Tire demand planning based on reliability and operating environment

Tires are of critical spare parts in mines. There is a shortage of medium and large tires. In addition, since the mining activities and opening new mines has increased, the demand for tires has increased significantly as well. Thus, it is very important for mining engineers to identify the tire characteristics and properly manage the spare part inventory. Spare parts management is critical from an operational perspective, especially in intensive industries assets, such as mining, as well as in organizations that own and operate costly assets. A knowledge of the tires’ behavior (historical data) must be taken into account along with the operating environment conditions (covariates). This study uses Cox multiple regression model to incorporate machine operating environment information into systems reliability analysis for estimation of spare parts. It considers a proportional hazard model and a stratified Cox regression model for time independent and dependent covariates. Based on the results, the study develops a mathematical model for spare parts estimation at the component level for non-repairable parts (tires). It validates the outcomes using a case study of loader tires in the Sungun mine in Iran. There is a significant difference in the results of spare parts forecasting and inventory management when considering and dismissing the covariates.