Assessing the Difference Between Shock Sharing and Demand Sharing in Supply Chains

Abstract

We consider the problem of assessing value of demand sharing in a multi-stage supply chain in which the retailer observes stationary autoregressive moving average demand with Gaussian white noise (shocks). Similar to previous research, we assume each supply chain player constructs its best linear forecast of the leadtime demand and uses it to determine the order quantity via a periodic review myopic order-up-to policy. We demonstrate how a typical supply chain player can determine the extent of its available information under demand sharing by studying the properties of the moving average polynomials of adjacent supply chain players. Hence, we study how a player can determine its available information under demand sharing, and use this information to forecast leadtime demand. We characterize the value of demand sharing for a typical supply chain player. Furthermore, we show conditions under which (i) it is equivalent to no sharing, (ii) it is equivalent to full information shock sharing, and (iii) it is intermediate in value to the two previously described arrangements. We then show that demand propagates through a supply chain where any player may share nothing, its demand, or its full-information shocks with an adjacent upstream player as quasi-ARMA in - quasi-ARMA out. We also provide a convenient form for the propagation of demand in a supply chain that will lend itself to future research applications.