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Flexible Decision Modeling for Evaluating the Risks in Green Supply Chain Using Fuzzy AHP and IRP Methodologies

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Abstract

There are several different risks and risk factors involved in managing the green supply chain (GSC) practices successfully. These risks and risk factors have a tendency to disrupt the typical GSC operations, and hence, reduce the success rate. To mitigate the consequences, therefore, a flexible decision modeling which could evaluate the risks in the context of GSC is needed from the industrial viewpoint. The present research work intends to propose a flexible decision model based on combined fuzzy analytic hierarchy process (AHP) and interpretive ranking process (IRP) methodology to evaluate the risks associated with implementation of GSC practices under the fuzzy surroundings. Fuzzy AHP approach estimates the priority or ranking of the identified risks by determining of their relative importance. To analyze the risks ranking obtained through the fuzzy AHP, the methodology of IRP is applied. The methodology of IRP, however, also enables the decision makers to understand the interpretive logic for dominance of one risk over the other for each pair wise comparison. The proposed flexible risk evaluation model is applied to an empirical case of Indian poly plastic manufacturing company. The model proposed in this study offers logical means to understand the significance of different risks in the strategic decision processes. It would help the managers and practitioners to interpret the influence of key strategic actions on the performance and to improve the effectiveness of the processes by building some robust and flexible strategies for managing risks in GSC.

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Acknowledgments

This paper is an extended version of the paper presented in “GLOGIFT-13, IIT- Delhi, India. The authors wish to thanks to Prof. Dr. Sushil, IIT Delhi (Editor-in-chief, JFSM) to provide us an opportunity to extend the paper. We are also grateful to two anonymous reviewers for their constructive comments, which helped us greatly in improving the presentation and quality of this paper. The authors also acknowledge great thanks for the support to the research facilities provided by the Department of Mechanical and Industrial Engineering, in Indian Institute of Technology Roorkee, India.

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Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, Indian Institute of Technology, Roorkee, India

    Sachin K. Mangla & Pradeep Kumar

  2. Department of Management Studies, Indian Institute of Technology, Roorkee, India

    Mukesh Kumar Barua

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  1. Sachin K. Mangla
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  2. Pradeep Kumar
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Correspondence to Sachin K. Mangla.

Appendices

Appendix I

Triangular fuzzy number based pair-wise judgment matrix for GSC risk sub-criteria. See the Tables 14, 15, 16, 17.

Table 14 Triangular fuzzy number based pair-wise judgment matrix for GSC risk sub-criteria under D
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Table 15 Triangular fuzzy number based pair-wise judgment matrix for GSC risk sub-criteria under PR
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Table 16 Triangular fuzzy number based pair-wise judgment matrix for GSC risk sub-criteria under S
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Table 17 Triangular fuzzy number based pair-wise judgment matrix for GSC risk sub-criteria under O
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Appendix II

Excel calculation sheet for determining of the weights of risks criteria.

Appendix III

See the Table 18.

Table 18 Interpretive logic—knowledge base—Ranking of risks w.r.t. Performances
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Mangla, S.K., Kumar, P. & Barua, M.K. Flexible Decision Modeling for Evaluating the Risks in Green Supply Chain Using Fuzzy AHP and IRP Methodologies. Glob J Flex Syst Manag 16, 19–35 (2015). https://doi.org/10.1007/s40171-014-0081-x

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