A Markov Chain Model to Maximize Revenue by Varying Refurbished Product Upgrade Levels

Abstract

Refurbishing companies receive used products with varying levels of quality. One challenge facing the remanufacturer is the extent to which upgrading should be conducted. An analytical approach is proposed to evaluate the product recovery system with stochastic variability of the quality of the components/parts returned to the remanufacturing environment. The decision process is formulated via a discrete time Markov chain model. Then, a linear program approach is applied to solve the model to determine up to which level a returned product should be upgraded. The effects of different policies (the level of upgrade) on the total expected value are studied and the optimal decision which maximizes expected average revenue is determined. An example of a copy machine is used to illustrate an application of the model. Finally, the results of the model are further investigated applying a systems dynamics approach to determine the effects of the suggested upgrade strategy on the amount of reusable inventory.