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Performance prediction using supply chain uncertainty modelling

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  • S.C.L. Koh
  • A. Gunasekaran

Abstract

This paper presents a new quantitative method and a holistic approach to assess the impact of supply chain uncertainty on customer delivery performance. We adopt system theory, Multi Criterion Decision Making (MCDM) theory – Analytical Hierarchy Process (AHP) and Theory of Constraints (TOC) as the underpinning theoretical frameworks for this new method. Some 72 UK companies have verified this method and we call this – the Supply Chain Uncertainty Impact Assessment (SCUIA) method. Some 66 types of supply chain uncertainty are assessed. This method involves three levels of assessment, namely: (1) relative percentage occurrences of each supply chain uncertainty, (2) relative upstream and downstream impact of each supply chain uncertainty and (3) relative likelihood of each underlying causes of supply chain uncertainty. The findings show that underlying causes of supply chain uncertainty with higher likelihood of occurrences do not necessarily result in greater impact on the downstream delivery performance in the supply chain. It can be concluded that decision makers should not use the likelihood of occurrence or impact criteria in isolation when devising solutions to tackle supply chain uncertainty, but to tackle those underlying causes of supply chain uncertainty that have both greater impact and higher likelihood of occurrence.

Suggested Citation

  • S.C.L. Koh & A. Gunasekaran, 2006. "Performance prediction using supply chain uncertainty modelling," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 2(3), pages 279-293.
    • Handle: RePEc:ids:ijsoma:v:2:y:2006:i:3:p:279-293
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    Cited by:

    1. Jung-Yu Lai & Juite Wang & Yi-Hsuan Chiu, 2021. "Evaluating blockchain technology for reducing supply chain risks," Information Systems and e-Business Management, Springer, vol. 19(4), pages 1089-1111, December.

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