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Fuzzy MCDM Model for Analysis of Critical Success Factors for Sustainable Collaboration with Third Party Reverse Logistics Providers

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Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1393))

Abstract

Reverse Logistics (RL) has become an important factor for manufacturing industries to gain competitive edge, address environmental concerns and enhance supply chain sustainability. Due to lack of expertise, financial support and resources, manufacturers prefer outsourcing all activities related to the return flow of products to Third Party Reverse Logistics Providers (3PRLPs). However, in the current scenario, with regard to being truly sustainable, manufacturers are considering possibilities of collaboration with the 3PRLPs in order to reap benefits of better customer satisfaction, reduced environmental impact and generation of additional revenue. Thus, it becomes essential to analyze plausible Critical Success Factors (CSFs) which can aid in realizing sustainable collaboration with 3PRLPs. With this perspective, the present study seeks to recognize and examine CSFs related to the implementation of RL under sustainable collaboration with 3PRLPs. Further it attempts to develop an analytical structural model to study the contextual relationship among the identified CSFs and determine the most influential CSFs with the aid of fuzzy Interpretive Structural Modeling (ISM). The results obtained from fuzzy-ISM are further used as an input to fuzzy Matrix Impact of Cross-Multiplication Applied to Classification (MICMAC) analysis to classify the listed factors into four groups on the basis of their driving and dependence power. The uncertainty in the decision making environment is handled effectively using fuzzy set theory. The analytical model developed in the study yields a practical tool for the supply chain managers to centre focus primarily on the CSFs which exert the maximum dominance, to ensure effective implementation of RL under sustainable collaboration.

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References

  1. Almeida CMVB, Bonilla SH, Giannetti BF, Huisingh D (2013) Cleaner production initiatives and challenges for a sustainable world: an introduction to this special volume

    Google Scholar 

  2. Gandhi S, Mangla SK, Kumar P, Kumar D (2015) Evaluating factors in implementation of successful green supply chain management using DEMATEL: a case study. Int Strat Manag Rev 3(1–2):96–109

    Article  Google Scholar 

  3. Nikolaou IE, Evangelinos KI, Allan S (2013) A reverse logistics social responsibility evaluation framework based on the triple bottom line approach. J Clean Prod 56:173–184

    Article  Google Scholar 

  4. Govindan K, Soleimani H (2017) A review of reverse logistics and closed-loop supply chains: a journal of cleaner production focus. J Clean Prod 142:371–384

    Article  Google Scholar 

  5. Govindan K, Kadziński M, Ehling R, Miebs G (2019) Selection of a sustainable third-party reverse logistics provider based on the robustness analysis of an outranking graph kernel conducted with ELECTRE I and SMAA. Omega 85:1–15

    Article  Google Scholar 

  6. Mangla S, Madaan J, Chan FT (2013) Analysis of flexible decision strategies for sustainability-focused green product recovery system. Int J Prod Res 51(11):3428–3442

    Article  Google Scholar 

  7. Starostka-Patyk M, Zawada M, Pabian A, Szajt M (2014) Reverse logistics barriers in Polish enterprises. Int J Serv Oper Manag 19(2):250–264

    Google Scholar 

  8. Mangla SK, Govindan K, Luthra S (2016) Critical success factors for reverse logistics in Indian industries: a structural model. J Clean Prod 129:608–621

    Article  Google Scholar 

  9. Bouzon M, Govindan K, Rodriguez CMT, Campos LM (2016) Identification and analysis of reverse logistics barriers using fuzzy Delphi method and AHP. Res Conserv Recycl 108:182–197

    Google Scholar 

  10. Zou W, Kumaraswamy M, Chung J, Wong J (2014) Identifying the critical success factors for relationship management in PPP projects. Int J Proj Manag 32(2):265–274

    Article  Google Scholar 

  11. Routroy S, Pradhan SK (2013) Evaluating the critical success factors of supplier development: a case study. Benchmarking Int J

    Google Scholar 

  12. Aquilani B, Silvestri C, Ruggieri A, Gatti C (2017) A systematic literature review on total quality management critical success factors and the identification of new avenues of research. TQM J

    Google Scholar 

  13. Sharma SK, Panda BN, Mahapatra SS, Sahu S (2011) Analysis of barriers for reverse logistics: an Indian perspective. Int J Mod Optim 1(2):101

    Google Scholar 

  14. Aguezzoul A (2014) Third-party logistics selection problem: a literature review on criteria and methods. Omega 49:69–78

    Article  Google Scholar 

  15. Panjehfouladgaran H, Shirouyehzad H (2018) Classification of critical success factors for reverse logistics implementation based on importance-performance analysis. Int J Product Qual Manag 25(2):139–150

    Google Scholar 

  16. Luthra S, Mangla SK, Shankar R, Prakash Garg C, Jakhar S (2018) Modelling critical success factors for sustainability initiatives in supply chains in Indian context using Grey-DEMATEL. Prod Plan Control 29(9):705–728

    Article  Google Scholar 

  17. Bai C, Sarkis J (2012) Supply-chain performance-measurement system management using neighbourhood rough sets. Int J Prod Res 50(9):2484–2500

    Article  Google Scholar 

  18. Subramanian N, Gunasekaran A, Abdulrahman M, Liu C (2014) Factors for implementing end-of-life product reverse logistics in the Chinese manufacturing sector. Int J Sustain Dev World Ecol 21(3):235–245

    Google Scholar 

  19. Abdulrahman MD, Gunasekaran A, Subramanian N (2014) Critical barriers in implementing reverse logistics in the Chinese manufacturing sectors. Int J Prod Econ 147:460–471

    Article  Google Scholar 

  20. Ansari ZN, Kant R, Shankar R (2019) Prioritizing the performance outcomes due to adoption of critical success factors of supply chain remanufacturing. J Clean Prod 212:779–799

    Article  Google Scholar 

  21. Shaik M, Abdul‐Kader W (2012) Performance measurement of reverse logistics enterprise: a comprehensive and integrated approach. Meas Bus Excell

    Google Scholar 

  22. Chiou CY, Chen HC, Yu CT, Yeh CY (2012) Consideration factors of reverse logistics implementation-a case study of Taiwan’s electronics industry. Proced Soc Behav Sci 40:375–381

    Google Scholar 

  23. Knemeyer AM, Ponzurick TG, Logar CM (2002) A qualitative examination of factors affecting reverse logistics systems for end‐of‐life computers. Int J Phys Distrib Logist Manag

    Google Scholar 

  24. Akdoğan MŞ, Coşkun A (2012) Drivers of reverse logistics activities: an empirical investigation. Proced Soc Behav Sci 58:1640–1649

    Google Scholar 

  25. Vasanthakumar C, Vinodh S, Ramesh K (2016) Application of interpretive structural modelling for analysis of factors influencing lean remanufacturing practices. Int J Prod Res 54(24):7439–7452

    Article  Google Scholar 

  26. Gavareshki MHK, Hosseini SJ, Khajezadeh M (2017) A case study of green supplier selection method using an integrated ISM-fuzzy MICMAC analysis and multi-criteria decision making. Ind Eng Manag Syst 16(4):562–573

    Google Scholar 

  27. Chen CT, Lin CT, Huang SF (2006) A fuzzy approach for supplier evaluation and selection in supply chain management. Int J Prod Eco 102(2):289–301

    Article  Google Scholar 

  28. Jayant A, Azhar M (2014) Analysis of the barriers for implementing green supply chain management (GSCM) practices: an interpretive structural modeling (ISM) approach. Proced Eng 97:2157–2166

    Article  Google Scholar 

  29. Lee TY, Lee SY, Lee HM (2001) A note on Evaluating the rate of aggregative risk in software development using fuzzy sets theory. In: Sixth Asia-Pacific Decision Sciences (APDSI) conference, Singapore

    Google Scholar 

  30. Wierman MJ (1997) Central values of fuzzy numbers—defuzzification. Inf Sci 100(1–4):207–215

    Article  MathSciNet  Google Scholar 

  31. Duperrin JC, Godet M (1973) Méthode de hiérarchisation des éléments d’un système: essai de prospective du système de l’énergie nucléaire dans son contexte societal

    Google Scholar 

  32. Bhosale VA, Kant R (2016) An integrated ISM fuzzy MICMAC approach for modelling the supply chain knowledge flow enablers. Int J Prod Res 54(24):7374–7399

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Operational Research, University of Delhi, Delhi, India

    Shiwani Sharma

  2. Department of Mathematics, Lady Shri Ram College, University of Delhi, Delhi, India

    Jyoti Dhingra Darbari

Authors
  1. Shiwani Sharma
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  2. Jyoti Dhingra Darbari
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Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Aruna Tiwari

  2. Computer Science and Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Kapil Ahuja

  3. Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Anupam Yadav

  4. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  5. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

  6. School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

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© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Sharma, S., Darbari, J.D. (2021). Fuzzy MCDM Model for Analysis of Critical Success Factors for Sustainable Collaboration with Third Party Reverse Logistics Providers. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1393. Springer, Singapore. https://doi.org/10.1007/978-981-16-2712-5_51

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