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

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.

[1]  Randall P. Sadowski,et al.  Introduction to Simulation Using Siman , 1990 .

[2]  S. T. Enns.  MRP performance effects due to lot size and planned lead time settings , 2001 .

[3]  C. Ou-Yang,et al.  Developing a STEP-Based Integration Environment to Evaluate the Impact of an Engineering Change on MRP , 1999 .

[4]  T. Saaty.  Fundamentals of Decision Making and Priority Theory With the Analytic Hierarchy Process , 2000 .

[5]  J. Parnaby,et al.  A systems approach to the implementation of JIT methodologies in Lucas Industries , 1988 .

[6]  Surendra M. Gupta,et al.  A structured analysis of material requirements planning systems under combined demand and supply uncertainty , 1993 .

[7]  Chrwan-Jyh Ho,et al.  Uncertainty-dampening methods for reducing MRP system nervousness , 1995 .

[8]  Averill M. Law,et al.  Simulation modelling and analysis , 1991 .

[9]  Angappa Gunasekaran,et al.  Measuring uncertainties in MRP environments , 2000 .

[10]  Jeff Hoi Yan Yeung,et al.  Parameters affecting the effectiveness of MRP systems: A review , 1998 .

[11]  R. B. Whitner,et al.  Guidelines for selecting and using simulation model verification techniques , 1989, WSC '89.

[12]  Sameh M. Saad,et al.  Uncertainty under MRP-planned manufacture: Review and categorization , 2002 .

[13]  Victor Weinberg,et al.  Structured Analysis , 1978 .

[14]  V. D. R. Guide,et al.  A review of techniques for buffering against uncertainty with MRP systems , 2000 .

[15]  Sameh M. Saad,et al.  Development of a business model for diagnosing uncertainty in ERP environments , 2002 .

[16]  B. W. Taylor,et al.  A simulation-based experimental investigation of supply/timing uncertainty in MRP systems , 1984 .

[17]  D. Clay Whybark,et al.  MATERIAL REQUIREMENTS PLANNING UNDER UNCERTAINTY , 1976 .

[18]  T. C. Ireland,et al.  Correlating MRP system nervousness with forecast errors , 1998 .

[19]  Sameh M. Saad,et al.  Handling internal and external disturbances in responsive manufacturing environments , 1998 .

[20]  H Mather,et al.  Reschedule the reschedules you just rescheduled - Way of life for MRP? , 1977 .