Supply Information Sharing and Updating in Supply Chain Management

This thesis deals with the effect of supply information sharing and updating on different areas of supply chain management including, inventory management, Marketing and behavioral management. It addresses how mathematical modeling can help managers who are concerns about supply uncertainty issues. Although in literature more attention is restricted to demand updating, models developed in this thesis contribute to learning effect on supply availability literature. First, from inventory management point of view, we model a two-product, periodic-review inventory problem in which the probability of supply availability is unknown and there are two different fixed cost assigned to each product. We investigate the effect of supply uncertainty forecasting with Bayesian updating on ordering policy and the behavior of the system. (s,S) policy along with two switching curves are proved to determine the optimal ordering policy. Our results indicates that improving the accuracy of the forecast leads to making a better ordering decision and eliminating the negative effect of supply disruption on the total cost. Second, we study channel coordination and yield risk forecast sharing for additive random yield in a three-level supply chain including a supplier, a manufacturer and a distributor. We investigate whether random yield forecasting is beneficial for the supply chain parties or not. If it is, under which condition and what contract the distributor shares the knowledge of supply risk with the retailer. Finally, we focus on the rule of trust in supply forecast signaling for a supply chain with a supplier and a manufacturer in a one-shot game. We determine the manufacturer’s optimal order quantity for two types of information sharing situation, truthful and untruthful. Moreover, different scenarios of information sharing are compared numerically.