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The Value of Information Sharing in the Retail Supply Chain: Two Case Studies


  • Tonya Boone
  • Ram Ganeshan


Retail supply chains are complex, with each company in the chain having multiple echelons of distribution. Forecasting and requirements planning are further challenged by managers’ reliance on “local” rather than chain-wide retail demand to make key operational decisions. A frequent consequence is the bullwhip effect. Using two case studies, Tonya and Ram show how information sharing – both within the company’s boundaries and with external partners – can mitigate the bullwhip effect and reduce supply-chain costs. Copyright International Institute of Forecasters, 2008

Suggested Citation

  • Tonya Boone & Ram Ganeshan, 2008. "The Value of Information Sharing in the Retail Supply Chain: Two Case Studies," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 9, pages 12-17, Spring.
  • Handle: RePEc:for:ijafaa:y:2008:i:9:p:12-17

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    References listed on IDEAS

    1. Kesten C. Green & J. Scott Armstrong, 2004. "Value of Expertise For Forecasting Decisions in Conflicts," Monash Econometrics and Business Statistics Working Papers 27/04, Monash University, Department of Econometrics and Business Statistics.
    2. Paul W. Rhode & Koleman S. Strumpf, 2004. "Historical Presidential Betting Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 127-141, Spring.
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    Cited by:

    1. Ali, Mohammad M. & Boylan, John E. & Syntetos, Aris A., 2012. "Forecast errors and inventory performance under forecast information sharing," International Journal of Forecasting, Elsevier, vol. 28(4), pages 830-841.
    2. Ali, Mohammad M. & Babai, Mohamed Zied & Boylan, John E. & Syntetos, A.A., 2017. "Supply chain forecasting when information is not shared," European Journal of Operational Research, Elsevier, vol. 260(3), pages 984-994.
    3. Babai, M.Z. & Boylan, J.E. & Syntetos, A.A. & Ali, M.M., 2016. "Reduction of the value of information sharing as demand becomes strongly auto-correlated," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 130-135.
    4. Babai, M.Z. & Ali, M.M. & Boylan, J.E. & Syntetos, A.A., 2013. "Forecasting and inventory performance in a two-stage supply chain with ARIMA(0,1,1) demand: Theory and empirical analysis," International Journal of Production Economics, Elsevier, vol. 143(2), pages 463-471.

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