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

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  • Tonya Boone
  • Ram Ganeshan

Abstract

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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    Cited by:

    1. 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.
    2. 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.
    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. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    5. Nguyen, Duy Tan & Adulyasak, Yossiri & Landry, Sylvain, 2021. "Research manuscript: The Bullwhip Effect in rule-based supply chain planning systems–A case-based simulation at a hard goods retailer," Omega, Elsevier, vol. 98(C).
    6. 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.
    7. Boone, Tonya & Ganeshan, Ram & Jain, Aditya & Sanders, Nada R., 2019. "Forecasting sales in the supply chain: Consumer analytics in the big data era," International Journal of Forecasting, Elsevier, vol. 35(1), pages 170-180.

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