Disposition decisions in reverse logistics by using AHP-fuzzy TOPSIS approach

Abstract

Purpose

The purpose of this paper is to explore the various disposition alternatives and to develop a framework for the optimal disposition decisions in reverse logistics.

Design/methodology/approach

In reverse logistics, once the products are collected and inspected, decision is to be taken regarding their disposition for reuse, re-manufacture or recycle or other possible alternatives. A combination of analytical hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach is proposed for the selection of best disposition alternative based on criteria economic benefits, environmental benefits, corporate social responsibility, stakeholder’s needs and reverse logistics resources.

Findings

A case of electronics firm was illustrated for the demonstration of the approach for the disposition of mobile phones. Returned mobile phones must be disposed for repairing or reuse in current business scenario, if possible. Otherwise, the firm may prefer to recycle them rather than dispose or remanufacture.

Research limitations/implications

The study is limited to mobile manufacturing firm. Also, these findings may vary depending on the sector and products. Further, empirical studies and case studies can be carried out to validate the findings.

Practical implications

The proposed framework provides useful tool to the practitioners and researchers in decision-making for disposition in reverse logistics.

Originality/value

Very few studies related to disposition decisions in reverse logistics were found in the previous research literature review. The study will add value to the very limited research on reverse logistics disposition. Also, the AHP-Fuzzy TOPSIS approach is first time being used for the disposition decisions in reverse logistics.

Citation

Agrawal, S., Singh, R.K. and Murtaza, Q. (2016), "Disposition decisions in reverse logistics by using AHP-fuzzy TOPSIS approach", Journal of Modelling in Management, Vol. 11 No. 4, pp. 932-948. https://doi.org/10.1108/JM2-12-2014-0091