| This thesis is motivated by the impact that information technology has had on supply chain management. In particular, information technology has changed the way companies interact with suppliers and customers. For example, in quick response, suppliers receive Point-of-Sales (POS) data from retailers and use this information to improve their forecast and better manage production and inventory activities.; Our objective is to study the value of information sharing and how to effectively utilize demand related information in supply chains. For this purpose, we develop and analyze two models, the first one focuses on inventory cost reduction in a two-stage supply chain where the manufacturer has a limited production capacity. The second model characterizes the forecast accuracy improvement in a multi-stage supply chain facing stationary and correlated demand.; The thesis starts by analyzing a periodic review, two-stage production-inventory system with a single capacitated manufacturer and a single retailer facing stochastic demand. The manufacturer receives demand information from the retailer even during time periods in which the retailer does not place orders. Assuming a finite time horizon, we characterize the optimal production-inventory policy for the manufacturer, explore the policy structure, and study the optimal frequency and timing in which information should be shared.; We then analyze a similar model in infinite time horizon. First, we provide a new and simple proof for the optimality of the cyclic order-up-to policy under average cost criterion. Then, using Infinitesimal Perturbation Analysis (IPA) we quantify the impact of information sharing, as well as the impact of the frequency and timing of information sharing on the manufacturer's performance.; In the last part of the thesis, we consider a distribution system with a single manufacturer, a single distribution center and multiple non-identical retailers in infinite time horizon. The retailers place orders periodically, the distribution center transfers the aggregated orders from the retailers to the manufacturer. Assuming stationary and correlated external demands, we quantify the impact of sharing the order and demand information of individual retailers on the manufacturer's forecast accuracy. |
