No abstract

Spare parts provision is a complex problem and requires an accurate model to analyze all factors that may affect the required number of spare parts. The number of spare parts required for an item can be effectively estimated based on its reliability. The reliability characteristics of an item are influenced by different factors such as the operational environment, maintenance policy, operator skill, etc. However, in most reliability-based spare parts provision (RSPP) studies, the effect of these influence factors has not been considered. Hence, the statistical approach selected for reliability performance analysis must be able to handle the effect of these factors. One of the important models for reliability analysis by considering risk factors is the proportional hazard model (PHM), which has received less attention in the field of spare parts provisioning. Thus, this paper aims to demonstrate the application of the available reliability models with covariates in the field of spare part predictions using a case study. The proposed approach was evaluated with data from the system of fleet loading of the Jajarm Bauxite mine in Iran. The outputs represent a significant difference in spare parts forecasting and inventory management when considering covariates.

In civil and mining industries, Wheel loaders are an important component and their cost capability at effective operation. The environmental and operational factors dramatically affect the performance of loaders. In many cases, failure data are often collected from multiple and distributed units in different operational conditions, which can introduce heterogeneity into the data. Part of such heterogeneity can be explained and isolated by the observable covariates, whose values and the way they can affect the item's reliability are known. However, some factors that may affect the item's reliability are typically unknown and lead to unobserved heterogeneity. These factors are categorized as unobserved covariates. In most reliability studies, the effect of unobserved covariates is neglected. This may lead to erroneous model selection for the time to failure of the item, as well as wrong conclusions and decisions. There is a lack of sufficient knowledge, theoretical background, and a systematic approach to model the unobserved covariate in reliability analysis. This paper aims to present a framework for reliability analysis in the presence of unobserved and observed covariates. The unobserved covariates will be analyzed using frailty models (Such as Mixed Proportional Hazard).A case will illustrate the application of the framework..