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Supply chain forecasting when information is not shared

Listed author(s):
  • Ali, Mohammad M.
  • Babai, Mohamed Zied
  • Boylan, John E.
  • Syntetos, A.A.
Registered author(s):

    The operations management literature is abundant in discussions on the benefits of information sharing in supply chains. However, there are many supply chains where information may not be shared due to constraints such as compatibility of information systems, information quality, trust and confidentiality. Furthermore, a steady stream of papers has explored a phenomenon known as Downstream Demand Inference (DDI) where the upstream member in a supply chain can infer the downstream demand without the need for a formal information sharing mechanism. Recent research has shown that, under more realistic circumstances, DDI is not possible with optimal forecasting methods or Single Exponential Smoothing but is possible when supply chains use a Simple Moving Average (SMA) method. In this paper, we evaluate a simple DDI strategy based on SMA for supply chains where information cannot be shared. This strategy allows the upstream member in the supply chain to infer the consumer demand mathematically rather than it being shared. We compare the DDI strategy with the No Information Sharing (NIS) strategy and an optimal Forecast Information Sharing (FIS) strategy in the supply chain. The comparison is made analytically and by experimentation on real sales data from a major European supermarket located in Germany. We show that using the DDI strategy improves on NIS by reducing the Mean Square Error (MSE) of the forecasts, and cutting inventory costs in the supply chain.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0377221716309717
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    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 260 (2017)
    Issue (Month): 3 ()
    Pages: 984-994

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    Handle: RePEc:eee:ejores:v:260:y:2017:i:3:p:984-994
    DOI: 10.1016/j.ejor.2016.11.046
    Contact details of provider: Web page: http://www.elsevier.com/locate/eor

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