Assessing the Value of Demand Sharing in Supply Chains

Abstract

We introduce a class of new sharing arrangements in a multi-stage supplychain in which the retailer observes stationary autoregressive movingaverage demand with Gaussian white noise (shocks). Similar to previousresearch, we assume each supply chain player constructs its best linearforecast of the leadtime demand and uses it to determine the orderquantity via a periodic review myopic order-up-to policy. We demonstratehow a typical supply chain player can create a sequence of partialinformation shocks (PIS) from its full information shocks FIS and sharethese with an adjacent upstream player. We go on to show how such asharing arrangement may be benecial to the upstream player bycharacterizing the player's FIS in such a case. Hence, we study how aplayer can determine its available information under PIS sharing, anduse this information to forecast leadtime demand. We characterize thevalue of FIS sharing for a typical supply chain player. Furthermore, weshow conditions under which a player is able to form and share valuablePIS without (i) revealing its historic demand sequence or (ii) revealingits FIS sequence. We also provide a way of comparing various PIS sharingarrangements with each other and with conventional sharing arrangementsinvolving demand sharing or FIS sharing. We show that demand propagatesthrough a supply chain where any player may share nothing or a sequenceof PIS shocks with an adjacent upstream player as quasi-ARMA in -quasi-ARMA out.

Keywords: Supply Chain Management, Information Sharing, Time Series, ARMA,Invertibility, QUARMA, Demand Sharing, Full Information Shocks, PartialInformation Shocks, Order-up-to policy