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Development of an interpretive structural model of drivers for reverse logistics implementation in Indian industry

Author

Listed:
  • Anil Jindal
  • Kuldip Singh Sangwan

Abstract

Reverse logistics has gained growing attention among supply chain management researchers and practitioners because of many diverse issues ranging from global warming to waste disposal. It is important for the decision makers to understand the various factors which drive the implementation of reverse logistics in a given business environment. Eleven economical, environmental and social drivers of reverse logistics have been identified. A hierarchical relationship model is developed among these eleven drivers by using interpretive structural modelling technique. It is expected that the understanding of the relationships will help the decision makers in industry and government to strategically leverage the drivers for easy and effective implementation of reverse logistics.

Suggested Citation

  • Anil Jindal & Kuldip Singh Sangwan, 2013. "Development of an interpretive structural model of drivers for reverse logistics implementation in Indian industry," International Journal of Business Performance and Supply Chain Modelling, Inderscience Enterprises Ltd, vol. 5(4), pages 325-342.
    • Handle: RePEc:ids:ijbpsc:v:5:y:2013:i:4:p:325-342
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    Citations

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    Cited by:

    1. Srie Vidhya Janani, E. & Ganesh Kumar, P., 2015. "Evaluating the technical barriers of large scale sustainable wireless sensor network: A resources approach," Resources Policy, Elsevier, vol. 46(P1), pages 134-141.
    2. Deepak Lamba & Devendra K. Yadav & Akhilesh Barve & Ganapati Panda, 2020. "Prioritizing barriers in reverse logistics of E-commerce supply chain using fuzzy-analytic hierarchy process," Electronic Commerce Research, Springer, vol. 20(2), pages 381-403, June.
    3. Kamble, Sachin S. & Gunasekaran, Angappa & Parekh, Harsh & Joshi, Sudhanshu, 2019. "Modeling the internet of things adoption barriers in food retail supply chains," Journal of Retailing and Consumer Services, Elsevier, vol. 48(C), pages 154-168.
    4. Jia, Peng & Diabat, Ali & Mathiyazhagan, K., 2015. "Analyzing the SSCM practices in the mining and mineral industry by ISM approach," Resources Policy, Elsevier, vol. 46(P1), pages 76-85.
    5. Luthra, Sunil & Mangla, Sachin Kumar & Xu, Lei & Diabat, Ali, 2016. "Using AHP to evaluate barriers in adopting sustainable consumption and production initiatives in a supply chain," International Journal of Production Economics, Elsevier, vol. 181(PB), pages 342-349.
    6. Anil Jindal & Kuldip Singh Sangwan, 2017. "Multi-objective fuzzy mathematical modelling of closed-loop supply chain considering economical and environmental factors," Annals of Operations Research, Springer, vol. 257(1), pages 95-120, October.
    7. Lixin Shen & Kannan Govindan & Madan Shankar, 2015. "Evaluation of Barriers of Corporate Social Responsibility Using an Analytical Hierarchy Process under a Fuzzy Environment—A Textile Case," Sustainability, MDPI, vol. 7(3), pages 1-22, March.
    8. Agrawal, Saurabh & Singh, Rajesh K. & Murtaza, Qasim, 2015. "A literature review and perspectives in reverse logistics," Resources, Conservation & Recycling, Elsevier, vol. 97(C), pages 76-92.

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