Information transformation in a supply chain: a simulation study
Computers and Operations Research
Quantity flexibility contracts under Bayesian updating
Computers and Operations Research
ICEC '05 Proceedings of the 7th international conference on Electronic commerce
A strategic analysis of inter organizational information sharing
Decision Support Systems
The governing dynamics of supply chains: The impact of altruistic behaviour
Automatica (Journal of IFAC)
dg.o '07 Proceedings of the 8th annual international conference on Digital government research: bridging disciplines & domains
Relational Antecedents of Information Flow Integration for Supply Chain Coordination
Journal of Management Information Systems
Electronic Commerce Research and Applications
Is there a benefit to sharing market sales information? Linking theory and practice
Computers and Industrial Engineering
On supply chain cash flow risks
Decision Support Systems
An XML-based modular system analysis and design for supply chain simulation
Robotics and Computer-Integrated Manufacturing
Information Technology and Management
Competition and Cooperation in a Two-Stage Supply Chain with Demand Forecasts
Operations Research
Expert Systems with Applications: An International Journal
A belief-rule-based inventory control method under nonstationary and uncertain demand
Expert Systems with Applications: An International Journal
The value of information sharing in a supply chain with a seasonal demand process
Computers and Industrial Engineering
Performance of supply chain collaboration - A simulation study
Expert Systems with Applications: An International Journal
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In a recent paper, Lee, So, and Tang (2000) showed that in a two-level supply chain with non-stationary AR(1) end demand, the manufacturer benefits significantly when the retailer shares point-of-sale (POS) demand data. We show in this paper, analytically and through simulation, that the manufacturer's benefit is insignificant when the parameters of the AR(1) process are known to both parties, as in Lee, So, and Tang (LST). The key reason for the difference between our results and those of LST is that LST assume that the manufacturer also uses an AR(1) process to forecast the retailer order quantity. However, the manufacturer can reduce the variance of its forecast further by using the entire order history to which it has access. Thus, when intelligent use of already available internal information (order history) suffices, there is no need to invest in interorganizational systems for information sharing.