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A Supply Chain Tracking Model Using Auto-ID Observations

Author

Listed:
  • Thomas Kelepouris

    (Institute for Manufacturing, University of Cambridge, Cambridge, UK)

  • Duncan McFarlane

    (Institute for Manufacturing, University of Cambridge, Cambridge, UK)

  • Vaggelis Giannikas

    (Institute for Manufacturing, University of Cambridge, Cambridge, UK)

Abstract

Order location information is undoubtedly one of the most critical pieces of supply chain information. Yet supply chain visibility generally remains a challenge as observations of order progress are often irregular and collected manually. The emergence of Automated Identification (Auto-ID) technologies like Radio Frequency Identification (RFID) is improving the effectiveness of supply chain tracking systems. The authors propose a model that describes how Auto-ID observations across a supply chain and historical observation data can be combined to produce an ongoing order location estimation over time. The model is based on probabilistic reasoning principles and the resulting location estimation can be used to support operational decisions as well as to assess the quality and value of tracking information. The authors provide explicit instructions as to how to use the proposed model and using an illustrative example, they demonstrate how the model can produce ongoing location estimates based on RFID read events.

Suggested Citation

  • Thomas Kelepouris & Duncan McFarlane & Vaggelis Giannikas, 2012. "A Supply Chain Tracking Model Using Auto-ID Observations," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 5(4), pages 1-22, October.
    • Handle: RePEc:igg:jisscm:v:5:y:2012:i:4:p:1-22
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