the needs of sugarcane and other raw materials so that the factory capacity can be
met. Cultivation cycle time for farmers is the time required for farmers from land preparation
to sugarcane harvesting. Processing cycle time at the sugar factory means the time needed to
process sugar cane into sugar and this relates to the level of efficiency of the factory in
processing sugar cane. Delivery cycle time is the time required to deliver sugarcane to the
factory after harvesting by farmers and the time required to load sugar onto delivery trucks
by the sugar factory. The delivery time at the sugar factory has different dimensions, because
the sugar factory does not carry out the delivery process but is taken directly by the
distributor.
The agility performance attribute consists of the performance metrics of production
capacity increase flexibility, capacity increase adaptability and quality and capacity decrease
adaptability. Capacity increase flexibility means the time required to increase unplanned
capacity by 20%. Capacity and quality adaptability means the maximum percentage amount
that can be done to increase/decrease production capacity in one period. Capacity expansion
flexibility cannot be calculated for farmers, as their business processes are very limited and
rely on natural resources.
The cost performance attribute consists of the performance metrics cost of service,
cost of goods manufactured (COGS) and value at risk. Cost of service means the cost
required to deliver products to consumers. Cost of goods manufactured is derived from the
cost of goods manufactured plus the desired normal margin. Value at risk for farmers is
measured by the opportunity risk of crop failure while for factories it is measured by the risk
of stopping the factory.
Asset performance attributes consist of cash-to-cash cycle time, profit and supply
chain inventory days. The cash-to-cash cycle time for farmers is calculated based on how
long it takes farmers to receive payment from consumers after the sugarcane harvesting
process, while for sugar mills it is calculated based on the payment they can receive after
the sugar sales process in each period. Profit metrics for farmers and sugar factories are
calculated based on the results of the previously formulated value-added calculations. The
number (days) of inventory in the farmers' supply chain is determined based on how long the
farmers have to wait after the sugarcane is harvested until it can be transported to the factory,
while the sugar factory is calculated based on how long the sugar has to be stored in the
warehouse so that it can be sold to the distributor. Expert opinion is needed to clarify the
model and prioritize the weighting of the hierarchy prepared using α of 0.5 and ω of 0.5 in
accordance with the fuzzy-AHP technique. The results of the expert assessment are translated
through Equations 9-16 in the Literature Review section in accordance with the concept of
fuzzy-AHP assessment (Marimin et al. 2013). The weighting of supply chain performance
measurement metrics in this study involved 2 experts in the sugarcane sugar agroindustry
practitioners, especially in the sugarcane farmers and sugar factories and 1 expert from
academia who has expertise in the sugarcane sugar agroindustry.
Performance measurement based on supply chain drivers
Chopra and Meindl (2013) analyzed supply chain performance operationally through
supply chain performance drivers and metrics. There are 6 supply chain drivers to determine
supply chain performance, namely facilities, inventory, transportation, information,
sourcing, and pricing. In this study, the supply chain drivers are decomposed into supply
chain performance metrics and adjusted to the performance metrics according to SCOR as
calculated, analyzed and formulated in Figure 10 and the previous section. The value of
supply chain performance metrics according to the six supply chain performance drivers can
define supply chain objectives for improved efficiency or improved effectiveness
(responsiveness). Both supply chain objectives need to be properly balanced.
The facilities driver covers the physical location of the supply chain where products
are stored and manufactured. Facilities in the supply chain must be responsive and flexible.
Performance metrics derived from facility drivers are capacity, utilization, production
service level, actual flow/cycle time of production and processing/setup/down/idle time. The
better the facilities the supply chain has, the more effective and responsive the supply chain
will be, whereas if the facilities are sufficient, the supply chain will be efficient.
Inventory drivers include raw material supply, production process and product
storage in the supply chain. Inventory drivers are decomposed into cash to cash cycle time,
number of days inventory, average inventory and average replenishment batch size
performance metrics. More inventory can increase effectiveness and responsiveness but will
decrease supply chain efficiency.
Transportation drivers play an important role in moving products/raw materials from
one driver to another or from one stakeholder to another to meet consumer demand.
Transportation drivers are decomposed into the average inbound transportation cost metric.
Faster and more flexible transportation can increase effectiveness and responsiveness, but it
can also decrease supply chain efficiency.
Information drivers cover all supply chain activities and drivers, connect
stakeholders and have a major impact on the supply chain production system. Information
drivers are decomposed into forecast horizon metrics. Better information drivers can
improve the effectiveness, responsiveness and efficiency of the supply chain.
Sourcing drivers are a series of business processes that seek to determine how to carry out
each activity in the supply chain either by the company itself or in collaboration with other
parties. Sourcing drivers are decomposed into supply lead time metrics and franction ontime
deliveries. Determining the right sourcing strategy can improve supply chain effectiveness
and efficiency Driver pricing is a way to determine how much profit can be obtained and in
accordance with consumer and supply chain conditions. Driver pricing is decomposed into
profit margin and average sale price metrics. The right strategy in driver pricing can
increase the responsiveness as well as the efficiency of the supply chain.
6. Supply Chain Risk Identification and Assessment
This risk identification and assessment is modeled following the house of risk (HOR) model.
This research will develop a HOR model for agro-industrial supply chains by considering
aspects of fuzzy assessment. Broadly speaking, the stages of the conventional HOR model
with fuzzy HOR will have the same stages, except that the assessment model and assessment
aggregation are different. The stages of developing the HOR model according to Pujawan
and Geraldine (2009) coupled with the assessment of the fuzzy risk assessment scale are
described below.
Stage 1: Identification of risks that occur in the supply chain.
The HOR framework divides risks into two types, namely risk events and risk agents.
The identification of supply chain risk events is more effectively described in accordance
with the SCOR framework, because it has been able to describe the supply chain more fully.
Risk events are identified for each supply chain stakeholder and then described using a
hierarchy task analysis (HTA) approach. The source of risk (risk agent) in the supply chain
was identified at each chain stakeholder through field observations, interviews and literature
studies. Risk sources are specifically the factors that cause a risk event to occur. This risk
identification stage will produce a hierarchy of the cane sugar agro-industry supply chain
according to HTA, a list of risk events and risk agents in the cane sugar agro-industry.
Stage 2: Assessment of risk event severity and risk agent occurrence rates
The assessment of severity and occurrence rates in this HOR model has the same
assessment method as the FMEA method with a rating scale of 1-.10 (no impact at all to very
impactful). At this stage, the researcher developed a model for assessing the impact level and
risk occurrence level following the fuzzy assessment model so that expert assessments are
not multi-interpretative and consistent. The rating scale is modeled in fuzzy triangles,
because it has been widely used for supply chain risk assessment and can overcome the
vagueness of judgments and conflicting judgments (Bottani 2009; Lee et al. 2015). The fuzzy
risk rating scales for supply chain risk events and sources can be seen in Table 10 and Table
11.
Stage 3: Aggregation of expert judgment
The supply chain risk assessment involved expert practitioners and academics linguistically
in accordance with the risk assessment scales in Tables 10, 11 and 12. There were 5 experts
involved to provide an assessment of the severity level, the level of occurrence and the level
of correlation of events and risk sources, with the following expertise:
1. Two experts came from academia, with expertise in the risks of smallholder
sugarcane farmers, sugar factories and the sugarcane agro-industry supply chain.
2. Two experts were practitioners with expertise in the risk aspects of smallholder
sugarcane farmers, sugar factories, distributors and the sugarcane agro-industry system.
3. One expert came from a researcher who actively examines the business processes of
the cane sugar agro-industry.
The overall expert opinion is then aggregated to get 1 value that represents the overall expert
assessment. The order weighted average (OWA) approach developed by Yager (1993) is
used to aggregate expert opinions as in Equations 20 and 21.
Research procedures Research data collection techniques
There are two types of data required in the research, namely primary data and
secondary data. Secondary data were collected through literature studies of relevant previous
research from scientific journals, research reports or other information derived from
publications by the Central Bureau of Statistics, Ministry of Agriculture, Ministry of
Industry, Ministry of Trade, research centers and companies in the cane sugar agro-industry.
Primary data were collected in several ways, namely:
1. Field observation, which is a direct observation of management operations and
stakeholder activities involved in the cane sugar agro-industry supply chain. Data collection
through field observations was carried out using purposive sampling method. This means
that the selection of respondents or research locations is based on easy access to data,
information and research locations.
2. Interviews and discussions, to obtain direct information from cane sugar agro-
industry supply chain stakeholders and confirm the results found in the research process.
Data collection methods through interviews were conducted through stratified random
sampling.
3. Expert opinion, is data obtained directly from experts through measuring instruments
in the form of questionnaires or direct interviews. Experts involved in this study consisted of
practitioners and academics. Data collection through expert opinion is done through the
judgment/purposive sampling method. This means that the data obtained is the result of
intuition, experience and expert knowledge. The experts involved in this study were selected
through purposive sampling, namely experts who have knowledge and experience in the
object of research and are willing to provide opinions and assessments on the aspects asked.
Data Type and Source
The data sources used in this study come from primary data and secondary data.
Secondary data were obtained from literature studies, previous research results, scientific
journals, data documentation in research institutions and centers, and data documentation
from cane sugar agroindustry companies.
Primary research data was obtained using the data collection techniques described above,
namely field observations, interviews and discussions as well as expert opinion. More
complete types and sources of data required in this research are shown in Table 13.
Research Location and Schedule
Data collection and interviews were conducted in the cane sugar agro-industry in
East Java Province. East Java Province was chosen as the research location because it is the
province with the most cane sugar production in Indonesia (BPS 2017). Selection of
companies and sugar factories used as objects,
Policies Related to Risk Mitigation and Value-Added Improvement of Cane Sugar
Agro-industry Supply Chain
Mitigating risks and increasing added value in the cane sugar agro-industry supply
chain in Indonesia can be achieved if supported by appropriate regulations and policies. The
cane sugar agro-industry supply chain covers all business activities from farmers to
consumers, in which there are three main problems, namely problems in the farming aspect,
problems on the sugar factory side and problems in sugar trading (Mardianto et al. 2005). So
far, policies for the cane sugar agro-industry have focused on the distribution and trade
(Supriyati et al. 2013). The focus of policies that only pay attention to one part of the cane
sugar agro-industry supply chain will not solve the problem, because the cane sugar agro-
industry business process has strong linkages from upstream to downstream factories that
must be resolved comprehensively (Fahrizal 2015).
Policies related to sugarcane farming began with the issuance of the People's
Sugarcane Intensification (TRI) policy in 1975, in accordance with Presidential Instruction
No. 9 of 1975. This policy was issued to ensure that farmers could cultivate and manage
their own sugarcane land, which was previously leased to sugar factories. The policy also
aimed to increase the productivity and revenue of sugarcane farmers and ensure an increase
in Indonesia's cane sugar production. In reality, this TRI policy did not work well as the
desired sugarcane productivity was not achieved. Hamid (1992) conveyed several causes of
the failure of the TRI program, namely land limitations, farmers' financing and capital, good
cultivation techniques, harvest and post-harvest handling, productivity and farmers'
awareness in supporting the TRI program.
Seventeen years after the TRI policy, the government issued regulations on crop
cultivation through Law No. 12 of 1992. The main objective of this regulation was to
improve the welfare of farmers and to give them the freedom to cultivate commodity types
that farmers thought were more economically profitable. Especially for smallholder
sugarcane farmers, this policy was a turning point of change in that they were no longer
required to grow sugarcane if it was not profitable and could switch to other commodities.
This policy has an impact on the high conversion of sugarcane land to other commodities,
especially farmers who have small land areas. This regulation also poses risks for sugar
factories related to the fulfillment of factory capacity and the certainty of the amount of
sugarcane supply from farmers.
Efforts to increase the productivity and added value of Indonesian cane sugar
received an opportunity in 1997, in accordance with Presidential Instruction No. 5 of 1997
and the revocation of the TRI Presidential Instruction. Through this policy, the government
instructed the development of smallholder sugarcane by optimizing coordination between
smallholder sugarcane farmers, sugar factories and cooperatives and providing credit for
sugarcane production (Yadjid 2011). The policy to support the national cane sugar agro-
industry in 1997 did not last long, because in 1998 the government issued a new policy.
Presidential Instruction No. 5 of 1998 confirms the revocation of Presidential Instruction No.
5 of 1997 and returns to Law No. 12 of 1992 on the freedom of farmers to plant commodities
on their land. The re-application of Law No. 12 of 1992 is certainly a risk to the guaranteed
availability of sugarcane raw materials for sugar factories. Sugar factories must improve
partnership and institutional patterns with farmers so that cane raw materials can maximally
meet factory capacity.
The policy to support added value and minimize the risk of the cane sugar agro-
industry supply chain on the sugar factory side is the revitalization program. Revitalization is
a policy to increase sugar productivity and achieve sugar self-sufficiency. The sugar factory
revitalization program has shown good results in several sugar factories in East Java
(Rusono et al. 2013). In reality, the revitalization program is still running slowly due to the
lack of integrated government policies to support the program (Hariadi 2015).
Christianingrum (2016) has suggested that the revitalization of sugar factories needs to be
well considered as an effort to reform the sugar agro-industry to meet domestic needs and
must also be supported by policies to limit the amount of sugar imports.
Efforts to increase added value and mitigate the risks of the cane sugar agro-industry
are also supported by the sugar self-sufficiency policy. The national sugar self-sufficiency
policy has basically started since 2002 as a response to the increase in domestic sugar
consumption and import schemes that protect domestic entrepreneurs with the issuance of
several related regulations. From some of these regulations, it is known that policies
supporting sugar self-sufficiency are issued by various institutions and agencies that mostly
regulate capital, farmers' cost of goods (HPP), import schemes and tariffs, and land
arrangements (Supriyati et al. 2013).
According to PP 17/1986, the authority to foster and develop the national sugar
industry is under the coordination of the ministry of agriculture, but in 2009 the government
instructed the ministry of industry to create a road-map for the development of the cane
sugar industry. This shows that in an effort to improve the productivity and performance of
the cane sugar agro-industry, state institutions have not been well coordinated to achieve this
goal. In the end, the policy and roadmap to support sugar self-sufficiency after 2002 and
2007 failed to be achieved and did not meet the target. In 2009, the Ministry of Industry
developed a roadmap for the sugar industry to support national sugar self-sufficiency.
Subsequently, the roadmap was revised through industry regulation number 11/M-
D/PER/1/2010.
Several targets for sugar self-sufficiency and increased productivity in accordance
with the national sugar industry roadmap have not been well achieved. Nowadays, these
challenges are becoming more severe due to the ASEAN Economic Community (AEC)
agreement (Susilowati and Rachman 2015). Some of the factors that have not achieved
national sugar self-sufficiency at the farm level are land availability. According to the
roadmap of the national sugar industry contained in the regulation of the minister of industry
number 11/M-IND/PER/1/2010, it includes the determination of land area and the processing
of land use permits that will be facilitated. The implementation of the regulation, namely the
addition of 300 hectares of land, is not well monitored, causing sugar production to remain
constant as in the previous period Previously. Another cause is the implementation of
regulations related to unloading ratoon that has not been well realized, resulting in a
decrease in the quality of sugarcane, the development of less productive varieties and a
decrease in sugarcane productivity per planting unit.
The failure of the sugar self-sufficiency plan each year is not only influenced by on
farm and off farm factors, but also by other factors, such as the lack of guaranteed farmer
income from the aspect of sugar pricing. Sugar pricing policies that are only determined by a
few parties can cause losses to farmers (Rusono et al. 2013). Losses to farmers are caused by
the setting of sugar auction prices that are always above the HPP so that traders earn much
greater profits compared to upstream actors (KPPU 2010). This pricing scheme causes
fluctuations in domestic sugar prices and even tends to continue to increase. So far, the
government has implemented an import scheme by appointing certain importers to maintain
the stability of sugar prices (Sawit 2010).
The limited sugar import policy is carried out by the government to fulfill the
shortage of sugar supply. This policy is stipulated in the Decree of the Minister of Industry
and Trade No. 522/MPP/Kep 9/2004 concerning the provisions on sugar imports, which has
undergone several changes and was last revised into Decree of the Minister of Trade No.
117/M- DAG/PER/12/2015. The limited sugar import policy refers to the forecast of
domestic sugar demand based on the needs of the community and industry as well as
maintaining the stability of domestic sugar prices. Policies related to sugar imports are issued
by the government in the form of import regulations that not only regulate the benchmark
price of sugar but also regulate the amount of sugar supply as well as the terms of import of
GKR and GKM which do not allow them to be traded in the domestic market (KPPU 2010).
In an effort to increase added value and minimize risk to farmers, Supriyati et al. (2013)
suggested setting a benchmark price for farmers and maintaining the stability of domestic
sugar prices. In this regard, the government through the Ministry of Trade of the Republic of
Indonesia set a reference price for purchases at farmers and sales prices at consumers with
the Regulation of the Minister of Trade of the Republic of Indonesia Number 27/M-
DAG/PER/5/2017 which was also preceded by the Regulation of the Minister of Trade of the
Republic of Indonesia Number 08/M-DAG/PER/2/2016.
Based on the results of the study by Mardianto et al (2005); Supriyati et al. (2013)
and Nugroho (2017) and the results of the literature review on regulations related to the
development of policies related to the cane sugar agroindustry from 1971 to 2017 that are
still valid can be seen in Table 14. In Table 14, researchers also provide an assessment of the
positive impact (+) and negative impact (-) of each government policy on the progress of the
cane sugar agroindustry sector.
In the above analysis, it is known that there are several factors that are the key to not
optimizing risk mitigation and increasing added value from a policy point of view, including
the not optimal role of research institutions in efforts to improve the performance of the
sugar agroindustry, capital support from financial institutions that have not been impartial to
improving the welfare of farmers and revitalizing factories, the weak role of cooperatives
and associations of sugarcane farmers in supporting efforts to increase sugarcane production
and the absence of an integrated policy for the sugarcane agroindustry.
Objective Conditions of Indonesia's Sugar Agro-Industry
There are 3 types of sugar circulating in Indonesia, namely raw crystal sugar (GKM)
or commonly called raw sugar, refined crystal sugar (GKR) and white crystal sugar (GKP).
GKM is a type of sugar used as raw material for GKR, processed through defacation and
cannot be directly consumed by humans with an ICUMSA value of 1200 IU (BSN 2008;
Kurniasari et al. 2015). GKR is a raw material for production in the food and beverage
industry with an ICUMSA value of 45-80 IU (BSN 2006; Pujiatsih et al. 2014). GKP is
sugar commonly consumed by the public which is processed from sugar cane plants with an
ICUMSA value of 81-300 IU (BSN 2010). In general, the characteristics of GKM, GKR and
GKP lie in the processing process and differences in ICUMSA (International Commission
for Uniform Methods of Sugar Analysis) values.
The demand for GKR increased by 16% due to an increase in the growth of the food
and beverage industry (KPPU 2010). The circulation of GKR in the market obtained from
the production of 11 refined sugar factories or imports has exceeded the volume needed by
the industry, so that GKR is also sold in the domestic GKP market which can destabilize
prices (Fajrin et al. 2015). The increase in GKR production and demand is also driven by the
more secure availability of GKM through the import scheme. Increased production and
cheaper prices compared to GKP have resulted in GKR products entering the traditional
market structure, which should only be sold to industry (Rusono et al. 2013). The entry of
GKR at lower prices into the traditional market is a result of GKR processing factories
selling more of their products to distributors than selling them directly to the industry (KPPU
2010). The distribution pattern of GKR should be simpler and should not be sold directly to
the public because it can cause losses to GKP entrepreneurs and sugarcane farmers.
According to KPPU (2010), the distribution pattern of GKR should be seen in Figure 11.
CONCLUSIONS :
Stakeholders involved in the cane sugar agro-industry supply chain are smallholder
cane farmers, sugar factories and distributors. In addition, cooperatives and marketing
management are secondary stakeholders that support the smooth running of the cane sugar
agro-industry business process. This research has successfully formulated a framework of
supply chain performance measurement model, value-added analysis model and supply
chain risk assessment model. The measurement results show that the farmers' supply chain
performance is moderate, while the sugar factory's supply chain performance is poor. The
analysis and measurement results show that the cane sugar agro-industry supply chain is
oriented towards the goal of increasing effectiveness consisting of reliability, responsiveness
and agility attributes.
The analysis shows that sugarcane farmers face more risks than other stakeholders,
while the added value obtained by farmers is lower. Risk mitigation and value-added
formulation are efforts to improve the performance of the cane sugar agro-industry supply
chain. The highest value-added ratio in sugarcane farmers was obtained during the first cane
ratoon cultivation, which was 23%, while the value-added ratio of the sugarcane sugar
factory was 40.75%. The acquisition of added value in smallholder sugarcane farmers and
sugar factories is very likely to be improved.
The identification and assessment of supply chain risks show that there are 15 main
sources of risk in farmers which include climatic factors, production inputs and low
productivity, 7 main sources of risk in sugar factories which include efficiency and condition
of factory machinery and 4 main sources of risk in distributors which include regulations,
sugar marketing and production planning. All of the main risks that have been identified in
smallholder sugarcane farmers, sugar factories, and distributors can affect the performance
of the supply chain so that it requires appropriate mitigation efforts. For each source and
occurrence of risk faced by cane sugar agro-industry supply chain stakeholders, mitigation
activities have been developed aimed at minimizing the impact of supply chain risks.
