Inventory Management with Advance Supply Information

Recently, supply chain collaboration received substantial attention from both practitioners and academics. In inventory management, there are two major sources of uncertainty: demand and supply uncertainty. Even though supply chain implementations in industry put equal emphasis on both, the interest on collaboration regarding the supply side has been relatively lower in academic literature. In this paper, we consider a manufacturer who gets "advance supply information" from its supplier in the form of capacity availability forecasts for a given rolling information horizon. In each period, an updated capacity forecast vector is provided by the supplier. We model the evolution of the capacity availability forecasts via the Martingale Method of Forecast Evolution (MMFE). We analyze such a system with advance supply information from the perspective of the manufacturer and study two main questions: i) How can the manufacturer utilize the the advance supply information when making replenishment decisions? ii) What is the value of such information sharing? We show that state-dependent base stock policies are optimal for the manufacturer. We then proceed to develop easily computable and implementable heuristic policies. The heuristics rely on the idea of assuming a functional form for the relationship between the forecast vector and the state-dependent base stock level. First, we develop a simulation-based method to optimize the parameter of the heuristic for any predetermined functional form. Second, we study a particular functional form and obtain a closed form approximation for the optimal parameter using an asymptotic approximation. Through computational experiments, we test the accuracy of the approximations and analyze the value of collaboration for the manufacturer under various business scenarios.