Collaborative Research: Optimal Sampling Plans in Supply Chains with Endogenous Product Quality

The objective of this proposed research is to study both the physical and economic incentive functionalities of sampling plans in an outsourcing environment, in which suppliers may lack incentive to improve product quality. Inspection has been applied widely in practice as a useful quality control tool to catch defects and assure product quality. However, in an outsourcing environment, inspection can not only serve as a preventive tool that catches defects, but also as a screening device that offers economic incentive for suppliers to improve parts quality. This research adopts game and industrial organization theories to capture the interest conflicts among different parties in a supply chain or network, and uses statistical theory to model product quality correlation and detection across product units/batches. This research has three goals: (1) to examine the performance of existing inspection policies (e.g., variables/attribute/multi-stage acceptance sampling plans) in an outsourcing environment; (2) to derive optimal sampling plans that not only balance the tradeoffs among various costs but also provide sufficient economic incentive for suppliers to improve parts quality; and (3) to integrate inspection tools with operational strategies (e.g. product design) to effectively manage product quality in supplier networks. If successful, the result of this research will lead to new sampling plans and quality control strategies for supply chains that face high quality risks. Practitioners can utilize these new sampling plans to achieve their desired quality level in an outsourcing environment. The results of this research are expected to not only extend the inspection theory but also to provide a linkage between the quality control field and the supply chain management field. Research efforts and results will be disseminated through publications, conferences and classroom instruction.