Three Essays on Product Recall Decision Optimization

Abstract

This thesis examines decision optimization of product recalls. Product recalls in recent years have shown unprecedented impact on both immediate economic and reputational damage to the company and long-lasting impact on the brand and industry. Admittedly, imperfect product quality makes recalls inevitable. Thus, we explore from three perspectives to elicit business insights regarding better management and risk control. Chapter 1 introduces the topic of product recall management optimization and its real-world motivation. Chapter 2 views the decision making of "when to initiate a product recall" as a dynamic process and takes the feedback of customer returns to update the product defect rate. Updating is simplified by the conjugate properties of beta distribution and Bernoulli trials. We develop the optimal stopping model to find the thresholds of total product returns above which initiating recall is optimal. We implement dynamic programming to solve the model optimally. For large-size problems, we propose a simulation method to balance computation time with solution quality. Chapter 3 allows the company to control the recall risk by investing in quality. We adopt the one-stage stochastic newsvendor model and add quality-dependent recall risk. The resulting model is not concave in production quantity and quality levels. The parametric analysis reveals several interesting features such as the optimal ordering quantity and quality level have a conflicting relationship. We further extend our model from internal supply to external supply from multiple sources. Chapter 4 examines managing product recalls from the closed-loop supply chain management and disruption management perspectives. We model the location and allocation decisions of both manufacturing plants and reprocessing facilities where facilities are built after the recalls. Numerical experiments show the costs of overlooking potential recalls vary greatly, indicating the necessity of considering recalls in initial designs and the importance of accurate recall probability prediction. Chapter 5 summarizes.