Statistical Management and Modeling for Demand Spare Parts

In recent years, increased emphasis has been placed on improving decision-making in business and government. A key aspect of decision-making is being able to predict the circumstances surrounding individual decision situations. Examining the diversity of requirements in planning and decision-making situations, it is clearly stated that no single forecasting methods or narrow set of methods can meet the needs of all decision-making situations. Moreover, these methods are strongly dependent on factors, such as data quantity, pattern, and accuracy, that reflect their inherent capabilities and adaptability, such as intuitive appeal, simplicity, ease application, and, least but not last, cost. Section 15.1 deals with the placement of demand forecasting problem as one of biggest challenge in the repair and overhaul industry; after this brief introduction, Sect. 15.2 summarizes the most important categories of forecasting methods; paragraphs from 15.3 to 15.4 approach the forecast of spare parts first as a theoretical construct, but some industrial applications and results are added from field training, as in many other parts of this chapter. Section 15.5 undertakes the question of optimal stock level for spare parts, with particular regards to Low Turnaround Index (LTI) parts conceived and designed for the satisfaction of a specific customer request, by the application of classical Poisson methods of minimal availability and minimum cost; similar considerations are drawn and compared in Sect. 15.6 dealing with models based on binomial distribution. An innovative extension of binomial models based on total cost function is discussed in Sect. 15.7. Finally, Sect. 15.8 adds the Weibull failure rate function to LTI spare parts stock level in maintenance system with declared wear conditions.