We describe, model, and optimize the inventory in a reverse logistics system that supports the warranty returns and replacement for a consumer electronic device. The context and motivation for this work stem from a collaboration with an industrial partner, a Fortune 100 company that sells consumer electronics. The reverse-logistics system is a closed-loop supply chain: failed devices are returned for repair and refurbishing; this inventory is then used to serve warranty claims or sold through a side-sales channel. Managing inventory in this system is challenging due to the short life-cycle of these devices and the rapidly declining value for the inventory. In the first part of the talk, we characterize the structure of the optimal policy for this inventory problem for both deterministic and stochastic demand. In addition, we introduce two heuristics: (i) a certainty-equivalent approximation, which leads to a simple closed form policy; and (ii) a dual balancing heuristic, which results in a more tractable policy. We also develop a robust version of this model in order to obtain bounds for the overall performance of the system. We test the performance of these heuristics using data from our industrial partner.
The second part of the talk concerns the problem faced by a consumer electronics retailer when matching devices in inventory to customers. More specifically, we analyze a setting where there are two warranties in place: (i) the consumer warranty, offered by the retailer to the consumer, and (ii) the Original Equipment Manufacturer (OEM) warranty, offered by the OEM to the retailer. Both warranties are valid for a limited period (usually 12 months), and once warranties expire, the coverage to replace or repair a faulty device ends. Thus, a customer does not receive a replacement if he/she is out of consumer warranty, and the retailer cannot send the device to the OEM for repairs if it is out of OEM warranty. The retailer would ideally like to have the two warranties for a device being matched, i.e., the customer would have the same time left in his consumer warranty as the device would have left in the OEM warranty. A mismatch between these warranties can incur costs to the retailer beyond the usual processing costs of warranty requests. Namely, since a device can fail multiple times during its lifecycle the replacement device sent to customers that file warranty requests can lead to out-of-OEM-warranty returns. In order to mitigate the number of out-of-OEM-warranty returns, we propose an online algorithm tomatch customers that have filed warranty claims to refurbished devices in inventory. The algorithm matches the oldest devices in inventory to the oldest customers in each period. We characterize the competitive ratio of this algorithm and, through numerical experiments using historical data, demonstrate that it can significantly reduce out of warranty returns compared to our partner's current strategy.
co-author: Stephen C. Graves, Massachusetts Institute of Technology
About the presenter
Andre Calmon is an Assistant Professor of Technology and Operations Management at INSEAD. His current research involves using analytics to address sustainability and efficiency issues in supply chains. He also studies operations management and marketing challenges faced by both the for-profit and the non-profit sectors in emerging markets. Andre has also done Big Data and analytics consulting for multiple companies and organizations in the US and in Brazil. Andre holds a Ph.D. in Operations Research from the Massachusetts Institute of Technology (MIT). He also holds a M.Sc. in Electrical Engineering from the Universidade Estadual de Campinas (Unicamp) and a B.Sc. in Electrical Eng. From the Universidade de Brasília (UnB).
More info about Prof. Andre Calmon
About the Seminar
The KLU research seminar series is a regular meeting of PhD students, Post-Docs and professors who conduct research in the field of logistics and supply chain management. The research seminar is open to the public and we happily welcome guests.