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Abstract

As the title implies, the application of MRP into an aviation context is the response to the huge cost of parts holding in an ever-expanding industry. The nature of intermittent parts demand (unpredictable parts), typical of maintenance and overhaul inventory parts control, is investigated both to illustrate the deficiency of traditional ROP systems for dependent-demand inventory and other applications in the area of lot sizing and forecasting with a specific exploration into sources of demand lumpiness.

In order to investigate current inventory procedure, we surveyed 175 airline operators and maintenance service organisations, to explore the status of MRP and ROP worldwide. This response showed current inventory practice to be less than effective and that better systems were required, leading us to investigate specific problems experienced namely; lot-size and forecasting methods used within the MRP concept. MRP had made some inroads into the aviation sector, but a number of factors have prevented its general uptake.

Through a case study of KTM-uk’s workshop practices within overhaul and repair, we apply various solutions to lot-size and forecasting methodology in order to realise best practice, putting forward a small scale MRP-spreadsheet as a working tool. In the process we present two predictive models; a Lot-size Predictive Cost Model. LPCM, and a Predictive Error-Forecasting Model, PEFM. The models in their present form use seventeen lot-size and thirteen forecasting methods respectively, simplifying material management through appropriate estimates of costs and planning needs. Within lotsizing, we found that under almost all operations conditions the WWA and MSM2 methods give the best performance. Similarly the WMA method followed by the Holt and the Croston methods work best for forecasting intermittent demand parts.

Publication Type:	Thesis (Doctoral)
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Departments:	School of Science & Technology > School of Science & Technology Doctoral Theses Doctoral Theses School of Science & Technology > Engineering

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