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Spare-part management in a heterogeneous environment.

Reza Barabadi1, Mohammad Ataei1, Reza Khalokakaie1

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Effective spare-part planning in mining requires accounting for data heterogeneity. This study uses a frailty model to accurately estimate spare parts by addressing observed and unobserved risk factors affecting equipment reliability.

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Area of Science:

  • Operations Research
  • Reliability Engineering
  • Mining Engineering

Background:

  • Spare-part management is crucial for mining equipment productivity.
  • Reliability data (failure and repair times) inform spare-part needs.
  • Operational environment and equipment behavior cause data heterogeneity.

Purpose of the Study:

  • To address the impact of heterogeneity in reliability data on spare-part estimation.
  • To introduce and apply a frailty model for modeling observed and unobserved risk factors.
  • To improve the accuracy of spare-part planning in the mining industry.

Main Methods:

  • Collected and analyzed mining equipment failure and repair data.
  • Investigated heterogeneity within the reliability data.
  • Applied a frailty model to account for observed and unobserved risk factors.

Main Results:

  • Identified significant heterogeneity in mining equipment reliability data.
  • Demonstrated that ignoring unobserved risk factors leads to biased spare-part estimations.
  • The frailty model effectively incorporates heterogeneity for more realistic estimates.

Conclusions:

  • Effective spare-part planning necessitates modeling data heterogeneity.
  • Frailty models offer a robust statistical approach to handle observed and unobserved risk factors.
  • Accurate spare-part estimation improves mining equipment productivity and reduces costs.