Reliability and operating environment based spare parts planning

Abstract: The required spare parts planning for a system/machine is an integral part of the product support strategy. The number of required spare parts can be effectively estimated on the basis of the product reliability characteristics. The reliability characteristics of an existing machine/system are influenced not only by the operating time, but also by factors such as the environmental parameters (e.g. dust, humidity, temperature, moisture, etc.), which can degrade or improve the reliability. In the product life cycle, for determining the accurate spare parts needs and for minimizing the machine life cycle cost, consideration of these factors is useful. Identification of the effects of operating environment factors (as covariates) on the reliability may facilitate more accurate prediction and calculation of the required spare parts for a system under given operating conditions. The Proportional Hazard Model (PHM) method is used for estimation of the hazard (failure) rate of components under the effect of covariates. The existing method for calculating the number of spare parts on the basis of the reliability characteristics, without consideration of covariates, is modified and improved to arrive at the optimum spare parts requirement. In this research an approach has been developed to forecast and estimate accurately the spare parts requirements considering operating environment and to create rational part ordering strategies. Subsequently, two models (exponential and Weibull reliability based) considering environmental factors are developed to forecast and estimate the required number of spare parts within a specific period of the product life cycle. This study only discusses non-repairable components (changeable/service parts), which must be replaced upon failure. To test the models, the data collection and classification was carried out from two mining company in Iran and Sweden and then the case studies concerning spare parts planning based on the reliability characteristics of parts, with/without considering the operating environment were done. The results show clearly the differences between the consumption patterns for spare parts with and without taking into account the effects of covariates (operating environment) in the estimation. The final discussion treats a risk analysis of not considering the system’s working conditions through a non-standard (new) event tree approach in which the organizational states and decisions were included and taken into consideration in the risk analysis. In other words, we used the undesired states instead of barriers in combination with events and consequent changes as a safety function in event tree analysis. The results of this analysis confirm the conclusion of this research that the system’s operating environment should be considered when estimating the required spare parts.