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International Journal of Production Research Vol. 50, No. 11, 1 June 2012, 3039–3050

Supply chain risk management and its mitigation in a food industry

Ali Diabat a*, Kannan Govindan

b and Vinay V. Panicker

a Engineering Systems and Management, Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates;

b Department of Business and Economics, University of Southern Denmark, Campusvej, Odense M, 5230 Denmark;

c National Institute of Technology, Mechanical Engineering, Calicut, India

(Received 13 September 2010; final version received 6 April 2011)

We create a model which analyses the various risks involved in a food supply chain with the help of interpretive structural modelling (ISM). The various types of risks were identified based on a review of the literature and in consultation with experts in the food industry. The types of risks are clustered into five categories and risk mitigation is discussed. The model developed is validated with the help of a case study involving a food products manufacturing firm.

Keywords: supply chain risk management (SCRM); food supply chain; interpretive structural modelling (ISM); risk mitigation

1. Introduction

Supply chain risk may result from unexpected variations in capacity constraints, or from breakdowns, quality problems, fires or even natural disasters at the supplier end (Blackhurst et al. 2005, Yang and Yang 2010). A failure of any one element in a supply chain potentially causes disruptions for all partnering companies upstream and downstream (Yang and Yang 2010). For example, the leading telecom company Ericsson was severely impacted by a fire at one of its suppliers (Wall Street Journal 2001).

The vulnerability of a supply chain increases with increasing uncertainty (Svensson 2000), and it increases even further if companies, by outsourcing, have become dependent on other organisations. The greater uncertainties in supply and demand, increasing globalisation of the market, shorter and shorter product and technology life cycles, and the increased use of manufacturing, distribution and logistics partners resulting in complex international supply network relationships have led to increased exposure to risks in the supply chain.

Although many risks exist in business, three have applicability to the supply chain, namely supply risks, operations risks and demand risks. Supply risks reside in the course of movement of materials from suppliers to the firm and include the reliability of suppliers, and considerations such as single versus multiple sourcing and centralised versus decentralised sourcing. Operational risks affect the firm’s internal ability to produce goods and services, ultimately affecting the profitability of the company, and may result from a breakdown in manufacturing or processing capability and/or changes in technology. Demand risks reside in the movement of goods from the firm to the customers, and include the risk of obsolescence, stock-outs, and over-inventory.

The development of effective strategies for managing risk hinges on first understanding the sources of risk and their relationships. In this paper we model the various risks that can have an impact on the food supply chain. The main objectives of this paper are:

(1) to identify and rank the risks involved in food supply chains; (2) to determine the interactions among identified risks; and (3) to understand the managerial implications of this research.

The paper is organised as follows. Section 2 surveys the literature on supply chain risks and their mitigation. Section 3 describes the solution methodology. Section 4 provides an overview of the company that is the subject of the case study, and applies the methodology developed in this paper to analyse the risks it faces in its food supply chain, followed by discussion and conclusions in Section 5.

*Corresponding author. Email: [email protected]

ISSN 0020–7543 print/ISSN 1366–588X online

� 2012 Taylor & Francis http://dx.doi.org/10.1080/00207543.2011.588619

http://www.tandfonline.com

2. Literature survey

Some of the classic techniques of risk management are:

(1) prevention or lowering of risks through understanding; (2) controlling the impact of risk, so that even if an adverse event occurs, the impact is minimised; (3) mitigating risk by transferring it to other parties; (4) diversification of products; (5) risk pooling.

Insurance provides one example of mitigating risk by transferring it to other parties. In this case the insurance

company assumes the risk for a price. Deloach (2000) defines business risk as ‘the level of exposure to uncertainties that the enterprise must understand

and effectively manage as it executes its strategies to achieve its business objectives and create value’. A measure of

risk combines a measure of the probability of occurrence of each primary event with a measure of the consequences

of that event. Quantitatively, risk can be calculated as the product of the probability of an event and the business

impact (or severity) of that event. A number of authors have studied supply chain risks or supply chain risk management. Norrman and Lindroth

(2002) define supply chain risk management as collaborating with partners to deal with risks and uncertainties

caused by, or impacting on, logistics-related activities or resources. Supply chain risk management (SCRM) can be

defined as ‘the management of supply chain risks through coordination or collaboration among the supply chain

partners so as to ensure profitability and continuity’ (Tang 2006). Jüttner et al. (2002) have observed that the use of the term ‘risk’ can be confusing, and argue that risk should be

separated from ‘risk (and uncertainty) sources’ and ‘risk consequences’ (risk impact). Risk sources are the

environmental, organisational or supply chain related variables that cannot be predicted with certainty and that

affect the supply chain outcome variables (Norrman and Jansson 2004). Jüttner et al. (2002) organised the risk

sources relevant for supply chains into three categories:

(1) external to the supply chain; (2) internal to the supply chain; and (3) network-related.

Johnson (2001) classified risks into two categories:

(1) supply risks (e.g. capacity limitations, currency fluctuations and supply disruptions); and (2) demand risks (e.g. seasonal imbalances, volatility of fads, new products).

Zsidisin et al. (2000) considered supply risks related to design, quality, cost, availability, manufacturability,

suppliers, legal and environmental issues, health and safety. Chidambaram (2003) identified the steps involved in handling risk as risk classification, risk identification, risk

calculation and implementation/validation. In the context of supply chain risk management, Juttner et al. (2003)

consider the steps in handling risk as:

. Assessing risk sources in the supply chain.

. Defining adverse consequences for the supply chain.

. Identifying risk drivers.

. Mitigating risks for the supply chain.

Tang (2006) divided risk into operational risks and disruption risks. Operational risks are associated with

inherent uncertainties such as uncertain customer demand, uncertain supply, and uncertain cost, whereas disruption

risks are associated with major disruptions caused by natural and man-made disasters such as earthquakes, floods,

hurricanes, terrorist attacks, or economic crises such as currency devaluation or strikes. He finds that the business

impact associated with disruption risks is much greater than that of the operational risks. Tang (2006) associated

supply chain risks with four management areas, namely supply management, demand management, product

management, and information management, as illustrated in Figure 1. Supply management involves coordinating

with upstream partners to ensure timely delivery of supplies. Demand management involves coordinating with

downstream partners to influence demand in a beneficial manner. Product management involves modifying the

product or process design so as to make it easier to ensure that supply meets demand. Information management

3040 A. Diabat et al.

involves an effort on the part of supply chain partners to improve their coordination, which may involve sharing

various types of information that is available to individual supply chain partners. There have been a number of key findings related to supply chain risk. Chopra and Sodhi (2004) found that risk

reduction can be expensive; pooling forecasted risk across partners may reduce the cost of mitigating risks. Harland

et al. (2003) developed a supply chain risk management tool and tested it on a case study. Johnson (2001) presented

risk reduction methodologies and listed the lessons learned from managing supply chain risk. Other relevant

literature on supply chain risk includes Huang et al. (2009), Yang and Yang (2010), Kumar et al. (2010), Lockamy

III and McCormack (2010), Wu and Olson (2010) and Canbolat et al. (2008).

3. Solution methodology

Handling risk involves four steps: risk classification, risk identification, risk calculation and implementation/

validation. The purpose of risk classification is to have a collective viewpoint on the group of factors, in order to

help to identify the sources of maximum risk. Risk identification enumerates the sources of risk. The purpose of risk

calculation is to calculate the impact of various factors on the risk and may require the use of a decision support

tool. Implementation and validation is the final step in risk management (Wu et al. 2006). Based on Yin’s work on case study design (Yin 2003) we adopt the holistic single case design, and use the

methodology of interpretive structural modelling (ISM) to provide insight. The mathematical foundations of the

methodology can be found in Harary et al. (1965), while the philosophical basis for the development of the ISM

approach is presented in Warfield (1973). The ISM methodology was developed as a communication tool for

complex situations. It has been used for policy analysis (Hart and Malone 1974, Hawthorne and Sage 1975, Brand

et al. 1976, Kawamura and Christakis 1976) and management research (Mandal and Deshmukh 1994, You et al.

1994, Jharkharia and Shankar 2004, 2005, Bolaňos et al. 2005, Ravi et al. 2005, Sushil 2005, Kannan and Haq 2007). Despite the benefits of the ISM methodology, namely that it transforms unclear, poorly articulated models of

systems into clear, well-defined models (Sage 1977), it has certain drawbacks (Kannan and Haq 2007). One

drawback is that the model obtained may be influenced strongly by the bias of the person who is judging the

variables, as the relations among the variables always depends on that person’s knowledge and familiarity with the

firm, its operations, and its industry. Another drawback is that in the ISM framework, no weights are associated

with the variables to take into account their relative importance. The ISM methodology has been widely applied in

various applications (Table 1). The various steps involved in the ISM methodology are given below (Kannan and Haq 2007) and in the flow

chart shown in Figure 2.

Step 1: The risks involved in the food supply chain under study are listed.

Product management

Supply chain

risks

Supply management

Demand management

Information management

Figure 1. Basic approaches for risk mitigation (Tang 2006). Reprinted from International Journal of Production Economics, 103/2, C.S. Tang, Perspectives in supply chain risk management, 451-488, Copyright (2006), with permission from Elsevier.

International Journal of Production Research 3041

Step 2: Based on the identified risks in Step 1, a contextual relationship is established among risks with respect to which pairs of remaining risks will be examined.

Step 3: A structural self-interaction matrix (SSIM) is developed, which indicates pairwise relationships among risks for the system under consideration.

Step 4: A reachability matrix is developed from the SSIM and the matrix is checked for transitivity. The transitivity rule states that if a variable ‘A’ is related to ‘B’ and ‘B’ is related to ‘C’, then ‘A’ is necessarily related to ‘C’.

Step 5: The reachability matrix obtained in Step 4 is partitioned into different levels.

Step 6: Based on the reachability matrix, a directed graph is drawn and the transitive links are removed.

Step 7: The resultant digraph is converted into an ISM by replacing variable nodes with statements.

Step 8: The ISM model developed in Step 7 is checked for conceptual inconsistency and necessary modifications are made.

4. Case study

4.1 Overview of the company

The company under study, RMK food products, is a leading producer of food products in south India. The company manufactures flours and powders for the household, and produces at least 20 different products. The firm obtains the required raw materials from around seven suppliers. The manufactured packed food product is

List of risks involved in the food supply chain under study

Remove transitivity from the diagraph Develop diagraph

Develop the reachability matrix in its conical form

Partition the reachability matrix into different levels

Develop reachability matrix Develop a structural self-interaction matrix

(SSIM)

Establish contextual relationship (Xij ) between variables (i, j)

Literature review

Replace variables nodes with relationship statements

Represent relationship statement into model for the risks involved in the food supply chain under study

Is there any conceptual

inconsistency?

Yes

Develop reachability matrix

Figure 2. Flow chart for the ISM methodology.

3042 A. Diabat et al.

distributed in and around the state through a network of many distributors. The product reaches the customer with the help of retailers. An overview of RMK’s supply chain, along with the associated risks, is shown in Figure 3.

We now apply the framework developed above to analyse the supply chain risks for the company under study.

4.2 Identification of the various risks in the supply chain

We begin by enumerating the risks for the company based on a review of the literature and consultation with industry experts.

4.2.1 Macro level risks

The macro level risks for the food supply chain are due to natural disasters, diseases like bird flu (mentioned earlier), political unrest in the region, terrorist attacks, government regulations, labour strikes and lack of skilled personnel.

4.2.2 Demand management risks

Demand management risk in a supply chain is connected to demand for the product. Demand for a product can change suddenly due to economic downturn, changes in customer tastes, failure to communicate with customers or an increase in the bargaining power of customers, or demand can become more volatile.

4.2.3 Supply management risks

Supply management risk in a supply chain is associated with obstacles at the supply end. A shortage in raw materials is a major reason for this risk. Other reasons include suppliers going bankrupt, a failure in communications between the client (in this case RMK food products) and a supplier, failure of the partnership, poor quality of the supplied goods, and delays at the supplier end.

4.2.4 Product/service management risks

Product/service management risk is caused by maintaining an inventory level that is too high, thus increasing holding costs, or by underutilised capacity.

4.2.5 Information management risks

Information management risk in the supply chain is due to errors in forecasting the demand for the product, distortions in the information sharing and failures in IT systems.

Supply management risk Demand management risk

Product/service management risk

Information management risk

Material flow Information flow Suppliers (S1)

Customers RetailerFood products

manufacture

Suppliers (S2)

Suppliers (S7)

Distributor

Figure 3. Overview of RMK’s supply chain and its risks.

International Journal of Production Research 3043

Table 2. Summary of various risks and mitigation strategies.

Risk category Risk type Mitigation strategy

Macro level risks 1. Natural disaster Identify vulnerability points and have contingency plans

2. Diseases like bird flu 3. Political unrest Lobbying 4. Terrorist attacks 5. Government regulation Always support a participative style of

management 6. Labour strikes 7. Lack of skilled personnel

Demand management risks 1. Sudden loss of demand due to economic downturn

Cost reduction in operations

2. Volatile demand Cost reduction in operations; manage demand through promotions and incentives to cus- tomers; assistance from professionally qualified agencies

3. Changes in customer tastes Manage demand through promotions and incen- tives to customers; work to incorporate changes in customer tastes; assistance from profession- ally qualified agencies

4. Failure to communicate with customers

Better planning and coordination of supply and demand; identify vulnerability points and have contingency plans; invest in good communica- tions infrastructure

Supply management risks 5. Supplier bankruptcy Multiple sourcing strategy; supplier evaluation and selection

6. Communication failure Multiple sourcing strategy 7. Failure of the partnership Multiple sourcing strategy; strengthen and build

trust with suppliers 8. Poor quality of the supplied goods Better planning and coordination with suppliers;

multiple sourcing strategy; flexible capacity; multiple sourcing strategy; supplier develop- ment programme

9. Inability of supply Product/service management risks 10. Excessive inventory Better planning and coordination of supply and

demand; flexible capacity 11. Underutilised capacity Better planning of capacity requirements

Information management risks 12. Error in forecasting Better planning and coordination of supply and demand; investment in good communications infrastructure

13. Distortions in information sharing Identify vulnerability points and have contingency plans

14. Failure in IT systems

Table 1. Applications of the ISM methodology.

Year Author Application

1975 Hawthorne and Sage Higher education program planning 1977 Sage Modelling complex situations associated with large systems 1980 Jedlicka and Meyer Exploring factors involved in a cross-cultural context 1992 Saxena et al. Determining the hierarchy and class of elements in cement industry 1993 Mandal and Deshmukh Vendor selection in supply chains 1999 Kanungo et al. Developing an information system effectiveness framework 2004 Ravi and Shankar Barriers to reverse logistics 2005 Jharkaria and Shankar Enablers of IT implementation in supply chain 2005 Ravi et al. Identifying key reverse logistic variables 2006 Faisal et al. Modelling the enablers for supply chain risk mitigation 2006 Thakkar et al. Integrated approach with ISM and ANP to develop a balanced score card

3044 A. Diabat et al.

For each risk type, a different risk mitigation strategy needs to be adopted (Oke and Gopalakrishnan 2009). We use the risk mitigation strategies given in Table 2. Since many of the risks are associated with rare events, it takes a substantial amount of time to evaluate the proposed risk mitigation strategy.

4.3 Development of structural self-interaction matrix (SSIM)

A structural self-interaction matrix (SSIM) is a matrix indicating the pairwise relationships among the variables, in this case the risks in the food supply chain of the firm under consideration. We now develop the SSIM for the various risks identified above.

The symbols used to denote the direction of relationship between the risks are given below. For variables (in this case risks) i and j, the (i, j) entry of the SSIM is ‘V’ if i will help to alleviate j; ‘A’ if i will be alleviated by j, ‘X’ if i and j help to alleviate each other, and ‘O’ if there is no relation.

V – Risk i will help to alleviate Risk j. A – Risk i will be alleviated by Risk j. X – Risks i and j will help to alleviate each other. O – Risks i and j are unrelated.

For example:

. Alleviating macro level risk helps to alleviate information management risk (V).

. Product/service management risk will be alleviated by alleviating information management risk (A).

Based on these relationships the SSIM is developed (Table 3).

4.4 Reachability matrix

The reachability matrix is derived from the structural self-interaction matrix (SSIM) developed in the previous step. The symbols are replaced with binary numbers 1 and 0 as follows:

. If the (i, j) entry in the SSIM is V, the (i, j) entry in the reachability matrix becomes 1 and the (j, i) entry becomes 0.

. If the (i, j) entry in the SSIM is A, the (i, j) entry in the reachability matrix becomes 0 and the (j, i) entry becomes 1.

. If the (i, j) entry in the SSIM is X, the (i, j) entry in the reachability matrix becomes 1 and the (j, i) entry also becomes 1.

. If the (i, j) entry in the SSIM is O, the (i, j) entry in the reachability matrix becomes 0 and the (j, i) entry also becomes 0.

From the SSIM, the initial reachability matrix is developed using the above rules. The initial reachability matrix is given in Table 3(a). The conceptual relationships among the risks corresponding to the initial reachability matrix are illustrated by the digraph shown in Figure 4. The final reachability matrix (Table 3(b)) is derived from the initial reachability matrix using the transitivity rule, which states that if a variable ‘A’ is related to ‘B’ and ‘B’ is related to ‘C’, then ‘A’ is necessarily related to ‘C’. The final diagraph is shown in Figure 5.

Table 3. Structural self-interaction matrix for the risks in the food supply chain.

Information management

risk (5)

Product/service management

risk (4) Supply management

risk (3)

Demand management

risk (2) Macro level

risk (1)

Macro level risk (1) V V V X – Demand management risk (2) X V X – – Supply management risk (3) X V – – – Product/service management risk (4) A – – – – Information management risk (5) – – – – –

International Journal of Production Research 3045

4.5 Level partitions

The reachability matrix obtained above in Section 4.3 is now partitioned into different levels. The reachability and antecedent sets for each risk (Warfield 1974) are found from the final reachability matrix (Table 3(b)). The reachability set for a particular risk consists of itself and the other risks which it may help to alleviate. The antecedent set for a particular risk consists of itself and the other risks which may help in alleviating it. The intersection set for each risk is the intersection of the corresponding reachability and antecedent sets. If the reachability set and the intersection set are the same then that risk is considered to be in level I and is given the top position in the ISM hierarchy (Kannan and Haq 2007), meaning that this risk would not help in alleviating any other risk above its own level. With this partition, iteration 1 is completed. After the first iteration, the risks classified to level I are discarded and the above procedure is repeated on the remaining risks to determine the level II risks. These iterations are continued until the level of each risk has been determined. Applying this procedure to the

Table 4(a). Level partition of risks – Iteration 1.

Risks Reachability set Antecedent set Intersection Level

Macro level risk 12345 1235 1235 Demand management risk 12345 1235 1235 Supply management risk 12345 1235 1235 Product/service management risk 4 12345 4 I Information management risk 12345 1235 1235

Table 3(b). Final reachability matrix for the risks in the food supply chain of the firm.

1 2 3 4 5 Driver power

1 1 1 1 1 1 5 2 1 1 1 1 1 5 3 1 1 1 1 1 5 4 0 0 0 1 0 1 5 1 1 1 1 1 5 Dependence power 4 4 4 5 4

Figure 5. Final diagraph for the risks.

Figure 4. Initial diagraph for the risks.

Table 3(a). Initial reachability matrix for the risks in the food supply chain of the firm.

1 2 3 4 5 Driver power

1 1 1 1 1 1 5 2 1 1 1 1 1 5 3 0 1 1 1 1 4 4 0 0 0 1 0 1 5 0 1 1 1 1 4 Dependence power 2 4 4 5 4

3046 A. Diabat et al.

reachability matrix obtained in Section 4.4 results in a partition in which product/service management risk is positioned at level I and forms the top level of the ISM hierarchy, while the remaining risks fall in level II. The results for iterations 1 and 2 are given in Tables 4(a) and 4(b), respectively.

4.6 Modelling of food supply chain risk

The analysis above yields an ISM hierarchy in which product/service management risk is at level I (the top level) and all other risks are at level II. The resulting ISM model is illustrated in Figure 6.

4.7 MICMAC analysis

MICMAC stands for Matrice d’Impacts Croisés Multiplication Appliquée à un Classement, which means ‘cross- impact matrix multiplication applied to classification’. In MICMAC analysis, the dependence power and driver power of the variables are analysed. On the basis of the above study, the risks were classified into four sectors, namely autonomous, dependent, linkage and driver/independent. In the final reachability matrix in Table 3(b), the driving power and dependence of each of the risks has been calculated. Risks having weak driver power and weak dependence will fall in sector I and are called autonomous elements. Risks having weak driver power but strong dependence will fall in sector II and are called dependent elements. Risks having both strong driver and dependence power will fall in sector III and are called linkage elements. These elements are unstable due to the fact that any action on these elements will affect the others and also may have a feedback effect on themselves. The driver or independent variables will fall in sector IV (Kannan and Haq 2007).

Applying MICMAC analysis to the ISM model of the risks in the food supply chain of the firm under study yields the driving power and dependence power diagram shown in Figure 7. To illustrate how MICMAC analysis is done, it is observed from Table 3(b) that the demand management risk has a driver power of 5 and dependence power of 4, therefore in Figure 7 this risk is positioned at coordinates which correspond to a driver power of 5 and

Product/service

management risks

Supply

management

risks

Information

management

risks

Macro level

risks

Demand

management

risks

Figure 6. Model for the food supply chain risks of the firm.

Table 4(b). Level partition of risks – Iteration 2.

Risks Reachability set Antecedent set Intersection Level

Macro level risk 1235 1235 1235 II Demand management risk 1235 1235 1235 II Supply management risk 1235 1235 1235 II Information management risk 1235 1235 1235 II

International Journal of Production Research 3047

a dependence power of 4. The objective of classifying the risk is to analyse the driver power and dependence power

of the risk. The analysis shows that the macro level risk (1), demand management risk (2), supply management risk (3) and

information management risk (5) have both strong driver power and dependence power and consequently fall in

sector III, and are thus linkage elements. These risks can thus be considered as unstable risks and the actions taken

on them may affect other risks. The product/service management risk (4) falls in sector I and is thus an autonomous element.

5. Discussion and conclusion

The risks involved in the food supply chain of the firm under study were identified and strategies for mitigating these

risks were proposed. This type of categorisation is key to identifying the relevant mitigation strategies to be adopted. Five categories of risk were identified, namely product/service management risk, macro level risk, demand

management risk, supply management risk, and information management risk. An ISM model of the risks was

constructed; the model placed product/service management risk at level I of the ISM hierarchy, suggesting that alleviating this risk would not help in alleviating any of the other risks, while the model placed all other risks at level

II of the ISM hierarchy. MICMAC analysis revealed that product/service management risk had weak driving power and weak dependence power, and consequently was classified as an autonomous factor. All other risks had strong

driving power and strong dependence power, and consequently were classified as linkage elements. The managerial

implications of this analysis are that since product/service management risk is at the top of the ISM hierarchy and is also classified as an autonomous variable, management should assign high priority to mitigating this risk.

Based on this analysis, management has taken steps to mitigate the risks identified. To mitigate the demand

management risk, the company plans to invest in good communication infrastructure to avoid any failure in communication. This will also help in mitigating the information management risk. Similarly demand forecasting is

being done with the help of professionally qualified agencies so that problems with volatile demand and declines in

demand can be mitigated. To avoid problems at the supplier end, the company has created a department for supplier selection and

evaluation. The requirements of the company are conveyed earlier to the suppliers and steps were taken to reduce

the likelihood of a shortage in raw materials. The company now places high priority on building long term partnerships with suppliers and seeks to ensure that the quality of the supplied goods are checked by the supplier

before leaving the supplier.

5 1, 2, 3, 5

4 IV III

2 I II

1 4

D ri vi

n g p

o w

e r

1 2 3 4 5

Dependence power

Figure 7. Driving power and dependence power diagram.

3048 A. Diabat et al.

The above model is based on the interpretive structural modelling methodology, which has its limitations. One limitation is that the model obtained is highly dependent on the judgements of the expert team, so the model must be validated. Structural equation modelling (SEM) can be used to validate the model.

There are several directions for future research. One is to validate the model obtained using structural equation modelling. It would also be of interest to determine the impact of a given mitigation strategy on the various partners in the supply chain.

Acknowledgements

This research was financially supported by RMK food products, Tiruchirappalli, Tamilnadu, India under the project ‘study on impact and capability of risk supply chain management for a food industry’ (No. C. S./RMK/2007/CS13).

References

Blackhurst, J., et al., 2005. An empirically derived agenda of critical research issues for managing supply-chain disruptions.

International Journal of Production Research, 43 (19), 4067–4081. Bolaňos, R., Fontela, R.E., and Pastor, P., 2005. Using interpretive structural modeling in strategic decision-making groups.

Management Decision, 43 (5/6), 877–895. Brand Jr, D.H., Irwin, D.M. and Kawamura, K., 1976. Implementation of interpretive structural modeling in a state-level

planning context. 7th annual Pittsburgh conference on modeling and simulation, 26–27 April 1976, Pittsburgh, Instrument

Society of America, 826–834. Canbolat, Y.B., et al., 2008. Analysing risk in sourcing design and manufacture of components and sub-systems to emerging

markets. International Journal of Production Research, 46 (18), 5145–5164. Chidambaram, V., 2003. Supply chain risk management: A study on inbound supply risk analysis. Thesis (MS). Department of

Industrial Engineering, Arizona State University. Chopra, S. and Sodhi, M., 2004. Avoiding supply chain breakdown. Sloan Management Review, 46 (1), 53–62. Christopher, M., 1992. Logistics and supply chain management. London: Pitman.

Deloach, J.W., 2000. Enterprise-wide risk management. Strategies for linking risk and opportunities. London: Financial Times/

Prentice-Hall.

Harary, F., Norman, R.Z., and Cartwright, D., 1965. Structural models: An introduction to the theory of directed graphs.

New York: Wiley. Faisal, M.N., Banwet, D.K., and Shankar, R., 2006. Supply chain risk mitigation: modelling the enablers. Business Process

Management Journal, 12 (4), 535–552. Harland, C., Brenchley, R., and Walker, H., 2003. Risk in supply networks. Journal of Purchasing & Supply Management, 9 (2),

51–62. Hart, W.L. and Malone, D.W., 1974. Goal setting for a state environmental agency. IEEE conference on decision and

control, 20–22 November, Phoenix, AZ, IEEE. Hawthorne, R.W. and Sage, A.P., 1975. On applications of interpretive structural modelling to Higher education program

planning. Socio-Economic Planning Sciences, 9 (1), 31–43. Huang, H.-Y., Chou, Y.-C., and Chang, S., 2009. A dynamic system model for proactive control of dynamic events in full-load

states of manufacturing chains. International Journal of Production Research, 47 (9), 2485–2506.

Jedlicka, A. and Mayer, R., 1980. Interpretive structural modelling cross-cultural uses. Transactions on Systems, Man, and

Cybernetics, 10 (1), 49–51.

Jharkharia, S. and Shankar, R., 2004. IT enablement of supply chains: modeling the enablers. International Journal of

Productivity and Performance Management, 53 (8), 700–712. Jharkharia, S. and Shankar, R., 2005. IT enablement of supply chains: understanding the barriers. Journal of Enterprise

Information Management, 18 (1/2), 11–27. Johnson, M.E., 2001. Learning from toys: lessons in managing supply chain risk from the toy industry. California Management

Review, 43 (3), 106–124. Jüttner, U., Peck, H. and Christopher, M., 2002. Supply chain risk management: outlining an agenda for future

research. In: J. Griffiths, F. Hewitt and P. Ireland, eds. Proceedings of the Logistics Research Network 7th annual

conference, 4–6 September 2002, Birmingham. London: Institute of Logistics and Transport, 443–450.

Kannan, G. and Haq, N.A., 2007. Analysis of interactions of criteria and sub-criteria for the selection of supplier in the

built-in-order supply chain environment. International Journal of Production Research, 45 (17), 3831–3852. Kanungo, S., Duda, S., and Srinivas, Y.A., 1999. A structured model for evaluating information systems effectiveness.

Systems Research and Behavioral Science, 16 (6), 495–518.

International Journal of Production Research 3049

Kawamura, K. and Christakis, A.N., 1976. The role of structural modeling in technology assessment. 2nd international congress on technology assessment, October 1976, The University of Michigan, Ann Arbor.

Knight, F.H., 1921. Risk, uncertainty, and profit. Boston, MA: Hart, Schaffner & Marx; Houghton Mifflin Company.

Kumar, S.K., Tiwari, M.K., and Babiceanu, R.F., 2010. Minimisation of supply chain cost with embedded risk using computational intelligence approaches. International Journal of Production Research, 48 (13), 3717–3739.

Lockamy III, A. and McCormack, K., 2010. Analysing risks in supply networks to facilitate outsourcing decisions. International Journal of Production Research, 48 (2), 593–611.

Mandal, A. and Deshmukh, S.G., 1993. Vendor selection using interpretative structural modeling. International Journal of

Operations & Productions Management, 14 (6), 52–59. Norrman, A. and Jansson, U., 2004. Ericsson’s proactive supply chain risk management approach after a serious sub-supplier

accident. International Journal of Physical Distribution & Logistics Management, 34 (5), 434–456.

Norrman, A. and Lindroth, R., 2002. Supply chain risk management: purchasers’ vs planners’ views on sharing capacity investment risks in the telecom industry. Proceedings of the 11th international annual IPSERA conference, 25–27 March, Twente University. Enschede NI: IPSERA, 577–595.

Oke, A. and Gopalakrishnan, M., 2009. Managing disruptions in supply chains: A case study of a retail supply chain.

International Journal of Production Economics, 118 (1), 168–174. Porter, M., 1985. Competitive advantage. New York: The Free Press. Ravi, V. and Shankar, R., 2004. Analysis of interactions among the barriers of reverse logistics. Technological Forecasting and

Social Change, 72 (8), 1011–1029. Ravi, V., Shankar, R., and Tiwari, M.K., 2005. Productivity improvement of a computer hardware supply chain. International

Journal of Productivity and Performance Management, 54 (4), 239–255.

Ritchie, R.L. and Brindley, C.S., 2001. The information–risk conundrum. Journal of Marketing Intelligence and Planning, 19 (1), 29–37.

Sage, A.P., 1977. Interpretive structural modeling: Methodology for large-scale systems. New York, NY: McGraw-Hill, 91–164.

Saxena, J.P., Sushil, J., and Vrat, P., 1992. Hierarchy and classiEcation of program plan elements using interpretive structural modeling: a case study of energy conservation in the Indian cement industry. Systems Practice, 5 (6), 651–670.

Sushil, J., 2005. Interpretive matrix: a tool to aid interpretation of management and social research. Global Journal of Flexible Systems Management, 6 (2), 27–30.

Svensson, G., 2000. A conceptual framework for the analysis of vulnerability in supply chains. International Journal of Physical Distribution & Logistics Management, 30 (9), 731–750.

Tang, C.S., 2006. Perspectives in supply chain risk management. International Journal of Production Economics, 103 (2), 451–488.

Thakkar, J., et al., 2006. Development of a balanced scorecard: an integrated approach of interpretive structural modelling (ISM) and analytic network process (ANP). International Journal of Productivity and Performance Management, 56 (1), 25–69.

Wall Street Journal, 2001. Trial by fire – a blaze in Albuquerque sets off major crisis for cell-phone giants, 29 January.

Warfield, J.N., 1973. An assault on complexity. Battelle Memorial Institute, Columbus, Ohio. Battelle Monograph. Warfield, J.W., 1974. Developing interconnected matrices in structural modeling. IEEE Transactions on Systems, Man and

Cybernetics, 4 (1), 51–81.

Wu, T., Blackhurst, J., and Chidambaram, V., 2006. A model for inbound supply risk analysis. Computers in Industry, 57 (4), 350–365.

Wu, D.D. and Olson, D., 2010. Enterprise risk management: a DEA VaR approach in vendor selection. International Journal of Production Research, 48 (16), 4919–4932.

Yang, B. and Yang, Y., 2010. Postponement in supply chain risk management: a complexity perspective. International Journal of Production Research, 48 (7), 1901–1912.

Yin, R.K., 2003. Case study research: Design and methods. 3rd ed. Thousand Oaks, CA: Sage.

You, N., Kato, Y., and Kitaoka, M., 1994. Numerous data in hierarchy for knowledge concentrated in decision tree. Computers & Industrial Engineering, 27 (1), 535–539.

Zsidisin, G., Panelli, A., and Upton, R., 2000. Purchasing organisation involvement in risk assessment, contingency plans, and

risk management: an exploratory study. Supply Chain Management: An International Journal, 5 (4), 187–197.

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