Advertisement

Prioritizing barriers in reverse logistics of E-commerce supply chain using fuzzy-analytic hierarchy process

  • Deepak Lamba
  • Devendra K. Yadav
  • Akhilesh BarveEmail author
  • Ganapati Panda
Article
  • 24 Downloads

Abstract

Several factors like returns, undelivered and damaged goods, exchange, and environmental concern make reverse logistics (RL) inevitable in an E-commerce supply chain. In spite of understanding the importance of RL in current business scenario, most of the companies concentrate on forward logistics, while reverse flow from customer to upstream business is not receiving much interest. Considering this less focused aspect of reverse logistics, the objective of this research is to identify and propose a model to rank the inhibiting variables i.e. the barriers so the logistician can solve them as per the priority. The extensive literature survey and experts’ opinion helped in identifying 16 barriers for the study. But the availability of a number of barriers makes evaluating and selecting the most important RL barrier a challenging task and thus it can be dealt with as a multi-criteria decision-making problem. In this paper, a methodology based on analytic hierarchy process has been used to prioritize the barriers of RL. Findings of this paper show that the lack of investment in reverse logistics, lack of understanding about best practices and uncertain return and demand are the three topmost barriers for RL of E-commerce companies.

Keywords

Reverse logistics E-commerce supply chain Fuzzy analytical hierarchy process Reverse logistics barriers 

Notes

References

  1. 1.
    Mukhopadhyay, S. K., & Setoputro, R. (2004). Reverse logistics in e-business: Optimal price and return policy. International Journal of Physical Distribution & Logistics Management, 34(1), 70–89.CrossRefGoogle Scholar
  2. 2.
    Trebilcock, B. (2002). Return to sender. Warehousing Management, 9, 24–27.Google Scholar
  3. 3.
    Tibben-Lembke, R. S., & Rogers, D. S. (2002). Differences between forward and reverse logistics in a retail environment. Supply Chain Management: An International Journal, 7(5), 271–282.CrossRefGoogle Scholar
  4. 4.
    Odedra-Straub, M. O. S. M. (2003). E-commerce and development: Whose development? The Electronic Journal of Information Systems in Developing Countries, 11, 1–5.CrossRefGoogle Scholar
  5. 5.
    Prakash, C., Barua, M. K., & Pandya, K. V. (2015). Barriers analysis for reverse logistics implementation in Indian electronics industry using fuzzy analytic hierarchy process. Procedia-Social and Behavioral Sciences, 189, 91–102.CrossRefGoogle Scholar
  6. 6.
    Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98.CrossRefGoogle Scholar
  7. 7.
    Özdağoğlu, A., & Özdağoğlu, G. (2007). Comparison of AHP and fuzzy AHP for the multi-criteria decision making processes with linguistic evaluations. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 6(11), 65–85.Google Scholar
  8. 8.
    Kwong, C. K., & Bai, H. (2003). Determining the importance weights for the customer requirements in QFD using a fuzzy AHP with an extent analysis approach. IIE Transactions, 35(7), 619–626.CrossRefGoogle Scholar
  9. 9.
    Custora, R., & Buckley, J. J. (2001). Fuzzy hierarchical analysis: The lambda max method. Fuzzy Sets and Systems, 120, 181–195.CrossRefGoogle Scholar
  10. 10.
    Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233–247.CrossRefGoogle Scholar
  11. 11.
    Wang, Y. M., & Chin, K. S. (2006). An eigenvector method for generating normalized interval and fuzzy weights. Applied Mathematics and Computation, 181(2), 1257–1275.CrossRefGoogle Scholar
  12. 12.
    Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655.CrossRefGoogle Scholar
  13. 13.
    Lee, S. H. (2010). Using fuzzy AHP to develop intellectual capital evaluation model for assessing their performance contribution in a university. Expert Systems with Applications, 37(7), 4941–4947.CrossRefGoogle Scholar
  14. 14.
    Park, S. H., & Ungson, G. R. (2001). Interfirm rivalry and managerial complexity: A conceptual framework of alliance failure. Organization Science, 12(1), 37–53.CrossRefGoogle Scholar
  15. 15.
    Fawcett, S. E., Magnan, G. M., & McCarter, M. W. (2008). Benefits, barriers, and bridges to effective supply chain management. Supply Chain Management: An International Journal, 13(1), 35–48.CrossRefGoogle Scholar
  16. 16.
    Jindal, A., & Sangwan, K. S. (2011). Development of an interpretive structural model of barriers to reverse logistics implementation in Indian industry. In Glocalized solutions for sustainability in manufacturing (pp. 448–453). Springer, Berlin.Google Scholar
  17. 17.
    Thiyagarajan, G., & Ali, S. (2016). Analysis of reverse logistics implementation barriers in online retail industry. Indian Journal of Science and Technology, 9(19), 1–6.CrossRefGoogle Scholar
  18. 18.
    Rogers, D. S., & Tibben-Lembke, R. (2001). An examination of reverse logistics practices. Journal of Business Logistics, 22(2), 129–148.CrossRefGoogle Scholar
  19. 19.
    Ravi, V., & Shankar, R. (2005). Analysis of interactions among the barriers of reverse logistics. Technological Forecasting and Social Change, 72(8), 1011–1029.CrossRefGoogle Scholar
  20. 20.
    Jindal, A., & Sangwan, K. S. (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, 5(4), 325–342.CrossRefGoogle Scholar
  21. 21.
    Chung, S. S., & Zhang, C. (2011). An evaluation of legislative measures on electrical and electronic waste in the People’s Republic of China. Waste Management, 31(12), 2638–2646.CrossRefGoogle Scholar
  22. 22.
    Li, X., & Olorunniwo, F. (2008). An exploration of reverse logistics practices in three companies. Supply Chain Management: An International Journal, 13(5), 381–386.CrossRefGoogle Scholar
  23. 23.
    Thierry, M., Salomon, M., Van Nunen, J., & Van Wassenhove, L. (1995). Strategie issues in product recovery management. California Management Review, 37(2), 114–135.CrossRefGoogle Scholar
  24. 24.
    Patil, S. K., & Kant, R. (2014). A fuzzy AHP-TOPSIS framework for ranking the solutions of knowledge management adoption in supply chain to overcome its barriers. Expert Systems with Applications, 41(2), 679–693.CrossRefGoogle Scholar
  25. 25.
    Luthra, S., Kumar, V., Kumar, S., & Haleem, A. (2011). Barriers to implement green supply chain management in automobile industry using interpretive structural modeling technique: An Indian perspective. Journal of Industrial Engineering and Management, 4(2), 231–257.CrossRefGoogle Scholar
  26. 26.
    Zhou, L., Naim, M. M., & Wang, Y. (2007). Soft systems analysis of reverse logistics battery recycling in China. International Journal of Logistics: Research and Applications, 10(1), 57–70.CrossRefGoogle Scholar
  27. 27.
    Dekker, R., Fleischmann, M., Inderfurth, K., & Van Wassenhove, L. N. (2005). Reverse logistics: Quantitative models for closed-loop supply chains. Berlin: Springer.Google Scholar
  28. 28.
    Guide, V. D. R., Jr., & Van Wassenhove, L. N. (2009). OR FORUM: The evolution of closed-loop supply chain research. Operations Research, 57(1), 10–18.CrossRefGoogle Scholar
  29. 29.
    Guide, V. D. R., & Srivastava, R. (1997). Repairable inventory theory: Models and applications. European Journal of Operational Research, 102(1), 1–20.CrossRefGoogle Scholar
  30. 30.
    Pokharel, S., & Mutha, A. (2009). Perspectives in reverse logistics: A review. Resources, Conservation and Recycling, 53(4), 175–182.CrossRefGoogle Scholar
  31. 31.
    Srivastava, S. K. (2008). Network design for reverse logistics. Omega, 36(4), 535–548.CrossRefGoogle Scholar
  32. 32.
    Rahman, S., & Subramanian, N. (2012). Factors for implementing end-of-life computer recycling operations in reverse supply chains. International Journal of Production Economics, 140(1), 239–248.CrossRefGoogle Scholar
  33. 33.
    Chaabane, A., Ramudhin, A., & Paquet, M. (2012). Design of sustainable supply chains under the emission trading scheme. International Journal of Production Economics, 135(1), 37–49.CrossRefGoogle Scholar
  34. 34.
    Abdulrahman, M. D., Gunasekaran, A., & Subramanian, N. (2014). Critical barriers in implementing reverse logistics in the Chinese manufacturing sectors. International Journal of Production Economics, 147, 460–471.CrossRefGoogle Scholar
  35. 35.
    Wath, S. B., Vaidya, A. N., Dutt, P. S., & Chakrabarti, T. (2010). A roadmap for development of sustainable E-waste management system in India. Science of the Total Environment, 409(1), 19–32.CrossRefGoogle Scholar
  36. 36.
    Wong, C. Y., Boon-Itt, S., & Wong, C. W. (2011). The contingency effects of environmental uncertainty on the relationship between supply chain integration and operational performance. Journal of Operations Management, 29(6), 604–615.CrossRefGoogle Scholar
  37. 37.
    Gunasekaran, A., & Ngai, E. W. (2004). Information systems in supply chain integration and management. European Journal of Operational Research, 159(2), 269–295.CrossRefGoogle Scholar
  38. 38.
    Jack, E. P., Powers, T. L., & Skinner, L. (2010). Reverse logistics capabilities: antecedents and cost savings. International Journal of Physical Distribution & Logistics Management, 40(3), 228–246.CrossRefGoogle Scholar
  39. 39.
    Liou, J. J., Wang, H. S., Hsu, C. C., & Yin, S. L. (2011). A hybrid model for selection of an outsourcing provider. Applied Mathematical Modelling, 35(10), 5121–5133.CrossRefGoogle Scholar
  40. 40.
    Wang, Y. M., Luo, Y., & Hua, Z. (2008). On the extent analysis method for fuzzy AHP and its applications. European Journal of Operational Research, 186(2), 735–747.CrossRefGoogle Scholar
  41. 41.
    Wang, Y. M., & Elhag, T. M. (2006). On the normalization of interval and fuzzy weights. Fuzzy Sets and Systems, 157(18), 2456–2471.CrossRefGoogle Scholar
  42. 42.
    Yager, R. R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information Sciences, 24(2), 143–161.CrossRefGoogle Scholar
  43. 43.
    Kaufman, A., & Gupta, M. M. (1991). Introduction to fuzzy arithmetic. New York: Van Nostrand Reinhold Company.Google Scholar
  44. 44.
    Armando, C., Roberta, C., & Tamara, M. (2013). Using fuzzy AHP to manage intellectual capital assets: An application to the ICT service industry. Expert Systems with Applications, 40, 3747–3755.CrossRefGoogle Scholar
  45. 45.
    Cheng, C. H., Yang, K. L., & Hwang, C. L. (1999). Evaluating attack helicopters by AHP based on linguistic variable weight. European Journal of Operational Research, 116(2), 423–435.CrossRefGoogle Scholar
  46. 46.
    Ruiz-Padillo, A., Ruiz, D. P., Torija, A. J., & Ramos-Ridao, Á. (2016). Selection of suitable alternatives to reduce the environmental impact of road traffic noise using a fuzzy multi-criteria decision model. Environmental Impact Assessment Review, 61, 8–18.CrossRefGoogle Scholar
  47. 47.
    Calabrese, A., Costa, R., & Menichini, T. (2013). Using fuzzy AHP to manage intellectual capital assets: An application to the ICT service industry. Expert Systems with Applications, 40(9), 3747–3755.CrossRefGoogle Scholar
  48. 48.
    Chan, F. T., Kumar, N., Tiwari, M. K., Lau, H. C., & Choy, K. L. (2008). Global supplier selection: A fuzzy-AHP approach. International Journal of Production Research, 46(14), 3825–3857.CrossRefGoogle Scholar
  49. 49.
    Chen, S., & Fan, J. (2011). Measuring corporate social responsibility based on a fuzzy analytical hierarchy process. International Journal of Computer Network and Information Security, 3(5), 13–22.CrossRefGoogle Scholar
  50. 50.
    Calabrese, A., Costa, R., Levialdi, N., & Menichini, T. (2016). A fuzzy analytic hierarchy process method to support materiality assessment in sustainability reporting. Journal of Cleaner Production, 121, 248–264.CrossRefGoogle Scholar
  51. 51.
    Kabra, G., Ramesh, A., & Arshinder, K. (2015). Identification and prioritization of coordination barriers in humanitarian supply chain management. International Journal of Disaster Risk Reduction, 13, 128–138.CrossRefGoogle Scholar
  52. 52.
    Chen, J. F., Hsieh, H. N., & Do, Q. H. (2015). Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach. Applied Soft Computing, 28, 100–108.CrossRefGoogle Scholar
  53. 53.
    Fleischmann, M., Bloemhof-Ruwaard, J., Dekker, R., Van der Laan, E., van Nunen, J., & Van Wassenhove, L. (1997). Quantitative models for reverse logistics. European Journal of Operational Research, 103, 1–17.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Deepak Lamba
    • 1
  • Devendra K. Yadav
    • 1
  • Akhilesh Barve
    • 2
    Email author
  • Ganapati Panda
    • 3
  1. 1.School of Mechanical SciencesIndian Institute of Technology BhubaneswarBhubaneswarIndia
  2. 2.Centre for Trade Facilitation and LogisticsIndian Institute of Foreign Trade, New DelhiNew DelhiIndia
  3. 3.C. V. Raman College of Engineering BhubaneswarBhubaneswarIndia

Personalised recommendations