Strategic Grading in the Product Acquisition Process of a Reverse Supply Chain

Most recommerce providers have moved to a quality-dependent process for the acquisition of used products. They acquire the products via websites at which product holders submit upfront quality statements and receive quality-dependent acquisition prices for their used devices. Motivated by this development of reverse logistics practice, the aim of this study is to analyze the product assessment process of a recommerce provider in detail. To this end, we first propose a sequential bargaining model with complete information which captures the individual behavior of the recommerce provider and the product holder. We determine the optimal strategies of the product holder and the recommerce provider in this game. We find that the resulting strategies lead to an efficient allocation, although the recommerce provider can absorb most of the bargaining potential due to his last mover advantage. In a second step, we relax the assumption of complete information and include uncertainty about the product holder's valuation of the product. We show the trade-off underlying the recommerce provider's optimal counteroffer decision and analyze the optimal strategy, using a logistic regression approach on a real-life dataset of nearly 6,000 product submissions. The results reveal a significant improvement potential, compared to the currently applied strategy.