A collaborative decentralized distribution system with demand forecast updates ?
In this paper, we study inventory pooling coalitions within a decentralized distribution system consisting of a manufacturer, a warehouse (or an integration center), and n retailers. At the time their orders are placed, the retailers know their demand distribution but do not know the exact value of the demand. After certain production and transportation lead time elapses, the orders arrive at the warehouse. During this time, the retailers can update their demand forecasts. We first focus on cooperation among the retailers  the retailers coordinate their initial orders and can reallocate their orders in the warehouse after they receive more information about their demand and update their demand forecasts. We study two types of cooperation: forecast sharing and joint forecasting. By using an example, we illustrate how forecast sharing collaboration might worsen performance, and asymmetric forecasting capabilities of the retailers might harm the cooperation. However, this does not happen if the retailers possess symmetric forecasting capabilities or they cooperate by joint forecasting, and the associated cooperative games have non-empty cores. Finally, we analyze the impact that cooperation and non-cooperation of the retailers has on the manufacturers profit. We focus on coordination of the entire supply chain through a three-parameter buyback contract. We show that our three-parameter contract can coordinate the system if the retailers have symmetric margins. Moreover, under such a contract the manufacturer benefits from retailers cooperation since he can get a share of improved performance.
coordination core inventory centralization game theory