• Editorial Board +
• For Contributors +
• Journal Search +
Journal Search Engine
ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.20 No.1 pp.9-26
DOI : https://doi.org/10.7232/iems.2021.20.1.9

Risk Management for Improving Supply Chain Performance of Sugarcane Agroindustry

Industrial Engineering Department, BINUS Graduate Program – Master of Industrial Engineering, Bina Nusantara University, Jakarta, Indonesia, 11480
Department of Agro-industrial Technology, Bogor Agricultural University, Indonesia
Faculty of Agriculture, University of the Ryukyus, Japan
May 1, 2019 March 14, 2020 January 5, 2021

ABSTRACT

The purposes of this paper are to evaluate sugarcane supply chain performance, identify supply chain risks, and define mitigation to improve the performance. Supply chains were modeled through a Supply Chain Operation Reference (SCOR) and Fuzzy-Analytic Hierarchy Process (AHP) to evaluate the performance. This paper modeled fuzzy-House of Risk (HOR) to identify and assess supply chain risk. Expert judgment for supply chain risk assessment was aggregated using an Ordered Weighted Average (OWA) model. The supply chain performance of farmers was found to be average, while that of sugar mill was at below average. Our model succeeded in identifying and assessing 15 risk agents for farmer, seven risk agents for sugar mills, and four risk agents for distributors which had to be mitigated. This work offers meaningful guidance for decision-makers to compose strategies to mitigate risk and enhance performance of sugarcane agroindustry supply chains.

1. INTRODUCTION

Sugarcane agroindustry is a business which transforms sugarcane into sugar for consumption or B2B needs (in the food and beverage industries). Sugarcane agroindustry carries the risk of uncertainty due to climate, variability, which affects production, harvesting, transportation, milling and marketing (Nicol et al., 2007). Sugarcane supply chain stakeholders must deal with downstream risk, such as excess supply in global sugar production which can lower sugar prices and increase import volumes in a country. Therefore, both upstream and downstream risk must be minimized in the supply chain by implementing risk management.

The business process in sugarcane agroindustry demonstrates strong interconnectivity between the upstream and downstream processing. Thus, any improvement must be carried out comprehensively to improve supply chain performance and risk management. Performance measurement is essential enabling and control of strategies, and regulating the coordination in order to fulfill consumer demands on the supply chain (Chopra and Meindl, 2013).

Supply chain risk management aims to identify and measure the risks, as well as determine strategic imperatives to vulnerability to risks (Neiger et al., 2009). Minimizing risks in the supply chain is crucial, as it impact the entire business process efficiency (Asrol et al., 2018). Researcher like Mishra et al. (2016) have stated that the right risk mitigation strategies can improve the supply chain performance. However, studies on this topic in this field are rather scarce. Therefore, this paper offers a novelty in identifying risks on each sugarcane supply chain’ stakeholder to defines mitigation activities in improving the performance.

This work contributes to the literature a novelty in designing models for supply chain performance evaluation as well as supply chain risk identification and assessment. Additionally, this work also formulates and recommends mitigations activities to minimize risk and improve supply chain performance simultaneously. As far of the authors knowledge, there were not any paper to discuss the risk assessment and mitigation to improve the supply chain performance directly. To begin, we identify supply chain configuration as a critical step to improve performance and formulate strategies (Oliva, 2016). To conclude, we formulate mitigation activities with preventive actions simultaneously improve sugarcane supply chain performance risks.

This research aims to (1) identify the supply chain mechanisms of the sugarcane agroindustry, (2) evaluate the sugarcane supply chain’s performance, and (3) develop a supply chain risk assessment model and define mitigation activities that improve the performance.

2. LITERATURE REVIEW

2.1 Supply Chain Management and Performance

Ballou (2007) defined supply chain management (SCM) as the extension of logistics management with the planning and management of all activities required to transform a raw material into a value-added product. All activities in SCM involve many stakeholders which have specific roles, therefore Asrol et al. (2018) mention that coordination and collaboration among stakeholders is essential. The coordination and collaboration among stakeholders in SCM are required to ensure the availability of raw materials, information, and cash flow in all portions of the supply chain to fulfill consumer demand.

The need for coordination in a supply chain has explained by Chopra and Meindl (2013), who state SCM is not only performed by manufacturers and suppliers but also retailers, distributors, transporters customers and all of which constitute additional stakeholders. All the activities of these stakeholders should be mutually supporting and coordinated with one another to fulfill consumer demands (Mentzer et al., 2001). Stakeholder coordination and the continuity of supply chain flow may improve the supply chain’s competitive advantage. Further, the main goals of the supply chain are to develop an effective relationship with the consumer (Mentzer et al., 2001) maximize profit (Chauhan and Proth, 2005;Yao et al., 2008), provide ‘value’ for the consumer (Kohli and Jensen, 2010), ensure the availability of raw materials and guarantee cash and information flow (Nunes et al., 2020).

Maintaining the supply chain’s competitive advantage required many aspects to be assessed and improved. On other hand, supply chain performance measurement is an operational and strategic ways to capture and improve the supply chains positions relative to competitors. Many researchers have contributed to the supply chain performance measurement literature to enhance competitive advantage, in the agri-food supply chain (van der Vorst, 2006), measuring supply chain performance using risk dimensions (Wagner and Bode, 2008), measuring soybean value added and supply chain performance using Data Evolvement Analysis (DEA) (Feifi et al., 2010), develop performance measurement framework using Soft System Methodology (Liu et al., 2012). Other have worked on measuring performance using multi-criteria decision making (Öztayşi and Sürer, 2014), developing conceptual frameworks to measure sustainable performance in the automotive and electrical industries (Schöggl et al., 2016), improving supply chain performance qualitatively using Structural Equation Modeling and risk mitigation (Mishra et al., 2016), and measuring palm-oil supply chain performance (Marimin et al., 2020).

Balfaqih et al. (2016) mentioned that there were a number of methods and techniques to measure supply chain performance, including Analytical Hierarchy Process (AHP), ANP, simulation, survey, and DEA. To improve the analysis, this research utilizes a combination of Fuzzy – AHP and SCOR (Supply Chain Operation References) to determine the supply chain performance of the sugarcane agroindustry. Fuzzy AHP is an advanced form of conventional AHP used to determine the level of importance of each element in a hierarchy with expert assessment. SCOR is a business process approach to measure the supply chain performance that is proposed by SCC (2012). The SCOR framework measures supply chain performance using metrics which are decomposed from reliability, responsiveness, agility, cost, and asset management attributes.

2.2 Supply Chain Risk Management

In supply chains, risk management is complex since a risk in specific point is possible to affect all business process activities (Chapman et al., 2002). Asbjørnslett (2009) summarized the main steps in risk management, including identification, assessment, monitoring and mitigation along with risk learning. In a supply chain, risk management is even more complex since it involves activities at many levels and multiple stakeholders. Therefore, Oliva (2016) has suggested that we pay more atten tion to risk identification to capturing all potential risks and mitigate at the final risk management steps.

All stages of risk management require specific tools to assist in managing the risk. The main goal of risk management in supply chains is identify and minimize the potential risks along the supply chain through a coordinated approach among stakeholders. Risk management is not only about minimizing risk but also designing mitigation strategies, minimizing cost, minimizing an organization or company’s vulnerability efficiently, and repairing the supply chain’s condition.

Related research has offered insight into risk management to improve supply chains. Some of these include the sustainable assessment of supply chains (Winter et al., 2014), supply chain disruptions using information sharing (Wakolbinger and Cruz, 2011), risk management in the dairy industry (Septiani et al., 2016), and in agribusiness and agri-food risk management (Behzadi et al., 2018; Cafiero, 2008;Sriwana et al., 2015), and supply chain risk management to improve performance and company integration (Munir et al., 2020).

Obviously, risk management offers many possible areas of improvement for the supply chain. This research therefore proposes a risk management framework to improve the performance of the supply chain. The idea is based on empirical work showing that risk management may improve the performance and competitive advantage of a supply chain (Vanany et al., 2009). Further, this research offers a comprehensive supply chain performance measurement methodology and a way to improve performance using risk management and mitigation.

2.3 The Sugarcane Agroindustry

Agroindustry is business process that transform farm product as raw materials into high value-added product through physical and chemical processing. The agroindustry process sugarcane through specific processing steps for use as sugar product for consumption. Sugar production comprises several steps, including sugarcane harvesting and transportation, sugarcane cleaning, juice extraction, juice clarification and treatment, evaporation, crystallization, centrifugation, sugar separation and sugar packaging (Chauhan et al., 2011).

Attempting to improve the supply chain performance of sugarcane agroindustry poses complex problems. Van der Vorst and Snels (2014) mentioned that the supply chain in agroindustry offers more complex problems due to the raw materials characteristics, climate and seasonal challenges and maintaining product quality. There are at least three main obstacles in sugarcane supply chain, it involves uncertain conditions and availability of raw material, it is related to chemical and biological transformations, and the business process comprises many actors and sectors, along with related activities by many stakeholders who face different risks and have different goals (Chiadamrong and Kawtummachai, 2008).

According to Food and Agriculture Organization (FAO), (2019), Indonesia is one of the largest sugar importers in the world. Moreover, sugar production in Indonesia is decreasing annually, due to many factors, including risk management and supply chain efficiency (Everingham et al., 2002;Kadwa, 2013;Nababan, 2013;Pujiatsih et al., 2014). Therefore, this research was conducted in Indonesia, especially in the central sugar production region of East Java (BPS, 2018), to improve supply chain performance and minimize.

3. METHODOLOGY

3.1 Data Collection and Research Flow

The data were collected through (1) a literature study, (2) field observation of sugarcane supply chain stakeholders in East Java, Indonesia, (3) data history acquisition about supply chain performance, and (4) in-depth interviews with stakeholder along with expert judgement. The first part of our research was conducted through a literature study and field observation in the sugarcane agroindustry in Indonesia. This stage allow us to formulate a list of the common supply chain performance attributes and potential risks in the sugarcane agroindustry. The performance attributes and potential risks were validated by the expert group.

In the field observation, we interviewed at least 30 farmers and observed two sugar mills. We determined which farmer to interview through stratified random sampling and represented at all farmers production scales. The sugar mills to be interviewed and observed were determined by purposive sampling and had almost similar business characteristics. These considerations are required to strengthening the analysis in providing the recommendation.

For the interview, the respondents of the research are supply chain stakeholders and the experts. For the stakeholders, the questions lists were related to supply chain performance metrics, the components of risk agent and event in business activities, and the idea of potential preventive action to be done in mitigating the risk. The list of questions related to the supply chain performance are the metric in SCOR framework mentioned in the bottom part of the Figure 5.

Expert are determined through purposive sampling who understand the problem and object, has an ability to share the opinion and assessment, has experience in the risk management and sugar agroindustry’s supply chain also professional. Expert also validate the model and the result with respect to their knowledge and experiences. This research involves 5 experts which the detail of ex pertise is delivered at Table 1.

Supply chain performance was evaluated for each stakeholder using Fuzzy AHP and SCOR. Supply chain risks were identified and assessed for each stakeholder using fuzzy-HoR model. House of Risk (HoR) is a risk assessment framework which was introduced by Pujawan and Geraldin (2009). Therefore, The Fuzzy House of Risk is a novel research method which has not been applied in any extant research yet, as far as the authors knowledge. Ultimately, risk mitigation activities were arranged to minimize risk and improve performance. The research flow is depicted in Figure 1. Similarly, detailed explanations of each research stages are delivered below.

3.2 Data Analysis and Model Development

3.2.1 Identification of The sugarcane Supply Chain

The supply chain of sugarcane agroindustry was identified descriptively and quantitatively based on van der Vorst (2006) conceptualization. This approach described supply chain configuration according to structure, business process, management, and resources in sugarcane agroindustry. The supply chain configuration and mechanisms are displayed descriptively and graphically. This stage also describes each supply chain stakeholder and the detail of each business activity.

3.2.2 Supply Chain Performance Measurement Model

SCOR and Fuzzy-AHP approaches were modeled to measuring supply chain performance. There were 15 metrics of supply chain performance defined and adopted from the SCOR framework which were derived from the overarching concerns of reliability, responsiveness, agility, cost, and management assets. Supply chain goals were established from supply chain effectiveness and internal efficiency as discussed by Chopra and Meindl (2013) and Reddy et al. (2019), and were also accommodated at the AHP hierarchy model to measure supply chain performance.

As suggested by Palma-Mendoza et al. (2014), the supply chain has a different importance level in performance metrics, so the weighting was completed by synthesizing the inputs (judgments) from experts using the Fuzzy-AHP technique as formulated and defined by Marimin et al. (2013) and Asrol et al. (2017). Describing the supply chain stakeholder’s performance, requires metrics depicting actual and benchmark value.

The actual value for stakeholders metrics performance are reported based on field observation and direct interviews with respondents. The benchmark value for each matric is adopted from the best stakeholders performance interviews or from expert opinions. The benchmark value is required to calculate the performance gap for stakeholder (SCC, 2012), which is described as the Score value. Furthermore, each supply chain performance metric value was benchmarked to the best performance value in its area, or the agroindustry’s target and stated in percentile form. Mathematically, deriving supply chain performance for a stakeholder using SCOR metrics and AHP is described by Equation 1.

(1)

Suppose that X is a supply chain stakeholder and i is a supply chain metric from SCOR. n represents the number of supply chain performance metrics, of which this paper forwards 15. Ai represents the actual value of metric i and Bi represents the benchmark value of metric i. Wi is weight of metric i which is found through the fuzzy-AHP technique. Finally, the total value of supply chain performance of stakeholder X (PX) is derived.

3.2.3 Supply Chain Risk Identification and Assessment Model

This step consisted of three stages: risk event and risk agent identification, risk assessment modeling, and an Aggregate Risk Potential (ARP) value determination model. The risk identification stage referred to the SCOR framework to list risks along the supply chain. This stage was applied to evaluate the level of risk severity (S), the level of risk occurrence (O) and the correlation (R) of risk events and agents. The evaluation scale was established using a fuzzy scaling model as displayed in Figure 2 and 3.

To assess supply chain risk, the judgement of five experts was sythesized. The aggregation of expert judgment was performed using an Ordered Weighted Average (OWA) model (Yager, 1993) as expressed Equations 2 and 3. Suppose that P is an expert judgment aggregation value which is determined by the assessment weight (Qj) and the order of greatest expert assessment for the criterion (bj). The number of rating scales is represented by q and r represents the number of experts whose judgments are aggregated.

$P = M A X [ Q j ∧ b j ]$
(2)

$Q j = I n t [ 1 + j × q − 1 r ]$
(3)

The ARP was determined according to the HoR framework proposed by Pujawan and Geraldin (2009) as stated in Equation 4. This paper developed a Fuzzy-HoR model which used the fuzzy operation to determine ARP as defined below. Suppose that $X ( x 1 , x 2 , x 3 )$ and $Y ( y 1 , y 2 , y 3 )$ represent the fuzzy sets and α represents the degree of confidence as described at Equation 58. Further, defuzzification of $A R P J ˜$ was obtained from the median range (Yue and Jia, 2013).

$A R P J ˜ = O ˜ J ∑ I S ˜ i R ˜ i j$
(4)

$X a = [ ( x 2 − x 1 ) α + x 1 , − ( x 3 − x 2 ) α + x 3 ] = [ c 1 , c 2 ]$
(5)

$Y a = [ ( y 2 − y 1 ) α + y 1 , − ( y 3 − y 2 ) α + y 3 ] = [ d 1 , d 2 ]$
(6)

$X × Y = [ c 1 , c 2 ] × [ d 1 , d 2 ] = [ c 1 × d 1 , c 2 × d 2 ]$
(7)

$X + Y = [ c 1 , c 2 ] + [ d 1 , d 2 ] = [ c 1 + d 1 , c 2 + d 2 ]$
(8)

In this research, risk mitigation was formulated by providing an ARP threshold (1,000 for sugarcane farmers, and mills and 500 for distributors) and was assumed to represent the dominant risks in supply chain. Risk mitigation activities were formulated through discussion and sytheshis of each risk priority, which had been defined based on the ARP threshold. The formulated mitigation activities were adjusted for each stakeholder and simultanously to improve performance.

4. RESULTS AND DISCUSSION

4.1 Supply Chain Configuration and Structure of Sugarcane Agroindustry

The identification stage constitutes an initial and principal step in supply chain analysis allowing the researcher to understand problems and formulate improvement strategy (Muchfirodin et al., 2015). In this paper, we examine the sugarcane agroindustry, which produce white sugar for B2B or household consumption in East Java Province, Indonesia. The supply chain stakeholders are divided into primary and secondary member. Primary members are those directly involved in the business process, while secondary support and ensure the supply chain speed and coordination. The results demonstrate that the primary members of the supply chain are sugarcane farmers, sugar mills and distributors. Secondary members include credit unions and the company marketing offices.

The primary stakeholders are responsible for the business process which includes materials, cash, information and the flow of coordination. Sugarcane farmers are involved in farming raw materials, sugar mills produce sugar from those raw materials and distributors distribute sugar to consumers. Credit union help farmers seeking capital while also collaborating with mills, whereas marketing offices manages coordination in sugar bidding and selling. The supply chain configuration of sugarcane is shown in Figure 4

4.2 Supply Chain Performance Analysis

Supply chain performance measurement holds an important role in achieving sugarcane supply chain efficiency (Asrol et al., 2017). Firstly, supply chain performance metrics have to be identified, since it performance must be measurable just as in business process evaluation (Bittencourt and Rabelo, 2008;Reddy et al., 2019). we implemented a weighting process to adjust for the level of importance of each metric in the sugarcane supply chain. The definitions for each supply chain metric for sugarcane farmers and mills are described in Table 2. The hierarchy and weighting results for supply chain performance metrics by the experts according to fuzzy-AHP is shown in Figure 5.

Fuzzy AHP assessment by experts for the supply chain metrics shows that three main performance metrics, i.e. the cost of goods sold, procurement cycle time, and yield, were considered to be the most important factors. Experts agree that the sugarcane supply chain had to pay more attention to effectiveness (0.588) than internal efficiency goals (0.412). Experts evaluation show a consistency ratio of 0.061, which meant their evaluations and assessments are relatively consistent.

The supply chain metrics and weights in Figure 5 were required to measure sugarcane farmers and mill according to stakeholder performance. Our results showed that farmer performance reached 80.55%, while mill performance was 77.47%. This supply chain performance was categorized as ‘average’ and ‘below average’ respectively, which values adopted from Monczka et al. (2009).

Sugarcane farmer metrics and performance are shown in Table 3. There are only 14 metrics measured at the farmer level, since upside supply chain flexibility metrics cannot be identified. these metrics cannot be identified due to supply chain system’s pull on agriculture (on farm) (Van der Vorst et al., 2007). Furthermore, metric weight as the result of Fuzzy AHP are normalized and the total weight equal to 1.

Table 3 shows actual value, benchmark value, and score value of farmer performance. The score value is the result of the actual and benchmark values of the sugarcane farmers which is stated in percents. Thus, overall performance results from score and metric weights for farmers. Finally, the total sugarcane supply chain’s performance for farmers is reported.

The score value clearly report which metrics should be improved in supply chain performance. Metrics with a 100% score demonstrate good performance that should be maintained. Therefore, the performance areas that should be improved for the sugarcane farmers involve procurement cycle time, delivery cycle time, value-at-risk, profit, and day(s) of inventory chain. These metrics show high gap between the actual and benchmark value.

Improved farmer performance could be achieved by enhancing the days of inventory, value-at-risk and profit performance which had the lowest performance values. The value-at-risk of the farmers was 60.00%, while profit performance and days of inventory were 51.72% and 50.00%, respectively. Value-at-risk refers to the level of the general plantation risk addressed by the farmers, while profit means the value-added and benefits gained. The farmer’ days of inventory refers to the delay period from harvesting to transporting sugarcane to the mill.

Table 4 reports the mills’ performance in the sugarcane supply chain. Actual value is identified at the mills based on field observation, discussion, and interviews with stakeholders, along with secondary data from related documents. The benchmark value is found using the best mills’ performance in the sugarcane supply chain and further elaborated with the experts and stakeholders. Finally, overall mills performance was found to be 77.47% (below average).

Some metrics that should be considered to maintain mill performance. Low metrics performance values are shown where the score values do not meet 100%. Overall, our analysis shows that there is no single metric at the mill which achievea the highest value due to low mill efficiency and old production machinary (Dewayana et al., 2010;Subiyakto, 2016). In this case, the analysis is focused on metrics where the score value is under 70%, including upside capacity and quality adaptability, valueat- risk, cash-to-cash cycle time and delivery cycle time.

The analysis found that the value-at-risk metric was the area of lowest performance for the sugar mills. Valueat- risk for the mills was measured based on the percentage of mill downtime due to machine failures or a lack of raw materials. Based on field observations, the mill downtime percentage of sugar mill was up to 5%, which may create profit loss and disturb business, while the benchmarking value was 2.48%. Therefore, the valueat- risk performance based on real downtime percentage and benchmarking value is only 49.60%.Upside capacity and quality adaptability and delivery cycle time also demonstrated low performance. These metrics represents the maximum percentile increases in the amount of goodquality products delivered continuously and performed at only 55.14%. In fact, mills must take care to ensure product quality, since it affects consumer satisfaction and therefore profits (Ushada and Murase, 2009).

The delivery cycle time at the sugar mill means the period required to deliver products to consumers. Sugar mill performance for delivery cycle time is 66.67% which is influenced by the significant required time for the loading and documentation process.

Asrol et al. (2017) measured mill performance in which the raw materials were supplied from mill-owned plantation, observing a result of 63.05% (very low). In this work, the performance of sugar mills in which raw materials were obtained from smallholder farmers reached 77.47% (below average).

As was mentioned before, the value-at-risk metric for sugarcane farmers and sugar mill had the lowest performance. For farmers, value-at-risk is measured by the probability of sugarcane harvesting failure, while for mills it is based on the probability of processing downtime. These results indicated that the value-at-risk performance in the sugarcane agroindustry supply chain has an important role in maintain the entire supply chain’s performance. In line with this hypothesis, scholars also reported and found that risk management and mitigation provide a better supply chain performance and competitive advantage (Maheshwari and Jain, 2014;Munir et al., 2020;Ritchie and Brindley, 2007;Ryding and Sahlin, 2013).

Therefore, it is important to formulate risk management strategies to improve the supply chain performance. As stated before, risk management steps involve identification, assessment, and mitigation. Risk management to improve sugarcane supply chain performance is explored in the next section.

4.3 Supply Chain Risk Identification and Assessment using Fuzzy – HoR Model

In this study, sugarcane supply chain risk was identified according to risk events and risk agents which potentially affect supply chain coordination. The risk agents and events for each supply chain stakeholders are identified based on a literature study, field observation, and in-depth interviews with stakeholders. A risk event is defined as an uncertain and unexpected events causing an undesired damage or effect (Manuj et al., 2008).

In this research, risk events are identified based on supply chain activities and the SCOR framework. Supply chain activities for each stakeholder are decomposed from the SCOR business process, namely Plan, Source, Make, Deliver, Return and Enable. For each SCOR business process, supply chain activities are defined and related risk event are found that potentially disrupt supply chains. This stage allowed to determine the number of risk events for farmers (29), mills (38) and distributors (17). To ensure all activities in the suply chain are identified, the risks related to activities were developed. The framework of risk identification based on SCOR and the number of risk in each activities is defined at the Figure 6.

Risk agents constitute factors that account for generating risk events, resulting from either the internal or external environment (Giannakis and Papadopoulos, 2016). It waas found that there were 35 risk agents for farmers, 21 risk agents for mills and six risk agents for distributors. For an illustration, the description of the HoR matrix for risk identification and assessment for sugarcane farmers is presented in Figure 7. The assessment value based on the fuzzy scale model then synthesized and aggregated the judgment of five experts through an OWA.

The correlation of risk events and risk agents is described in the HoR matrix, which is displayed as nocorrelation, low correlation (LC), high correlation (HC) and very high correlation (VC). Risk correlation means that a risk agent may affect the occurrence and severity of risk events and affect the supply chain effectiveness and efficiency.

As an illustration of risk correlation for sugarcane farmers, the risk agent of unclear information on sugarcane prices (FA1) showed a very high correlation (VC) to the low business revenue than high business capital (FPE2), and displayed a high correlation (HC) to the fluctuation of sugarcane prices (FPE3) and a high correlation (HC) to the errors in fertilization time and irrigation (FPE5). Nevertheless, FA1 demonstrated a low correlation (LC) to the sugarcane yield lost during transportation and queued (FDE25).

Risk assessment for sugar mills showed 12 risk agents with Low (L) occurrence and 9 risk agents with Moderate (M) occurrence. Sugar mill risk agent in low trust from the farmers (MA3) show a high correlation (HC) to the occurrence of high mill downtime (IPE7) and the occurrence of idle mill condition by unavailability of raw materials (IME17). Additionally, six risk agents for distributor stakeholders had a Low (L) occurrence, four had Moderate (M) occurrence, and one had high (H) occurrence. The high occurrence distributor risk agent is attributed to the low selling price due to overproduction during milling time.

All risk events, agents and correlations for farmer, mills, and distributor in the sugarcane supply chain which were identified and found in the analysis were validated by the expert group. The validation of the model using the face validity technique following Sargent (2013). Therefore, the risk assesment and analysis may be valid according to expert assessment and the risk mitigation that was proposed is considered appropriate into the real world cases.

Furthermore, HoR found ARP value for risk agents that should be considered for risk mitigation. The principal idea of HoR is to mitigate the risk agent with the highest ARP value (Pujawan and Geranldin, 2009). The ARP value of each risk agent to be mitigated for sugarcane farmers has been described in Figure 7. The ARP value for sugar mills and distributors also determine in the same way, therefore the final result of ARP value of risk agent for sugar mills and distributors using the HoR framework is described at the Table 5.

The main goal of the fuzzy-HoR matrix is determine ARP value in generating supply chain risk priorities for each stakeholder. In this case, the threshold of ARP values for farmers and mills are set to 1,000 and ARP values for distributor are set to 500. Determining the threshold value for the ARP is crucial in focusing on risk mitigation so as to minimize the most important problems. Risk mitigation may also include high costs that a threshold can minimize the cost and right on target. Our analysis found that there are 15 risk agents for farmers, seven risk agents for mills and four risk agents for distributors, which should be prioritized for mitigation as presented in Table 5.

Farmers in agroindustry supply chains always bear high risk and a lower bargaining position as as been argued by Suharjito and Marimin (2012) and Starks and Bukenya (2008). This research also conveyed that the farmers deal with the highest risks, considering the number of risks that need to be mitigated. The preventive actions to mitigate the risk in the farmers, sugar mill and distributor have been validated by the related stakeholders and experts.

The main risk for sugarcane farmers was climate anomalies, due to their great potential to affect harvest failures, cause declining yields, and impact profit due to the absence of mitigation efforts (Mustadjab et al., 2012). Mitigations effort are needed for improve performance also increase profit performance. Sugarcane farmer face risks from seedling, which are vulnerable to pests along with low yields, which affect profits and upside capacity and quality adaptability performance.

Based on Subiyakto (2016), the vulnerability of sugarcane to pests decreased productivity by up to 20% and has also remarkably affect quality and profitability. Furthermore, the lowered quality of sugarcane demonstrates a significant impact on harvesting time, lower prices and profits (Taira et al., 2013).

At sugar mills, the prioritized risks to be mitigated were the risks those of process efficiency and production machines (MA14, MA20, MA18, MA19). This results corresponded to the results of Subiyanto (2014), demonstrating that process efficiency and production machine performance in the mills were at low levels and greatly affect cycle time and profits and further harming supply chain performance and competitive advantage.

Sugar mills also face a high impact from the external risk of low trust from farmers (MA3) and discontinuity of raw material supply (MA13). These external risks affect the profit and value-at-risk performance due to bad management of raw materials and relationship with farmer’s relation and partners.

Risks for distributors that need to be mitigated included aspects of regulation and refined sugar flooding the market. In Indonesia, refined sugar is regulated to be only for industry use due to its standard quality and its lower price than white crystal sugar. Refined sugar might create price havoc in the white crystal sugar market and profit loss for white crystal sugar distributor (El Fajrin et al., 2015). Distributor also have to pay attention to human resources in managing their business. Skilled human resources in distributor can help to manage risk related to product continuity, price, excess supply also a lack of regulatory support.

4.4 Risk Mitigation to Improve Performance and Minimize Risks to the Supply Chain

Risk mitigation is the final step in supply chain risk management which aims to minimize the impact of risk agents on all supply chain stakeholders and improve performance. Recall that in Table 3 that the low performance of sugarcane farmers mainly related to days of inventory, value-at-risk and profit. To improve days of inventory performance, mitigation could be carried out through collaborative work with the mills and implementation of Good Agricultural Practices (GAP) (FA14) and invest on and rent semi-automatic cultivating and harvesting equipment (FA28). Enhanced profits could be achieved through removing ratoons over the third level (FA4; FA22) since they might decrease sugar yield (Fahrizal et al., 2014;Higgins and Muchow, 2003). Moreover, the value-at-risk performance problem was able to be improved by implementing all preventive actions for sugarcane farmer, as mentioned at Table 6.

The sugar mill had a poor performance in value-at-risk, cash-to-cash cycle time and upside capacity and quality adaptability. To improve the cash-to-cash cycle time performance, some preventive actions were proposed, including the improvement of partnership programs with farmers and the creation of proper Corporate Social Responsibilities (CSR) (MA3) along with the preparation of deals on the early stage of milling time and the improvement of supervision by the mill’s employees at plantation (MA13). These preventive actions may improve cash-to-cash cycle time, enhance farmer trust and mitigate raw material supply risk. The enhancement of upside capacity and quality adaptability performance could be made through arranging periodic mill revitalization and continuous maintenance (MA18), strengthening the implementation of proper SOP and providing communication devices for each sector coordinator (MA10). Generally, all preventive actions mentioned in Table 6 are capable of improving the value-atrisk performance of the mill and also minimize risk along supply chains.

A failure to achieve supply chain goals is caused by supply chain stakeholders being unable to control internal and external risks in their own business (Astuti et al., 2013). Therefore, there is a need to involve all supply chain stakeholders in constructing collaborative project to mitigate risks and achieve supply chain goals (Li et al., 2015). The formulated methods in this paper are easiliy applicable in the sugarcane supply chain stakeholder considering to maintain efficiency and effectiveness.

The mitigation activities and preventive actions proposed in this paper result from performance measurement and risk assessment. The strategies for mitigation as aforementioned have considered risk agents, risk events and risk correlations for farmer, sugar mills and distributors. All mitigation strategies and preventive actions are correlated, and they are mutually enforcing and can behave synergistically to improve performance and minimize risk.

4.5 Research limitations and Future Research

This study was able to analyze supply chain performance, assess risk and synthesize the correlation of risk management to improve performance. For the model to be generalized, the performance measurement, risk assessment, and mitigation models proposed in this paper would need to be implemented generally. These models have been validated by related experts and stakeholders and aimed at implementation in the real world. Moreover, risk events, risk agents and the value of each performance metric and other indicators require identification and assessment to adjust to specific cases and locations.

However, this research did not address supply chain value-added, which also affects supply chain performance. Supply chain value-added should be analyzed for all supply chain stakeholders to understand the distribution of profit and prevent unsustainability in the supply chain. Value added information also important to formulate a detail mitigation strategy to increase supply chain profit.

4.6 Theoretical Contributions and Managerial Implications

The contributions of this paper are in developing a framework to analyze, improve, and mitigate supply chian performance and supply chain risk. While, there are a number of supply chain analyses, this paper proposes a framework to mitigate the risk which is linked to supply chain performance results and which is developed through the global standard for supply chain design, SCOR framework. This paper also proposes a new approach and method to risk analysis and mitigation. Fuzzy HoR is a new method to assess risk alongside expert assessment and field observation. Using a fuzzy approach in the HoR framework makes is a novelty in assessing risk in agro-industrial SCM. A fuzzy approach is required for risk management in agro-industrial supply chains that face significant uncertainty issues.

This paper successfully measures sugarcane supply chain performance, assesses the risk along the supply chain and provides mitigation recommendation to improve supply chain performance. The main problem of the sugarcane agroindustry supply chain is coordination among stakeholders to improve competitive advantage. As is mentioned in much of the research literature, a problem for one stakeholders in supply chain may affects another stakeholder and the entire supply chain, also it is found at the sugarcane supply chain. This paper has described many performance and risk factors that should be taken into consideration by practitionairs. Furthermore, stakeholders need to collaborate with each other to solve these problems.

Analysis has shown that, for farmers, the main performance problems were value-at-risk, cash-to-cash cycle time, and upside capacity and quality adaptability, while the main risks were climate problems and product loss. To manage risk and improve the performance, farmers should enhance cooperation with research centers to improve seeds adaptability to the climate. Additionally, in harvesting activities, farmers should use efficient cultivation processing and transportation alongside mechanical harvesting machines.

At the sugar mill, the main performance problems were value-at-risk, cash-to-cash cycle time and upside capacity and quality adaptability. Risk occurred primarily in process efficiency and production machines. The managerial implications are performing regularly maintenance and control and enhancing effective partnership with the farmers for providing raw materials. Furthermore, sugar mill may provide rewards and punishments for business partner according to their performance in delivering raw materials.

Efforts to improve the performance should involve primary and secondary stakeholders in joint collaboration to minimize risk. Farmers, sugar mills, and distributors have to perform evaluations regularly to mitigate negative impacts on supply chains. They should arrange regular programs to monitor performance and risk. In this phase, secondary stakeholders also have a role in assisting primary stakeholders in supply chain activities aimed at minimizing the risk.

5. CONCLUSIONS AND RECOMMENDATIONS

The sugarcane supply chain involvs sugarcane farmers, sugar mills, and distributors. Moreover, credit unions and marketing management exist to support the business process of the supply chain. All stakeholders in the supply chain mechanism were involved in material, cash, and information flow and engaged in coordination. This research successfully measured supply chain performance, risk management and risk mitigation to improve overall performance. Our results showed that the farmer performance was at an average level, while mills performed below average.

This paper succeeded in developing a new approach to identifying supply chain risk, which is called as Fuzzy- HoR framework. The model enables assessment of risk through expert assessment and field observation, which also accommodates the uncertainty accommodated in a fuzzy model. Risk identification showed all known, possible risks that occur for all supply chain stakeholders. The priority of risk mitigation was determined by risk event severities, risk agent occurrences, and the correlation of risk agent and risk event event. Risk correlation was defined as the possibility of risk agent trigerring the risk event. For all supply chain stakeholders, there was correlation between their own risk agents and events, which were capable of altering supply chain performance. Supply chain risk assessment revealed that there were 15 main risk agents at the farmer’s level, i.e., climate factors, low production and productivity inputs, whereas seven main risk agents were found at the mill level, covering the efficiency and condition of plant machinery. Meanwhile, four main risk agents were found at the distributor level, including regulation, sugar production and marketing plans. The overall key risks studied could influence supply chain performance; thus, they need to be mitigated immediately. For each risk agent and event faced by sugarcane agroindustry supply chain stakeholders, a mitigation activity was developed to minimize the impact of supply chain risk.

Furthermore, the results showed that the upstream of the supply chain had to bear major risks; on the other hand, the downstream face with minor risks. Besides risk mitigation, supply chain performance also requires a value-added analysis in order to improve sugarcane agroindustry supply chain efficiency. Further research may seek a better model involving a balanced risk and value-added distribution model, aimed at achieving a fair and sustainable supply chain.

Muhammad Asrol received his PhD and Master degrees in Agro-industrial Technology from IPB University, supervised by Prof. Marimin. His research interests include supply chain management, decision support system, artificial intelligence, and business process modeling.

Marimin is Professor in System Engineering at IPB University (Bogor Agricultural University)– Bogor, Indonesia. His research interests are soft system methodology, intelligent and fuzzy expert systems, intelligent decision support system and sustainable supply chain management.

Machfud is Professor in the Department of Agroindustrial Technology, IPB University, Indonesia. His research interests include supply chain management, agro-industrial production and management system, lean and green production system, system modelling and optimization.

Moh. Yani is Associate Professor at IPB University, Indonesia. He works on environmental chemistry and engineering area. Moh. Yani serves as a technical expert at the Ministry of Environment and Forestry of Republic of Indonesia. He also serves as a reviewer for many journals.

Eizo Taira is currently Professor of the Faculty of Agriculture at the University of the Ryukyus, Japan. His research interests include development of chemical and physical property for agricultural product using nearinfrared spectroscopy, especially in sugarcane.

ACKNOWLEDGMENTS

The research leading to this publication was partly funded by the Ministry of Research, Technology and Higher Education, Republic of Indonesia, under the special education and research scheme grant: Master leading to Doctor Education program for brilliant undergraduate degree holder (PMDSU-Pendidikan Magister menuju Doktor bagi Sarjana Unggul) batch II, fiscal year 2018 and Enhancing International Publication Program (PKPI-Program Peningkatan Publikasi Ilmiah) fiscal year 2018.

Figure

Research flow.

Fuzzy scale for risk severity and occurrence.

Fuzzy scale for correlation of risk event and risk agent.

Configuration of sugarcane supply chain.

Hierarchy and weights of supply chain performance metrics.

Activity breakdown for risk event identification.

Fuzzy-House of Risk for sugarcane farmers.

Table

Experts to involve in the research

Metrics performance definitions for farmers and mills in the sugarcane agroindustry supply chain

Sugarcane farmer supply chain performance

Sugar mill supply chain performance

Risk agent ARP values for sugar mills and distributors

Supply chain risk priority and mitigation effort to improve supply chain performance

REFERENCES

1. Asbjørnslett, B. E. (2009), Assessing the vulnerability of supply chains. In Zsiding, G. A. and Ritchie, B. (Eds.), Supply chain risk; A Handbook of Assessment, Management, and Performance (pp. 15-33), New York (US): Springer US,
2. Asrol, M., Marimin, M., Machfud, M., and Yani, M. (2018), Method and approach mapping of fair and balanced risk and value-added distribution in supply chains: A review and future agenda, International Journal of Supply Chain Management, 7(5), 74-95,
3. Asrol, M., Marimin, and Machfud, M. (2017), Supply chain performance measurement and improvement for sugarcane agro-industry, International Journal of Supply Chain Management, 6(3), 8-21.
4. Astuti, R., Marimin, M., Machfud, M., Arkeman, Y., Poerwanto, R., and Meuwissen, M. (2013), Risks and risks mitigations in the supply chain of mangosteen: A case study, Operations and Supply Chain Management, 6(May 2014), 11-25,
5. Balfaqih, H., Nopiah, Z. M., Saibani, N., and Al-Nory, M. T. (2016), Review of supply chain performance measurement systems: 1998-2015, Computers in Industry, 82, 135-150,
6. Ballou, R. H. (2007), The evolution and future of logistics and supply chain management, European Business Review, 19(4), 332-348,
7. Behzadi, G., O’Sullivan, M. J., Olsen, T. L., and Zhang, A. (2018), Agribusiness supply chain risk management: A review of quantitative decision models, Omega, 79(2018), 21-42,
8. Bittencourt, F. and Rabelo, R. J. (2008), A systematic approach for VE partners selection using SCOR model and the AHP method, Collaborative Method and Their Breeding Environments, 186(2008), 99-108,
9. BPS (2018), Statistik Tebu Indonesia 2017, Badan Pusat Statistik, Jakarta (ID).
10. Cafiero, C. (2008), Agricultural Producer Risk Management in a Value Chain Context : Implications for Developing Countries, Food and Agriculture Organization of the United Nations.
11. Chapman, P., Christopher, M., Jüttner, U., Peck, H., and Wilding, R. (2002), Identifying and managing supply chain vulnerability, Logistics and Transport Focus, 4(4), 59-70.
12. Chauhan, M. K., Varun, Chaudhary, S., Kumar, S., and Samar (2011), Life cycle assessment of sugar industry: A review, Renewable and Sustainable Energy Reviews, 15(7), 3445-3453,
13. Chauhan, S. S. and Proth, J. M. (2005), Analysis of a supply chain partnership with revenue sharing, International Journal of Production Economics, 97(1), 44-51,
14. Chiadamrong, N. and Kawtummachai, R. (2008), A methodology to support decision-making on sugar distribution for export channel: A case study of Thai sugar industry, Computers and Electronics in Agriculture, 64(2), 248-261,
15. Chopra, S. and Meindl, P. (2013), Supply Chain Management: Strategy, Planning and Operation (5th Ed.), Pearson, New York,
16. Dewayana, T. S., Sukardi, M. Sukardi, S., and Raharja, S. (2010), Rancang bangun model pengukuran kinerja pabrik gula, Jurnal Teknik Industri, 5(3), 151-228.
17. El Fajrin, A., Hartono, S., and Waluyati, L. R. (2015), The demand for refined sugar in food and beverage and pharmaceutical industries (in Indonesia), Agro Ekonomi, 26(2), 150-158.
18. Everingham, Y., Muchow, R., Stone, R., Inman-Bamber, N., Singels, A., and Bezuidenhout, C. (2002), Enhanced risk management and decision-making capability across the sugarcane industry value chain on seasonal climate forecast, Agriculutural Systems, 74(3), 459-477,
19. Fahrizal, M., Yani, M., Purwanto, M. Y. J., and Tip, S. (2014), Model penunjang keputusan pengembangan agroindustri gula tebu (studi kasus di Provinsi Nusa Tenggara Timur), Jurnal Teknologi Industri Pertanian, 24(3), 189-199.
20. FAO (2019), Food Outlook: Biannual Report and Global Food Markets, Food and Agriculture Organization (FAO), Rome.
21. Feifi, D., Martini, S., Astuti, R., and Hidayat, S. (2010), Added value and performance analyses of edamame soybean supply chain: A case study, Operations and Supply Chain Management An International Journal, 3(1), 148-163.
22. Giannakis, M. and Papadopoulos, T. (2016), Supply chain sustainability: A risk management approach, International Journal of Production Economics, 171, 455-470,
23. Higgins, A. J. and Muchow, R. C. (2003), Assessing the potential benefits of alternative cane supply arrangements in the Australian sugar industry, Agricultural Systems, 76(2), 623-638,
24. Kadwa, M. (2013), An Overview of Sugarcane Supply Chain Inconsistencies [disertasi], University of KwaZulu-Natal, Pietermaritzburg, TZA.
25. Kohli, A. S. and Jensen, J. B. (2010), Assessing effectiveness of supply chain collaboration: An empirical study, Supply Chain Forum: An International Journal, 11(2), 2-16.
26. Li, G., Fan, H., Lee, P. K. C., and Cheng, T. C. E. (2015), Joint supply chain risk management: An agency and collaboration perspective, International Journal of Production Economics, 164, 83-94,
27. Liu, W. B., Meng, W., Mingers, J., Tang, N., and Wang, W. (2012), Developing a performance management system using soft systems methodology: A Chinese case study, European Journal of Operational Research, 223(2), 529-540,
28. Maheshwari, S. and Jain, P. (2014), Supply chain management – review on risk management from supplier’s perspective. In DAAAM International Scientific Book 2014 (pp. 557-566),
29. Manuj, I., Mentzer, J. T., Manuj, L., and Mentzer, J. T. (2008), Global supply chain risk management strategies, International Journal of Physical Distribution & Logistics Management, 38(3), 192-223,
30. Marimin, Djatna, T., Machfud, Asrol, M., Papilo, P., Taufik, B., and Darmawan, M. A. (2020), Supply chain performance measurement and improvement of palm oil agroindustry: A case study at Riau and Jambi Province, IOP Conference Series: Earth and Environmental Science, 443, 012056,
31. Marimin, M., Djatna, T., Suharjito, S., Hidayat, S., Utama, D., Astuti, R., and Martini, S. (2013), Fuzzy Decision Analysis Techique in Supply Chain Management (in Indonesia), IPB Pr, Bogor, ID.
32. Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., and Zacharia, Z. G. (2001), Defining supply chain management, Journal of Business Logistics, 22(2), 1-25.
33. Mishra, D., Sharma, R. R. K., Kumar, S., and Dubey, R. (2016), Bridging and buffering: Strategies for mitigating supply risk and improving supply chain performance, International Journal of Production Economics, 180, 183-197,
34. Monczka, R. M., Handfield, R., Guinipero, R., and Patterson, J. (2009), Purchasing and Supply Chain Management (4th ed.), Mason: Nelson Education Ltd.
35. Muchfirodin, M., Guritno, A. D., and Yuliando, H. (2015), Supply chain risk management on tobacco commodity in temanggung, central java (Case Study at Farmers and Middlemen Level), Agriculture and Agricultural Science Procedia, 3(2015), 235-240,
36. Munir, M., Jajja, M. S. S., Chatha, K. A., and Farooq, S. (2020), Supply chain risk management and operational performance: The enabling role of supply chain integration, International Journal of Production Economics, 227, 107667,
37. Mustadjab, M., Fahriyah, F., and Hana, I. (2012), The impact of climate change toward sugarcane farmers’ income (in Indonesian), Agrise, 12(1), 74-86.
38. Nababan, M. B. (2013), Efisiensi Produksi Pabrik Gula nasional, M.S. Thesis, IPB University, Bogor, ID.
39. Neiger, D., Rotaru, K., and Churilov, L. (2009), Supply chain risk identification with value-focused process engineering, Journal of Operations Management, 27(2), 154-168,
40. Nicol, R. Mac, Ortmann, G., and Ferrer, S. (2007), Perceptions of key business and financial risks by large-scale sugarcane farmers in KwaZulu-Natal in a dynamic socio-political environment, Agrekon, 46(3), 351-370,
41. Nunes, L. J. R., Causer, T. P., and Ciolkosz, D. (2020), Biomass for energy: A review on supply chain management models, Renewable and Sustainable Energy Reviews, 120, 109658,
42. Oliva, F. L. (2016), A maturity model for enterprise risk management, International Journal of Production Economics, 173, 66-79,
43. Öztayşi, B. and Sürer, Ö. (2014), Supply chain performance measurement using a SCOR based fuzzy VIKOR approach. In Kahraman, C. and Öztaysi, B. (Eds.), Supply Chain Management Under Fuzziness, Studies in Fuzziness and Soft Computing (Vol. 313, pp. 119-224), Springer-Verlag Berlin Heidelberg.
44. Palma-Mendoza, J. A., Neailey, K., and Roy, R. (2014), Business process re-design methodology to support supply chain integration, International Journal of Information Management, 34(2), 167-176,
45. Pujawan, I. N. and Geraldin, L. H. (2009), House of risk: A model for proactive supply chain risk management, Business Process Management Journal, 15(6), 953-967,
46. Pujiatsih, H., Arifin, B., and Situmorang, S. (2014), Analisis posisi dan tingkat ketergantungan impor gula kristal putih dan gula kristal rafinasi indonesia di pasar internasional, Jurnal Ilmu Ilmu Agribisnis, 2(1), 18-25.
47. Reddy, J. M., Rao, N., and Krishnanand, L. (2019), A review on supply chain performance measurement systems, Procedia Manufacturing, 30(2019), 40-47,
48. Ritchie, B. and Brindley, C. (2007), Supply chain risk management and performance: A guiding framework for future development, International Journal of Operations and Production Management, 27(3), 303-322,
49. Ryding, A. and Sahlin, J. (2013), Improving supply chain risk management by introducing performance measurement systems, Master of Science Thesis, Stockholm, Sweden.
50. Sargent, R. G. (2013), Verification and validation of simulation models, Journal of Simulation, 7, 12-24,
51. SCC (2012), Supply Chain Operations Reference Model Revision 11.0. Supply Chain Operations Management, Supply Chain Council, New York, US.
52. Schöggl, J. P., Fritz, M. M. C., and Baumgartner, R. J. (2016), Toward supply chain-wide sustainability assessment: A conceptual framework and an aggregation method to assess supply chain performance, Journal of Cleaner Production, 131, 822-835,
53. Septiani, W., Marimin, Herdiyeni, Y., and Haditjaroko, L. (2016), Risk dependency chain model of dairy agro-industry supply chain using fuzzy logic approach, Supply Chain Forum, 17(4), 218-230,
54. Sriwana, I. K., Arkeman, Y., Syah, D., and Marimin, M. (2015), Risk identification of cacao, Proceeding of the 8th International Seminar on Industrial Engineering and Management, 71-75.
55. Starks, D. N. and Bukenya, J. O. (2008), Assessment of the dynamics of value added production in Alabama, In Southern Agriculutural Economics Association Annual Meeting, Dallas, 2-19.
56. Subiyakto (2016), Sugarcane borers and development of control techniques (in Indonesia), Jurnal Litbang Pertanian, 35(4), 179-186,
57. Subiyanto, S. (2014), Analisis efektivitas mesin/alat pabrik gula menggunakan metode overall equipments Effectiveness, Jurnal Teknik Industri, 16(1), 43-52,
58. Suharjito, S. and Marimin, M. (2012), Risks balancing model of agri-supply chain using fuzzy risks utility regression, Journal of Theoretical and Applied Information Technology, 41(2), 134-144.
59. Taira, E., Ueno, M., Furukawa, N., Tasaki, A., Komaki, Y., Nagai, J. I., and Saengprachatanarug, K. (2013), Networking system employing near infrared spectroscopy for sugarcane payment in Japan, Journal of Near Infrared Spectroscopy, 21, 477-483,
60. Ushada, M. and Murase, H. (2009), Development of kansei-based intelligent decision support system (KIDSS) for quality evaluation of a biological greening material: Abstractive parameters for eliciting consumer satisfaction, Engineering in Agriculture, Environment and Food, 2(3), 102-107,
61. van der Vorst, J. (2006), Performance measurement in agri-food supply chain networks: An overview, Quantifying the Agri-Food Supply Chain, 13-26,
62. van der Vorst, J., Da Silva, C. A., and Trienekens, J. H. (2007), Agro-Industrial Supply Chain Management: Concepts and Applications (Vol. 17), Rome (IT): Food and Agriculture Organization of the United Nations.
63. van der Vorst, J. and Snels, J. (2014), Developments and Needs for Sustainable Agro-Logistics in Developing Countries, World Bank Position Note.
64. Vanany, I., Zailani, S., and Pujawan, N. (2009), Supply chain risk management: Literature review and future research, International Journal of Information Systems and Supply Chain Management, 2(1), 16-33.
65. Wagner, S. M. and Bode, C. (2008), An empirical examination of supply chain performance along several dimension of risk, Journal of Business Logistics, 29(1), 307-325,
66. Wakolbinger, T. and Cruz, J. M. (2011), Supply chain disruption risk management through strategic information acquisition and sharing and risk-sharing contracts, International Journal of Production Research, 49(13), 4063-4084,
67. Winter, A., Deniaud, I., and Caillaud, E. (2014), Risk assessment of a sustainable supply chain, case study at Kuehne + Nagel Luxembourg, Proceedings of the 2014 2nd IEEE International Conference on Logistics and Operations Management, Rabat, Morocco, 118-124,
68. Yager, R. R. (1993), Non-numeric multi-criteria multi-person decision making, Group Decision and Negotiation, 2(1), 81-93,
69. Yao, D. Q., Yue, X., and Liu, J. (2008), Vertical cost information sharing in a supply chain with value-adding retailers, Omega, 36(5), 838-851,
70. Yue, Z. and Jia, Y. (2013), A method to aggregate crisp values into interval-valued intuitionistic fuzzy information for group decision making, Applied Soft Computing, 13(5), 2304-2317,
 Do not open for a day Close