Developing effective spare parts estimations results in improved system availability

Production and manufacturing firms are under great pressure to continuously reduce their production costs in order to stay competitive. Industrial operation cost analysis shows that, in general, maintenance represents a significant proportion of the overall operating cost. For instance, the cost of maintenance in the highly mechanized Kiruna underground iron ore mine in Sweden is 30–50% of the total operating cost. Spare parts availability, an issue of the maintenance process, is studied in this paper. Simply stated, production can be enhanced by the increased availability of functional machinery and the subsequent minimization of the total production cost. Spare parts estimation based on machine reliability characteristics and operating environment is a pragmatic method to improve supportability; it can guarantee non-delay in spare parts logistics which can ultimately improve production output. This study uses an improved statistical-reliability (S-R) approach which incorporates system/machine operating environment information in systems reliability analysis. It selects a multiple regression type of analysis based on Cox's proportional hazards modeling (PHM). It considers a parametric approach with a baseline Weibull hazard function and time independent covariates and analyzes the influence of operating environment factors on this model. Based on the results of analyses, a mathematical model for spare parts prediction in component level for non-repairable parts is developed and the findings are validated through a case study in the Swedish mining industry. The study finds that the outputs represent a significant difference in the required spare parts estimation when considering the influence of the system operating environment. The difference is significant in the sense of spare parts forecasting and inventory management; this can enhance the availability of parts and consequently of machines resulting in economical operation and cost savings.