Development of a business model for diagnosing uncertainty in MRP environments

Abstract

Over the last thirty years, Materials Requirements Planning (MRP) based systems have become commonplace within batch manufacturing environments, but are still widely held to be under performing. This research hypothesises that there may be inherent problems associated with the application due to uncertainties that exist within dynamic operating environments. Research has highlighted both the absence of any business model that uses a structured and systematic approach to deal with uncertainty holistically and the lack of any widely used, consistent performance measures to allow comparison of research results. The industrial need for such a holistic approach became apparent from survey work, which showed MRP under-performed in the presence of uncertainty even when numerous Buffering and Dampening (BAD) approaches were applied. A business model of uncertainty that structures the causes and effects of uncertainty as a hierarchy of four levels has been proposed, to be verified and validated through industrial survey and simulation respectively. The relationship between causes and effects in the business model has been verified from survey results using Analysis of Variance (ANOVA), which identified twenty-three significant uncertainties within Mixed-Mode (MM) operating environments. Using a multi-product, multi-level dependent demand MRP simulation model within an MM operating environment driven by planned order release, an experimental programme has been carried out that showed finished products delivered late to be insensitive as a performance measure. Parts Delivered Late (PDL) was found to be more sensitive and has been adopted as the preferred measure. ANOVA on the simulation results validated the cause-and-effect relationships, showing that the higher the level of uncertainty, the worse was delivery performance. Individual uncertainties produced effects that were not discretely recognised in the literature. `Knock-on' effects are created by uncertainties delaying the issue of batches and affected particular Bill of Materials chains. `Compound' effects are caused by uncertainties affecting resource availability and also induced consequent knock-on effects. Simulation results also showed that late deliveries from suppliers, machine breakdowns, unexpected or urgent changes to schedules affecting machines and customer design changes are the most significant uncertainties within the parameter levels modelled. Several significant two-way and three-way interactions were found. The business model of uncertainty represents a practical and pragmatic attempt to act as a diagnostic tool to identify significant underlying causes affecting PDL for MM companies using MR1, enabling more effective application of suitable BAD approaches. Using the business model to drive a continuous improvement programme that monitored both levels of uncertainty and PDL would allow internal and external benchmarking for the efficacy of BAD approaches and for the reduction of uncertainties.