Machine Monitoring and Automation as Enablers of Lean Mining

Three decades ago mining companies, principally in North America, aided by machine and explosive manufacturers, started significant attempts to develop and implement automated mining systems, mainly underground. These tended to be motivated by decreasing cost, increasing productivity, reducing labour requirements, and improving safety. These are important business drivers, however, little attention was given to the underlying goal of mining automation, namely to bring upstream processes in control and reducing variation, as seen in downstream mineral processing plants.This paper first reviews the nature of Lean Thinking and its application in the context of the growing commitments by industry to develop future automated mine production systems. The paper then makes reference to prior and current experiences in open pit blasthole drill and shovel performance monitoring, to demonstrate the principle that machine performance data offers as a means, not only to characterise the dynamic geological and working environment, but also to provide a foundation for automation. In this case, drill and shovel monitoring was originally motivated as a means to optimise blast fragmentation through more effective drill and blast designs that are based on an accurate determination of the bench geological and geotechnical characteristics, hence managing variation in the drilling and blasting process. This paper considers how automating and integrating such components of the mine production system would bring processes into a controlled state, creating value at the face, within a framework of Lean mining.