The goal of this paper is to describe, model, and optimize inventory in a reverse logistics system that supports the warranty returns and replacements 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. We examine an inventory model that captures these dynamics. We characterize the structure of the optimal policy for this problem for stochastic demand and introduce an algorithm to calculate optimal sell-down levels. We also provide a closed-form policy for the deterministic version of the problem, and we use this policy as a certainty-equivalent approximation to the stochastic optimal policy. Finally, using numerical experiments, we analyze the sensitivity of this system to changes in various parameters, and we also evaluate the performance of the certainty-equivalent approximation using data from our industrial partner.

The goal of this paper is to describe, model, and optimize inventory in a reverse logistics system that supports the warranty returns and replacements 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. We examine an inventory model that captures these dynamics. We characterize the structure of the optimal policy for this problem for stochastic demand and introduce an algorithm to calculate optimal sell-down levels. We also provide a closed-form policy for the deterministic version of the problem, and we use this policy as a certainty-equivalent approximation to the stochastic optimal policy. Finally, using numerical experiments, we analyze the sensitivity of this system to changes in various parameters, and we also evaluate the performance of the certainty-equivalent approximation using data from our industrial partner.

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Problem definition: Novel life-improving products, such as solar lanterns and energy-efficient cookstoves address essential needs of consumers in the base of the pyramid (BOP). However, their profitable distribution is often difficult because BOP customers are risk-averse, their ability to pay (ATP) is lower than their willingness to pay, and they face uncertainty regarding these products’ value. Academic/practical relevance: We examine two practical strategies from distributors in the BOP: (1) improving the product’s affordability through a discount and (2) increasing awareness of the product’s value. Our results identify BOP-specific operational trade-offs in implementing these strategies. We also propose strategies to manage these trade-offs that can increase consumer surplus in the BOP. Methodology: We introduce a supply chain model for the BOP and analyze the distributor’s pricing problem with refunds as well as the distributor’s optimal budget allocation between strategies (1) and (2). Results: We find that, in the BOP, the distributor’s profit-maximizing budget allocation often yields the lowest consumer surplus. This misalignment between profits and consumer surplus disappears if customers’ ATP is high. Moreover, the misalignment can be resolved if the distributor offers free product returns and commits to a maximum retail price. We confirm the robustness of our results through numerical simulations. Managerial implications: Best operations strategy practices in the BOP can differ significantly from developed markets. Furthermore, BOP customers’ limited ATP and high risk aversion generate a BOP-specific misalignment between profits and consumer surplus. Operational commitments, such as free returns, reduce this misalignment and can serve as a signal to investors of a social enterprise’s focus on consumer surplus.

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Problem definition: We examine a dynamic assignment problem faced by a large wireless service provider (WSP) that is a Fortune 100 company. This company manages two warranties: (i) a customer warranty that the WSP offers its customers and (ii) an original equipment manufacturer (OEM) warranty that OEMs offer the WSP. The WSP uses devices refurbished by the OEM as replacement devices, and hence their warranty operation is a closed-loop supply chain. Depending on the assignment the WSP uses, the customer and OEM warranties might become misaligned for customer-device pairs, potentially incurring a cost for the WSP. Academic/practical relevance: We identify, model, and analyze a new dynamic assignment problem that emerges in this setting called the warranty matching problem. We introduce a new class of policies, called farsighted policies, which can perform better than myopic policies. We also propose a new heuristic assignment policy, the sampling policy, which leads to a near-optimal assignment. Our model and results are motivated by a real-world problem, and our theory-guided assignment policies can be used in practice; we validate our results using data from our industrial partner. Methodology: We formulate the problem of dynamically assigning devices to customers as a discrete-time stochastic dynamic programming problem. Because this problem suffers from the curse of dimensionality, we propose and analyze a set of reasonable classes of assignment policies. Results: The performance metric that we use for a given assignment policy is the average time that a replacement device under a customer warranty is uncovered by an OEM warranty. We show that our assignment policies reduce the average uncovered time and the expected number of out-of-OEM-warranty returns by more than 75% in comparison with our industrial partner’s current assignment policy. We also provide distribution-free bounds for the performance of a myopic assignment policy and of random assignment, which is a proxy for the WSP’s current policy. Managerial implications: Our results indicate that, in closed-loop supply chains, being completely farsighted might be better than being completely myopic. Also, policies that are effective in balancing short-term and long-term costs can be simple and effective, as illustrated by our sampling policy. We describe how the performance of myopic and farsighted policies depend on the size and length of inventory buildup.

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