Retailer Behavior In Information-Enabled Supply Chains: Models, Analysis, And Impact

This dissertation focuses on how an information-enabled (i.e. there is free flow of demand information from the retailer to the supplier) supply chain utilizes the information about retailer behavior. By understanding, quantifying, and incorporating retailer behavior into the supplier's decision making process, we can significantly improve supplier performance and in some cases, the total supply chain performance as well. The three chapters in this dissertation each deal with a different aspect of retailer behavior and thus result in models that are unique. In each case, a rigorous mathematical analysis coupled with an extensive numerical study enables us to characterize useful managerial insights. The first chapter analyzes a supplier's inventory-control mechanism and its resulting impact on total supply chain cost using knowledge of the retailer inventory policy and the availability of real-time demand information. When the retailer uses the locally optimal (s,S) policy, there is randomness in order time and order quantity to the supplier whereas the supplier sees randomness only in order quantity for the locally suboptimal (R,T) policy and only in order time for another locally suboptimal (Q,r) policy. We find that the suboptimal policies perform better in most cases from the total supply chain perspective. The second chapter examines when errors occur during a retailer's information processing. By incorporating knowledge about the presence of these additive errors into an information-sharing model, we analyze how they affect the supply-chain cost. We observe that the detrimental impact of errors outweighs the beneficial impact of information sharing when the variance of errors exceeds the variance of end-customer demands. We further present an analytical model for determining the optimal level of investment to reduce information errors. The third chapter studies how a supplier, with retailers behaving as human newsvendors, can reduce inventory costs by quantifying and incorporating the retailers' behavioral tendencies such as mean-anchoring and/or demand-chasing into the decision making processes. We develop mathematical models to estimate each retailer's order quantity in the presence of these behavioral tendencies. We observe that the supplier's inventory costs can be reduced significantly by considering this aspect of retailer behavior.