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  4. Will a supplier benefit from sharing good information with a retailer?
 
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Will a supplier benefit from sharing good information with a retailer?

Journal
Decision Support Systems
Journal Volume
56
Journal Issue
1
Pages
131-139
Date Issued
2013
Author(s)
TSAN MING CHOI  
Li J.
Wei Y.
DOI
10.1016/j.dss.2013.05.011
URI
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84889097145&doi=10.1016%2fj.dss.2013.05.011&partnerID=40&md5=c23b17f511b91d83f6455b0c138eb613
https://scholars.lib.ntu.edu.tw/handle/123456789/612338
Abstract
Information sharing has been known to be crucial in supply chain management. Prior empirical finding reveals that suppliers in practice tend to help their trading partners improve forecast accuracy. This paper examines this issue and explores the up-down (from an upstream supplier to a downstream retailer) strategic information sharing issues in a two-echelon supply chain. We first model a supply chain with forecast updating and returns policy. The forecast updating scheme adopts the Bayesian approach with unknown mean and unknown variance. We then proceed to analytically explore the effects of forecast updating on the supplier and the retailer. Our analysis has revealed that: 1. Demand information with low relevance can lead to a loss to the retailer. 2. In the absence of returns policy, the supplier has an incentive to provide "bad information" which may be harmful to the retailer. 3. The supplier will provide "good information" to the retailer only under the returns policy. 4. With up-down information sharing, win-win coordination can be achieved by using a proper returns policy. Many of these results can supplement and challenge the prior research findings that supplier has good incentive to help retailers in improving forecast. ? 2013 Elsevier B.V.
Subjects
Forecast updating; Returns policy; Supply chain management
Other Subjects
Bayesian approaches; Demand information; Empirical findings; Forecast accuracy; Information sharing; Returns policies; Trading partners; Two-echelon supply chain; Bayesian networks; Forecasting; Information analysis; Supply chain management; Sales
Type
journal article

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