The study analyzed production operations at an open-pit coal mine in East Kalimantan, one of Indonesia’s most important mining regions. By comparing existing production practices with an optimized planning model, the research shows how data-driven decision-making can transform operational performance in large-scale surface mining.
Production Planning Under Pressure
Open-pit mining relies heavily on precise coordination between excavation, loading, and hauling equipment. Any mismatch between equipment capacity, working time, and production targets can quickly lead to idle time, fuel waste, and escalating maintenance costs.
Globally, mining companies are under pressure from volatile commodity prices, stricter efficiency standards, and sustainability demands. In Indonesia, these challenges are amplified by the scale of coal operations and the intensive use of heavy machinery. Despite this, many mines still rely on conventional, experience-based planning methods rather than systematic optimization.
According to Malik’s study, inefficiencies in production planning—not resource shortages—are often the main reason mines fail to meet targets or control costs. This insight positions production optimization as a strategic lever for improving competitiveness in the mining sector.
How the Study Was Carried Out
The research used an applied quantitative approach grounded in operations research, focusing on a real-world case study of an open-pit coal mine in East Kalimantan.
Key features of the methodology included:
- Data sources:
Secondary operational data from company records, including equipment capacity, effective working hours, fuel use, maintenance costs, labor costs, and monthly production targets.
- Analytical approach:
A linear programming model was developed to minimize total operational costs while meeting production targets and respecting equipment and time constraints.
- Tools used:
Optimization software such as LINDO/LINGO or Excel Solver, commonly applied in mining operations research.
The optimized results were then directly compared with existing production conditions to assess performance improvements.
Clear Gains in Production Performance
The comparison between existing and optimized production plans revealed immediate and measurable improvements.
Under existing conditions, the mine produced 472,500 tons per month, falling short of its 500,000-ton target. After optimization, the production plan achieved the full target without increasing equipment capacity or working hours. This represents a 5.5 percent improvement in production achievement, driven entirely by better planning.
The findings show that production shortfalls were not caused by technical limitations, but by inefficient allocation of available resources.
Higher Utilization of Heavy Equipment
Heavy equipment represents the largest cost component in open-pit mining, making utilization rates a critical performance indicator.
Before optimization, average equipment utilization was around 70 percent, with significant idle time caused by poor synchronization between excavators and dump trucks. After applying the linear programming model:
- Excavator utilization increased from 72 percent to 85 percent
- Dump truck utilization rose from 68.5 percent to 82.5 percent
- Overall fleet utilization improved by more than 13 percent
This better balance between loading and hauling operations reduced bottlenecks and allowed material to flow more smoothly through the production system.
Lower Operating Costs Without Cutting Output
One of the most striking results of the study is the reduction in operating costs achieved through optimization.
Monthly operational costs fell from USD 2.63 million to USD 2.395 million, representing a cost reduction of nearly 9 percent. The largest savings came from:
- Fuel costs, reduced by more than 10 percent
- Maintenance costs, reduced by nearly 10 percent
- Labor costs, reduced through more efficient use of working time
These savings were achieved while maintaining full production output, highlighting the economic value of optimization-based planning.
Smarter Use of Working Time
The study also examined how working time was allocated across the production system. Total scheduled working hours remained constant, but optimization reduced idle time dramatically.
Effective working hours increased from 3,420 to 4,020 hours per month, while idle time fell by more than 40 percent. As a result, overall working time efficiency rose from 71 percent to nearly 84 percent.
This confirms that productivity gains can come from better scheduling and coordination, rather than longer shifts or additional equipment.
What This Means for the Mining Industry
The findings carry important implications for mine managers and policymakers.
From a management perspective, the study shows that data-driven optimization can unlock hidden capacity in existing operations. Linear programming provides a transparent and replicable framework for aligning equipment, time, and cost constraints in complex mining systems.
From a policy and industry standpoint, the research strengthens the case for adopting operations research tools in Indonesian mining, where many operations still rely on conventional planning methods. As Malik ethically paraphrases, optimization models allow mine planners to make rational, evidence-based decisions that improve efficiency without compromising production targets or sustainability goals (Universitas Bosowa).
Limitations and Future Directions
The study acknowledges that the optimization model uses deterministic assumptions and does not yet account for uncertainties such as weather disruptions, equipment breakdowns, or fluctuating material quality. In real operations, these factors can affect performance.
Future research is encouraged to integrate stochastic models, simulation techniques, or environmental and safety constraints to enhance robustness and support long-term sustainable mining management.
Author Profile
Malik.
Lecturer and researcher in mining engineering
Faculty
of Mining Engineering and Earth Sciences, Universitas Bosowa, Indonesia
Expertise: mine production planning, operations research, and optimization of
surface mining systems
Source
Article title: Optimization of Mine Production Planning to Support Operational Efficiency
Open-Pit Mining Operations
Journal: Formosa Journal of Science and Technology
Year: 2026
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