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Forecasting and Information Sharing in Supply Chains Under Quasi-ARMA Demand

Authors: Giloni, Avi
Hurvich, Clifford
Seshadri, Sridhar
Keywords: Supply Chain Management;Information Sharing;Time Series;ARMA;Invertibility
Issue Date: 13-Jul-2009
Series/Report no.: SOR-2009-04
Abstract: In this paper, we revisit the problem of demand propagation in a multi-stage supply chain in which the retailer observes ARMA demand. In contrast to previous work, we show how each player constructs the order based upon its best linear forecast of leadtime demand given its available information. In order to characterize how demand propagates through the supply chain we construct a new process which we call quasi-ARMA or QUARMA. QUARMA is a generalization of the ARMA model. We show that the typical player observes QUARMA demand and places orders that are also QUARMA. Thus, the demand propagation model is QUARMA-in-QUARMA-out. We study the value of information sharing between adjacent players in the supply chain. We demonstrate that under certain conditions information sharing can have unbounded bene¯ts. Our analysis hence reverses and sharpens several previous results in the literature involving information sharing and also opens up many questions for future research.
Appears in Collections:IOMS: Statistics Working Papers

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