Information sharing in supply chains: An empirical and theoretical valuation

Ruomeng Cui, Gad Allon, Achal Bassamboo, Jan A. Van Mieghem

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


We provide an empirical and theoretical assessment of the value of information sharing in a two-stage supply chain. The value of downstream sales information to the upstream firm stems from improving upstream order fulfillment forecast accuracy. Such an improvement can lead to lower safety stock and better service. Based on the data collected from a consumer packaged goods company we empirically show that, if the company includes the downstream sales data to forecast orders, the improvement in the mean squared forecast error ranges from 7.1% to 81.1% across all studied products. Theoretical models in the literature, however, suggest that the value of information sharing should be zero for over half of our studied products. To reconcile the gap between the literature and the empirical observations, we develop a new theoretical model. Whereas the literature assumes that the decision maker strictly adheres to a given inventory policy, our model allows him to deviate, accounting for private information held by the decision maker, yet unobservable to the econometrician. This turns out to reconcile our empirical findings with the literature. These "decision deviations" lead to information losses in the order process, resulting in a strictly positive value of downstream information sharing. Furthermore, we empirically quantify and show the significance of the value of operations knowledge-the value of knowing the downstream replenishment policy.

Original languageEnglish (US)
Pages (from-to)2803-2824
Number of pages22
JournalManagement Science
Issue number11
StatePublished - Nov 2015


  • Autoregressive integrated moving average process
  • Decision deviation
  • Empirical forecasting
  • Information sharing
  • Signal propagation
  • Supply chain
  • Time series

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research


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