# Distribution of Operating Costs Along the Value Chain of an Open-Pit Copper Mine
## 4 Discussion
While these operations are less energy-intensive compared to communication, hauling stands out as a significant cost contributor. In this study, hauling accounts for 40.52% of the mining OPEX, which is lower than the typical range of 43-70% reported in the literature for surface mining operations [30; 41; 44; 45; 46; 47]. This difference can be explained by the short haul distances between the pit and processing facilities, resulting in lower fuel consumption and maintenance costs. Despite this, hauling still represents a considerable portion of the total OPEX and is heavily influenced by factors such as haul distances, terrain, and fleet configuration. On the other hand, loading represents 21.98% of the total mining OPEX, which exceeds the industry benchmark range of 9-16% [25; 30; 41; 42; 43]. This higher cost could suggest inefficiencies that may be addressed through improved equipment utilization, optimized fleet management, or better labor allocation. Stockpiling, accounting for 0.86% of the total mining OPEX, is above the benchmark range of 0.05-0.25% [48], possibly reflecting higher costs associated with specific stockpile management practices or operational factors at the mine. Blending, at 2.59% of the total mining OPEX, falls within the industry benchmark range of 0.5-3% [48; 49], suggesting that this operation is operating efficiently in line with industry standards. This study provides valuable insights into the distribution of operating costs along the value chain of an open-pit copper mine, with a particular focus on the energy-intensive nature of communution, which accounts for nearly 70% of the total OPEX. The findings highlight opportunities for cost reduction through the adoption of energy-efficient technologies, mine-to-mill optimization, and renewable energy integration. By offering a detailed case study of cost distribution, this research contributes to the existing literature, enabling industry professionals to make data-driven decisions aimed at improving operational efficiency, reducing energy consumption, and enhancing sustainability. However, the study is limited by its exclusion of certain mineral processing stages, such as dewatering, tailings disposal, and extractive metallurgical processes, as well as external factors like transportation, environmental management, and reclamation. Additionally, its focus on a single case study may limit the generalizability of the findings. From a practical perspective, the research provides actionable strategies for improving cost-efficiency in mining operations. By optimizing energy use and refining operational practices, mining companies can significantly reduce costs, increase profitability, and enhance sustainability. While these findings offer valuable insights for mining engineers to make informed, data-driven decisions for enhanced efficiency and sustainability, future research should consider additional processing units, such as dewatering, tailings disposal, and extractive metallurgy, as well as external factors like transportation, environmental management, and reclamation, to offer a more comprehensive understanding of cost distribution across the entire mining value chain.