Decision Modeling for Risk Bearing in Sustainable Supply Chains using ISM Approach

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1 Decision Modeling for Risk Bearing in Sustainable Supply Chains using ISM Approach Sachin Mangla Department of Mechanical and Industrial Engineering, Indian Institute of Technology, Roorkee, India Dr. Jitendra Madaan, Department of Mechanical and Industrial Engineering, Indian Institute of Technology, Roorkee, India Ravi Shankar, Department of Management Studies, Indian Institute of Technology Delhi, New Delhi, India Abstract To make our sustainable SC capable to withstand fluctuation proactive planning is required. To supplement planners a structured decision model is required. This paper proposes ISM to depict interactions between sustainable factors influencing risk. Comprehensive MICMAC along with ISM hierarchy is presented to achieve desired impact on performance. Keywords: ISM, risk events, Sustainable Supply Chain (SSC) 1. Introduction From past few years, sustainability of supply chains has gained significant attention due to growing environmental awareness among masses. The sustainability issues are relevant to supply chain planners, because their supplying authorities, ultimate users, regulating agencies, governmental legislations, their workforce demanding it in supply chain. The organizations should address supply chain risks; manage the issues related to environment and social along 1

2 with their business activities. For an organization, to manage the disruptions and risks in supply chain an efficient planning and preparedness is necessary. Here, we build a framework for risk bearing supply chain that could sustain under risks and uncertainties associated along with environmental, social and economical concern issues for an organization. Further, there are various parameters and variables associated with a supply chain those have already or could create risks events for a supply chain. We have studied and categorized some variables and parameters into barriers and driving variables influencing risks in risk sustainable supply chain (SSC). The further of the paper is prepared as follows. We explain the purpose behind this study followed by development of a framework of risk influencing SSC is described in section 2. While section 3 explains the methodology of research along with detailing and development of Interpretive Structural Modeling (ISM) methodology to built final model. Further, the research discussion, managerial implications and future possibilities in the research is provided in final section. 2. Risk influencing sustainable supply chain outline There are numerous kinds of unexpected events associated with a supply chain and they can disrupt it adversely. So, this is wake up call for supply chain experts and operation managers to think and build a supply chain which have risk bearing capability along with behavior of sustainability in uncertain environment. Figure 1: An outline for risk influencing sustainable supply chains The framework in research concentrates the various risks and disruptive issues concern to environmental, social and economic aspects for an organizational supply chain (Linton, et al., 2007, Elkington, 2004). This framework provides interaction among the triple bottom line issues with risks and disruptive events in supply chains as shown in Figure 1. Further within the context, we define the approach of managing risks in supply chain as the capability of an organization to recognize and supervise the uncertainties related to economical, environment, and green, and societal aspects. 2

3 3. Methodology of research and Interpretative Structural Modeling (ISM) To fulfil the objectives of the study, we required qualitative data which is collected through the literature related to risks management and sustainable supply chains. While interpretive structural modeling approach is used to analyze the interaction between the identified variables, detailed description of ISM is given in next subsection. Furthermore, the major variables involved in this study are also described ahead. We have also used ISM methodology to explore the problem. For this research, authors have concerned to understand the risk bearing capability and sustainability of supply chain. The sustainability and less prone risk supply chain further depend upon a number of factors and variables. A model depicting those key variables and factors that should be focused on such that desired results could be achieved would be of great value to the top management. ISM suits to these circumstances to analyze the interaction among variables. The ISM process simplifies the modeling process and provides visible, well-defined models in end for further implications. Further, (Warfield, 1974) explains ISM as an interactive knowledge procedure which can structure different and related elements and their relationships into a comprehensive model. ISM can be used in identifying and analyzing interactions among the elements of a system. 3.1 Risk influencing SSC variables recognition The ISM methodology helps in understanding the complexity of system and inter-related elements. In this paper various variables in initiating and achieving risk bearing capability and sustainability in supply chain. After review of literature on sustainability, risk, disruptions and uncertainty related issues and the outlook of professionals, both from supply chain and the academe, nine important sustainable focusing variables have been recognized and listed in Table 1 as below: 3.2 Formation of Structural Self-Interaction Matrix (SSIM) A Structural Self-Interaction Matrix is developed on the basis of contextual relationship between recognized variables (see Table 2). This matrix indicates the pair wise relationships among the variables. A detailed description of the symbols used to denote the directions of the relationship between the variables are given as below:- If we consider the variables under learning are i and j, then the representation V indicates that variable i lead toward variable j, the representation A indicates that variable j leads toward 3

4 variable i. The representation X indicates that variable i and j will ease to achieve each other and the representation O indicates that variables are not related to each other. We derived the reachability matrix from the structural self-interaction matrix (SSIM) developed in the previous step. Further, the reachability matrix is constructed from the SSIM using the following rules (see Table 3). Table 1: Risk influencing variables in SSC S.No. Description of variables Sources 1 Corporate Social Responsibility Srivastava (2007); Zhu et al. (2008); 2 Role of government legislations Beamon, (2005) 3 Strategic risk planning and resources Zsidisin et al., (2004), Atkinson, (2006) allocation 4 Visibility, integration and flexibility of supply chains Tang, (2006), Agarwal & Shankar,(2002); Lee, (2004) 5 Commitment of Suppliers Christopher and Peck, (2004); Zhu et al. (2008) 6 Complexion due to network & global changes Atkinson, (2006); Manuj and Mentzer, (2008a); (2008b) 7 Understanding of risks and uncertainties in supply chain Chopra and Sodhi, (2004); Christopher and Lee, (2004); 8 Assessment & measurement of risks Christopher and Peck, (2004); Manuj and Mentzer, (2008) 9 Continuous improvement in the system Chopra and Sodhi, (2004); 3.3 Level Partitioning for reachability matrix The reachability matrix obtained as above was partitioned into different levels. The reachability and antecedent set for each variables (Warfield, 1974) found from the reachability matrix. The developed ISM hierarchy illustrates that variable positioned at a level would not help to access other variable lying above. From Table 3 infers that the continuous improvement in the system (variable 9) to sustain in uncertain conditions of environment is positioned at level 1 st and hence at top of ISM hierarchy. 3.4 Development of final ISM model and MICMAC analysis On the basis of level partition matrix an ISM model of the various variables imperative to understand the concept of risks in SSC is developed, and is shown in Fig. 3. Further, due to positioning at bottom of the hierarchy it can be deduce that SC visibility & mutual transparency Table 2: Representation of Structural self-interaction matrix (SSIM) S.N. Variables

5 1 Corporate Social Responsibility V V V V V A V V 2 Role of government legislations V V V V V A V 3 Strategic risk planning and resources allocation V V V V V A 4 Visibility, integration and flexibility of supply chains V V V V V 5 Commitment of Suppliers V V V V 6 Complexion understanding due to network & global V V V 7 Understanding of risks and uncertainties in supply chain V V 8 Assessment & measurement of risks V 9 Continuous improvement in the system Table 3: reachability matrix S.N. Variables Driving power 1 Corporate Social Responsibility Role of government legislations Strategic risk planning and resources allocation 4 Visibility, integration and flexibility of supply chains 5 Commitment of Suppliers Complexion understanding due to network & global 7 Understanding of risks and uncertainties in supply chain 8 Assessment & measurement of risks 9 Continuous improvement in the system Dependence power (variable 6) is a very significant factor for building the risk bearing capabilities in sustainable supply chains. With help of MICMAC study, we graphically analyzed the driving and dependence power of the variables. On this basis, the variables are categorized into four sectors 5

6 Fig. 3: Final ISM model for the variables associated with RBSSC name as autonomous, dependent, unstable or linkage, and variables/independent. Furthermore, the variables with weak driving power as well as dependence called as autonomous variables. While variables having weak driving but strong dependence power is dependent variables. Fig. 4: A representation of driving and dependence power matrix Similarly other set of variables both strong driving and dependence power known as linkage or unstable variables. These variables are further considered to be highly unstable and sensitive. In addition, the variables having strong driving power but weak dependence power called as independent variables (Diabat and Kannan, 2011; Mangla, et al., 2012). Because, these variables have strong driving power i.e. they strongly affect other sector variables and weak dependence power i.e. didn t much depend on other sector variables. 6

7 4. Discussion and managerial implication In case, proposed model may be used to obtain estimates on system performance for specific characteristics of risk bearing based sustainability focused supply chains. The managerial implication for implementing recovery operations and concluding remarks emerging from this study are as follows:- Ø Commitment of suppliers, Strategic risk planning and resources allocation and Complexion understanding due to network & global variables are categorized as autonomous variables for risk influencing SSC. These sector variables can be given little importance because of their weak driving and dependence power. Ø The variables listed as dependent variables are given as Understanding of risks and uncertainties in supply chain risks, Assessment & measurement of risks, Continuous improvement in the system variables have little driving tendency but strong dependency on all other variables. Ø The independent variables of the risk influencing SSC such as Corporate Social Responsibility, Role of government legislation and Visibility, integration and flexibility of supply chains occupied lower levels in developed ISM model. Because, these variables have strong driving impact power i.e. they strongly affect other sector variables and weak dependence power i.e. didn t much depend on other sector variables. Finally, we discussed the possibilities and scope for further improvements in domain. The ISM methodology is primarily depends on the experience and judgment of the decision makers so need to be carefully used. Structural equation modeling (SEM), a statistics based approach, can be used to test the validity of ISM developed theoretical models. References 1. Atkinson, W., (2006). Hilton s supply chain ready for anything heading into hurricane season, Purchasing, 135 (12), Beamon, B.M., (2005). Environmental and sustainability ethics in supply chain management. Science and Engineering Ethics, 11(2), Christopher, M., and Lee, H., (2004). Mitigating supply chain risk through improved confidence, International Journal of Physical Distribution & Logistics Management, 34(5), Christopher, M., and Peck, H., (2004). Building the resilient supply chain, International Journal of Logistics Management, 15(2), Chopra, S., and Sodhi. M., (2004). Managing risk to avoid supply chain breakdown. Sloan Management Review, 46(1),

8 6. Diabat, A., and Kannan G., (2011). An analysis of the drivers affecting the implementation of green supply chain Management. Resources, Conservation and Recycling, 55, Elkington, J., (2004). Enter the triple bottom line, in Henriques, A. and Richardson, J. (Eds), The Triple Bottom Line: Does It All Add up?, Earthscan, London, Lee, H.L., (2004). A triple-a supply chain, in Harvard Business Review vol. 82 (10), pp Linton, J.D., Jayaraman, V. and Klassen, R. (2007), Sustainable supply chains: an introduction, Journal of Operations Management, 25(6), Mason-Jones, R., and Towill, D.R., (1998). Shrinking the supply chain uncertainty circle, Control, The Institute of Operations Management, 24(7), Manuj, I., and Mentzer, J.T., (2008). Global supply chain risk management, Journal of Business Logistics, 29(1), Ravi, V., and Shankar, R., (2005). Analysis of interactions among the barriers of reverse logistics. International Journal of Technological Forecasting & Social change, 72(8), Sarkis, J., (2001). Manufacturing s role in corporate environmental sustainability, International Journal of Operations & Production Management, 21(5/6), Mangla, S., Madaan, J., and Chan, F.T.S., (2012). Analysis of Performance Focused Variables for Multi-Objective Decision Modeling Approach of Flexible Product Recovery Systems. Springer: Global Journal of Flexible Systems Management, 13(2), Srivastava, K.S., (2007). Green supply-chain management: a state-of-the-art literature review. International Journal of Management Reviews, 9(1): Tang, C Perspectives in supply chain risk management, International Journal of Production Economics, 103(2), Warfield, J. W., (1974). Developing interconnected matrices in structural modeling. IEEE Transaction Systems Man and Cybernetics, 4(1) Wisner, J.D., (2003). A structural equation model of supply chain management strategies and firm performance. Journal of Business Logistics, 24(1), Zhu, Q., Sarkis, J., and Lai, K., (2008). Confirmation of a measurement model for green supply chain management practices implementation. International Journal of Production Economics, 111(2), Zsidisin, G.A., Ellram, L.M., Carter, J.R., and Cavinato, J.L., (2004). An analysis of supply risk assessment techniques, International Journal of Physical Distribution & Logistics Management, 34(5),