Outsourcing analysis for Reverse Logistics systems: a qualitative study and a Markov decision model

Abstract

<p>Reverse Logistics (RL) has recently been considered as an improvement area if it is focused correctly. Every manufacturing, distribution or sales firm, irrespective of its size, types of products or geographic location, can benefit from planning, implementing and controlling RL activities;Given the nature of the RL field, one of the most important decisions to be taken by any firm is whether to outsource such functions or not. This comes from the fact that RL does not represent a core activity for a firm, given that the purpose of any company is not to manage the flow of products taken back from the sale point, but rather to distribute such products to its customers;The suitability of the outsourcing option for a particular RL system is evaluated in this dissertation, under a particular assumption about the behavior of the return volume. To accomplish this goal, a complete qualitative analysis of the current existing RL systems in the U.S. is performed. Return volume variability and product life cycle length are identified as the most important elements that determine the whether outsourcing is likely to occur. A quantitative analysis is also performed by developing a Markov decision model, which allows us to model not only the return process, but also the conditions under which a simple threshold policy is optimal. Such conditions are stated in terms of the cost parameters involved, as well as the return rate for the product considered;The hypothesis that outsourcing is a more suitable option for scenarios with greater variability in the return volume is also supported, both analytically and by studying a set of numerical examples, where it is shown how the threshold for outsourcing decreases while the probability of crossing any fixed threshold increases with the variability in the return volume.</p>