A combination of market volatility, radically different input economics and limitations of existing measurement approaches have been hindering the industry’s ability to address the challenge. E.g. Overall Equipment Effectiveness (OEE) metric provides important insights about availability, utilization and performance of a cone crusher or a processing plant, but doesn’t offer guidance on the whole operations progress and total productivity performance.
Technological complexity is increasingly beyond the human field of vision. Systems are not only working at speed, but are built on top and within each other, often interacting in unexpected ways. Operators find it difficult to consistently predict what is coming next, even with condition monitoring solutions in their toolbox.
“Many miners have adopted a mixture of technologies, including sensors, industrial internet of things (IIoT) devices, hardware, and software - facilitating reach to difficult asset locations and stretching monitoring distances, “says Ben Berwick, Global Digital Portfolio Manager for Asset Performance Management, ABB. “However, given that both modern and legacy technologies often collect and process data in a disjointed or siloed manner, subject-matter experts (SMEs) waste significant time wrangling data, running in and out of multiple systems, and relying on time-consuming and difficult visual interpretation of dense data trends, which precludes their ability to find latent asset performance issues or recommend corrective action in a timely manner.”
These problems have been compounded by the fact that the people with mechanical and electrical asset expertise are in high demand and in short supply around the world. When capable technicians and reliability engineers leave or retire, the knowledge of assets they worked on often leaves the mine with them.