INNOVATION IN DATA ANALYSIS USED TO DRIVE OPERATIONAL IMPROVEMENT North American Mine achieves 36 reduction in truck events through big data analysis and focused training intervention

摘要

In the information era, vast amounts of data have become available to decision makers to the extent that distinguishing the noise from the right signal, has become very problematic. This paper will show how Operator Performance Analytics (OPA) can be used to improve the performance of heavy equipment operators in any mine site, specifically we will showcase a successful North American case study to explore further details such as: 1. How various data sets from the mine were queried and run through an analytic engine, to produce performance metrics for heavy equipment operators. 2. How each operator was given a score card using as performance indicators productivity and reliability metrics. 3. How a tailored training program was initiated addressing mine objectives derived from these data insights. Part of the training initiative measured the impact on actual cost savings, productivity improvement and resulted value from data-driven decision making. While most data analysis is conducted by mining companies on productivity and maintenance, Operator Performance Analytics used operational data to identify operator performance variability and training needs in a time-sensitive manner. Mine management used the Operator Performance Analytics to drive a targeted mine site initiative reducing total truck events by 36%, the most notable event by count reduction was 'driving with the bed up' with a 94% improvement. Results achieved include reducing the mine maintenance costs and improving equipment reliability. The right data, correctly analyzed in the precise context, can provide mine site stakeholders with accurate and timely intelligence on operator performance variability and training needs analysis; thus, achieving significant improvements in safety, productivity and reliability.