The mining industry is currently undergoing a significant evolution in its production processes to search for greater sustainability in its operations. Historically, identifying the low performance of equipment occurs with inspections on the mining faces or through management reports. However, through the advent of technology and greater processing and data transmission capacity, many companies have developed research to improve the decision-making process to identify events in real-time to increase mine safety and act on low-performance equipment. Thus, this paper presents the development of a cyber-physical system to control loading productivity for open-pit mines. Furthermore, the tool allows analysis of the productivity, which generates alarms in low-performance situations measured according to pre-established targets. In the practical applications of the method, the increase in loading productivity reached 4.71% by reducing process variability. In addition, the stability of the loading operation resulted in the lowest queues, fuel consumption and greenhouse gas emissions.
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