Supply chain information sharing in a macro prediction market

Authors:
Zhiling Guo;Fang Fang;Andrew B. Whinston
Affiliations:
Department of Information Systems, UMBC (University of Maryland, Baltimore County), Baltimore, MD;Department of High Tech Management, College of Business Administration, Cal State Univ - San Marcos, San Marcos, CA;Department of Information, Risk, and Operations Management, McCombs School of Business, The University of Texas at Austin, Austin, TX
Venue:
Decision Support Systems
Year:
2006

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Abstract

This paper aims to address supply chain partners' incentives for information sharing from an information systems design perspective. Specifically, we consider a supply chain characterized by N geographically distributed retailers who order a homogeneous product from one manufacturer. Each retailer's demand risk consists of two parts: a systematic risk part that affects all retailers and an idiosyncratic risk part that only has a local effect. We propose a macro prediction market to effectively elicit and aggregate useful information about systematic demand risk. We show that such information can be used to achieve accurate demand forecast sharing and better channel coordination in the supply chain system. Our market-based framework extends the range of information sharing beyond the supply chain system. It also opens the door for other corporate risk management opportunities to hedge against aggregate economic risk.