A novel integrated key performance indicator for evaluating open-pit mine haulage systems: application of GMG standards

This study evaluates the performance of a haulage system in a South Korean limestone open-pit mine using the standardized time classification framework proposed by the Global Mining Guidelines Group (GMG). Additionally, a new integrated key performance indicator (KPI) that consolidates nine individual KPIs proposed by GMG is introduced. The aim is to assess the availability, utilization, and effectiveness of the haulage system comprising trucks and loaders, while developing a more intuitive and comprehensive KPI for performance assessment. Data were collected from various sources, including work orders, work logs, the haulage management system, and time studies, then classified according to the GMG standardized time categories. This study applied three methods—equal weighting, GMG group-based weighted aggregation, and correlation analysis with clustering—to calculate the integrated KPI and compare the results. The results showed high physical and mechanical availability (over 97%), but relatively low asset utilization (average 35.9%) and production effectiveness (average 59.8%). Using the GMG group-based weighted aggregation method, integrated KPI scores ranged from 69.8% (Truck 33) to 79.2% (Trucks 37 and 38), indicating performance differences related to uptime and utilization. This research suggests that integrated KPI is an effective tool for efficiently evaluating system performance and quickly identifying issues, thereby significantly improving the efficiency and sustainability of mining operations.

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Publication Details

Journal
Ain Shams Engineering Journal
Published
2025-06-27
DOI
https://doi.org/10.1016/j.asej.2025.103589
Primary Topic
Mining Techniques and Economics
Type
article
Field-Weighted Citation Impact
0.00

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article

A novel integrated key performance indicator for evaluating open-pit mine haulage systems: application of GMG standards

Sebeom Park, Yosoon Choi, Dahee Jung
Ain Shams Engineering Journal
Mining Techniques and Economics
article

A novel integrated key performance indicator for evaluating open-pit mine haulage systems: application of GMG standards

Sebeom Park, Yosoon Choi, Dahee Jung
article en

Abstract

This study evaluates the performance of a haulage system in a South Korean limestone open-pit mine using the standardized time classification framework proposed by the Global Mining Guidelines Group (GMG). Additionally, a new integrated key performance indicator (KPI) that consolidates nine individual KPIs proposed by GMG is introduced. The aim is to assess the availability, utilization, and effectiveness of the haulage system comprising trucks and loaders, while developing a more intuitive and comprehensive KPI for performance assessment. Data were collected from various sources, including work orders, work logs, the haulage management system, and time studies, then classified according to the GMG standardized time categories. This study applied three methods—equal weighting, GMG group-based weighted aggregation, and correlation analysis with clustering—to calculate the integrated KPI and compare the results. The results showed high physical and mechanical availability (over 97%), but relatively low asset utilization (average 35.9%) and production effectiveness (average 59.8%). Using the GMG group-based weighted aggregation method, integrated KPI scores ranged from 69.8% (Truck 33) to 79.2% (Trucks 37 and 38), indicating performance differences related to uptime and utilization. This research suggests that integrated KPI is an effective tool for efficiently evaluating system performance and quickly identifying issues, thereby significantly improving the efficiency and sustainability of mining operations.

Ain Shams Engineering JournalVol. 16(10)
Sejong University (KR), Korea Institute of Geoscience and Mineral Resources (KR), Pukyong National University (KR)
Ministry of Trade, Industry and Energy, Korea Institute of Energy Technology Evaluation and Planning
Openalex Percentile: Top 83%
Mining Techniques and Economics
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