Tire demand planning based on reliability and operating environment

Nouri Qarahasanlou, Ali; Ataei, Mohammad; Khalokakaie, Reza; Ghodrati, Behzad; Jafarei, Rasoul

doi:10.22059/ijmge.2016.59875

Tire demand planning based on reliability and operating environment

Document Type : Research Paper

Authors

¹ Ph.D candidate of mine engineering in faculty of mining, petroleum &amp; geophysics, shahrood university of technology, Shahrood, Iran

² Faculty member of mining engineering in Faculty of Mining, Petroleum & Geophysics, Shahrood University of Technology, Shahrood, Iran

³ Associate Professor at Luleå University of Technology, Lulea, Sweden

⁴ M.Sc. of mining engineering of Sahand University of Technology, Iran

10.22059/ijmge.2016.59875

Abstract

Tires represent a critical spare part in mines. There is a shortage of medium and large tires. In addition, with increased mining activities and the creation of new mines, the demand for tires has increased significantly. Thus, it is particularly important for mining engineers to identify tire characteristics and correctly manage the spare part inventory. Spare parts management is critical from an operational perspective, especially in asset intensive industries, such as mining, as well as in organizations owning and operating costly assets. A knowledge of the tires’ behavior (historical data) must be considered together with the operating environment conditions (covariates). This study uses multiple regression analysis based on Cox’s regression model to incorporate machine operating environment information into systems reliability analysis to estimate 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 Sungun mine in Iran. There is a significant difference in the results of spare parts forecasting and inventory management when considering and not considering covariates.

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