On the effect of quality overestimation in remanufacturing

We study a simple reverse supply chain consisting of a remanufacturing facility and a number of independent locations where used products are returned by the end-users. At the collection locations, the returned products are graded and classified based on a list of nominal quality metrics provided by the remanufacturer. It is assumed that this classification is subject to errors; specifically, the returns condition is overestimated because of a stochastic proportion of returned units which are classified in classes corresponding to better quality than the actual. The scope of the paper is to study how these classification errors affect the optimal procurement decisions of the remanufacturer as well as the associated profit for the cases of both constant and stochastic demand in a single-period context. Moreover, in the former case we study the impact of these classification errors on profit variability. The quantification of the impact of quality overestimation provides intuition on the value of reliable classification and on the extent of the necessary investments and initiatives to improve classification accuracy.