A modelling framework for the acquisition and remanufacturing of used products

Abstract

In recent times, there has been a consistent need for companies to produce ‘green’ products and offer ‘green’ services in order to contribute to environmental protection. The utilisation of used devices (extending their useful life cycle) is an excellent, indirect way for companies to conform to this requirement and, at the same time, increase their profit. Cell phones constitute one of the most interesting cases of products, which can be returned, remanufactured and reused: their replacement rate is large, the available quantity for reuse is huge and, consequently, the profit potential is significant. Motivated by the real case of a company involved in the acquisition and remanufacturing of used cell phones, a simple mathematical programming model is proposed in this work that can help remanufacturing companies to make optimal decisions concerning the quantities to be purchased and remanufactured. Its use, namely the simulation of the model stochastic parameters and the optimisation of the model, reveals not only that the exploitation of used products can be profitable, but also that as the ‘product acquisition system’ improves, the economic benefits for any remanufacturing company can be even greater.

Keywords:

• reverse logistics
• remanufacturing
• mixed integer programming
• cell phones/mobile phones
• product acquisition

Acknowledgements

The author would like to thank Professor Luk van Wassenhove for all his support and advice during the conduct of the present research, as well as Professors George Tagaras and Dan Guide for their constructive criticism of this manuscript. The author is also grateful to Dr George Nenes for his contribution to the computer programming tasks of this research. Finally, the State Scholarships Foundation of Greece (‘IKY’) is acknowledged for its financial support in the form of a post-doctoral scholarship.

Notes

^1. Table 1 includes a short version of the description of the various quality levels, presented initially by Guide and van Wassenhove (2001).

^2. q _i could be potentially deemed as a decision variable.