Performance Driver Trees for Optimising Open Pit Operations

Performance driver trees or KPI trees are reasonably well-known tools for analysing performance in mining operations. However, the tools are often not user friendly. They may be technically correct but they are often clunky. This means they donÆt promote simple management decision-making and donÆt encourage businesses to make them an integrated part of optimising their operations. This paper describes a simple but effective driver tree process and tool that has been successfully implemented and used to aid decision making across a number of mining and processing operations. The important aspects of employing driver trees to assist in management of operations are to ensure that the chosen solution:includes a reporting solution, provides the bottom line impact of the performance indicators, automatically gathers the necessary data that reconciles with existing reports, and is built around a review process that fits with existing management processes. An adequate solution enables operators to identify where there are performance gaps and to quickly prioritise the urgency for addressing these issues. Two case studies are chosen to illustrate the critical success factors of implementing a performance driver tree solution. The first is a large, complex open pit operation. The driver trees, reporting and management processes were initially established for the mining and processing aspects of the operations. Once the initial solution was bedded down, a follow-up project was carried out to establish the equivalent solution for the support areas of the business. The final solution covered stand alone performance driver trees for mining, processing, engineering, maintenance, health and safety, human resources and finance. These individual driver trees were integrated into a total solution that showed how all of the identified KPIÆs impact profit and quantified the dollar impact of all variations. The reporting solution that was put in place automatically creates prioritised lists of variances for the business to address. The second case study is a performance diagnostic project where driver trees were used to assess where gaps in performance were occurring, and then to evaluate the impacts of proposed initiatives.