Development of a business model for diagnosing uncertainty in ERP environments

KOH, S. C. L. and SAAD, S. M. (2002). Development of a business model for diagnosing uncertainty in ERP environments. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 40 (13), 3015-3039.

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It has been identified from a comprehensive literature review and a subsequent industrial survey that uncertainty within an Enterprise Resource Planning (ERP) controlled manufacturing environment has not been tackled systematically and not examined effectively. This has been shown in two areas: (i) most identified research on uncertainty within an ERP environment focuses on finding suitable approaches to cope with uncertainty rather than tackling the underlying causes; and (ii) most identified simulation models on uncertainty controlled by ERP, while purporting to represent such an environment, do not truly model a multi-level dependent demand system, with multi-products, and controlled by Planned Order Release (POR) based on planned lead times. The aim of this research is to tackle these two areas simultaneously. A business model that is aimed at enabling the underlying causes of uncertainty within ERP environments is developed. This business model is verified and validated through a comprehensive survey involving ERP users operating in batch manufacture with mixed demand patterns. Validation of the business model is carried out via an extensive experimental programme within a simulation model-developed using SIMAN V-that truly represents a multi-level dependent demand system, in ERP environments disturbed by uncertainty. An experimental fractional factorial design is executed whereby the simulation results are analysed using Analysis of Variance (ANOVA). The results indicated that late delivery from suppliers; machine breakdowns; unexpected or urgent changes to the schedule affecting machines; and customer design changes, affect Parts Delivered Late (PDL) significantly. It was found that the higher the level of uncertainties, the higher the level of PDL.

Item Type: Article
Additional Information: Times Cited: 15
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: 3015-3039
Depositing User: Danny Weston
Date Deposited: 28 Apr 2010 09:56
Last Modified: 18 Mar 2021 09:30

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