Incentives in Supply Chain Management

My dissertation focuses on strategic issues in supply chain management. It consists of three studies. In the first chapter, I investigate the incentive issues in supply chain information sharing. Specifically, I provide an explanation for a long-standing question in supply chain management: why do supply chain firms often use simple contracts when our models typically show that more complex forms are needed to coordinate the supply chain? Using a stylized supply chain model consisting of a retailer who solicits forecast information from a manufacturer, I show that if there is sufficient operational flexibility in the supply chain, credible information sharing is indeed possible in a simple contracting environment. The second chapter focuses on the structure of supply chains in a competitive environment. It is motivated by Ford's support of the government bailout of its competitors General Motors and Chrysler. This is in sharp contrast with the general presumption that firms prefer fewer to more competitors. Using a stylized supply chain structure, I sort out conditions where firms have self-interest in promoting the sustainability of their competitors. To get some sense of whether the model can be a credible explanation for Ford's support of the bailout, I calibrate it to pre- and post-recession data. The calibrated model suggests that the bailout of GM and Chrysler actually benefitted Ford. In the third chapter, I present the Almost Robust Optimization model that is a simple and tractable methodology for incorporating data uncertainty into supply chain models. The model trades off the objective function value with robustness and finds optimal solutions that are almost robust. To efficiently solve the model, a novel decomposition approach is proposed that decomposes the model into a deterministic master problem and a subproblem which checks the master problem solution under different realizations. Finally, to demonstrate the efficiency of the methodology, I apply it to two important logistics problems.Ph.D