Analytics Made Easy - StatCounter
Case Study 1 2018-03-20T16:31:55+00:00



  • Identified 16% overall improvement in BCMs moved by targeting haul speed and payload
    • 11% improvement due to increased full and empty haul speeds
    • 5% improvement due to increased payload


  • Haul speed and payload typically the most sensitive levers on a loaf and haul (L&H) value driver tree (VDT)
  • Haul speed usually has the most improvement potential
  • Traditional fleet management systems (FMS) and other data can be used to track average speed trends across crews, operators and machines on specific haul routes, but are not able to give information on where the improvement opportunities are
  • Operators tend to contest average speed data for various reasons, including equipment performance, weather conditions, traffic, haul road conditions, loading and dumping area conditions, overloading and ramp gradients
  • This is compounded by the fact that mining environments are dynamic, and vary greatly due to seasonality, mining methods, mine plan changes and changes in haul routes
  • Haul road and traffic management design also plays a crucial role in average haul speeds
  • Payload is often measured but without regular feedback loops in place, loader operators do not know where they need to improve, or how they are performing against target of relative to other loader operators

What we did:

  • Completed a Diagnostic to identify and wuantify where the biggest opportunities are in terms of loader and haul truck operator behavior
    • Loader operator – payload
    • Haul truck operator – speed zone conformance
    • Mine layout and traffic management plan, including the removal of stop signs
  • Install MaxMine hardware on all trucks in the fleet, as well as other hardware for viewing outputs in different locations (shift meeting areas, deployment centres)
  • Configure access for all users at different levels to be able to view reporting and identify improvement opportunities for operators
  • Coach operators, supervisors and management on the different aspects of the MaxMine system, and ensure that MaxMine was embedded in the organization as a key part of the Management Operating System (MOS) to drive accountability and improve performance

Client achieved:

  • Prioritised improvement focus areas, with specific, actionable items that need to be changed to drive improvement
  • Complete access to real driver performance data, with like for like comparisons across operators and identification of where the biggest operator improvement areas are
  • Continued coaching on site from experienced MaxMine coaches on implementing the behavioral changes needed to deliver bottom line improvement
  • Near-real time data on equipment and operator performance, including
    • Haul truck operator performance at an individual level, with specific improvement focus areas
    • Loader operator performance with regards to fill factor and bias loading
    • Real equipment utilization, without reliance on manual inputs – operating time, idle time, cycle time breakdown and engine-off time