Information Sharing in a Supply Chain Under ARMA Demand

Vishal Gaur (), Avi Giloni () and Sridhar Seshadri ()
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Vishal Gaur: Department of Information, Operations, and Management Science, Leonard N. Stern School of Business, New York University, 44 West 4th Street, New York, New York 10012
Avi Giloni: Sy Syms School of Business, Yeshiva University, 500 West 185th Street, New York, New York 10033

Management Science, 2005, vol. 51, issue 6, 961-969

Abstract: In this paper we study how the time-series structure of the demand process affects the value of information sharing in a supply chain. We consider a two-stage supply chain model in which a retailer serves autoregressive moving-average (ARMA) demand and a manufacturer fills the retailer's orders. We characterize three types of situations based on the parameters of the demand process: (i) the manufacturer benefits from inferring demand information from the retailer's orders; (ii) the manufacturer cannot infer demand, but benefits from sharing demand information; and (iii) the manufacturer is better off neither inferring nor sharing, but instead uses only the most recent orders in its production planning. Using the example of ARMA(1,1) demand, we find that sharing or inferring retail demand leads to a 16.0% average reduction in the manufacturer's safety-stock requirement in cases (i) and (ii), but leads to an increase in the manufacturer's safety-stock requirement in (iii). Our results apply not only to two-stage but also to multistage supply chains.

Keywords: single-item inventory model; nonstationary demand; information sharing; supply chain management; electronic data interchange (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (35)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:51:y:2005:i:6:p:961-969

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