A holistic approach to diagnose uncertainty in ERP-controlled manufacturing shop floor

KOH, S. C. L. and SAAD, S. M. (2003). A holistic approach to diagnose uncertainty in ERP-controlled manufacturing shop floor. PRODUCTION PLANNING & CONTROL, 14 (3), 273-289.

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    The development of a holistic approach to diagnose uncertainty in Enterprise Resource Planning (ERP) -controlled manufacturing shop floor is presented. The holistic approach entails two elements: a business model for diagnosing underlying causes of uncertainty to late delivery and an effect recognition matrix for assessing the relative significance of the underlying causes. The holistic approach was successfully verified and validated via industrial survey and simulation experiments respectively. A total of 23 underlying causes of uncertainty were diagnosed exerting significant effect to late delivery in ERP-controlled manufacturing enterprise with mixed demand pattern. Compound effects from these causes were identified to be the main driver resulting in high level of late delivery. A secondary effect from this was the knock-on effect within a Bill of 'Material (BOM) chain ultimately diffusing the lateness to the finished products level. To achieve maximum benefits from delivery improvement effort, it was suggested that a holistic approach should be applied to diagnose underlying causes of uncertainty that exert significant effect to late delivery, systemically and optimally, so that the causes can be tackled effectively and efficiently with appropriate buffering or dampening techniques.

    Item Type: Article
    Additional Information: Times Cited: 4
    Research Institute, Centre or Group - Does NOT include content added after October 2018: Materials and Engineering Research Institute > Centre for Automation and Robotics Research > Systems Modelling and Integration Group
    Page Range: 273-289
    Depositing User: Danny Weston
    Date Deposited: 28 Apr 2010 09:54
    Last Modified: 18 Mar 2021 09:30
    URI: https://shura.shu.ac.uk/id/eprint/1724

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