Benchmarking Productivity & Fleet Utilization

Benchmarking is an ultimate form of collaboration that drives improvement by identifying operations with superior performance and understanding their processes and applying that knowledge, which in turn strengthens the industry’s sustainability efforts.

Although some benchmarking is already being done, there’s difficulty due to the tracking of lagging indicators caused by different companies having different methods of case management and classification. This project will address this challenge by creating a template that allows the data to be collected and translated into a common database that is comparable across companies.

Industry-wide benchmarking includes all tier mining companies and OEMs with the alignment of terminology and the sharing of best practices across sites globally. Proper benchmarking can:

  • further mine progression,
  • enhance performance,
  • help others understand their position,
  • provide transparency to clearly identify possible areas for improvements compared to peers, and
  • be a basis for resource prioritization and a common area that supports regulations.

Benchmarking allows for a protocol to enhance normal reporting with minimum effort, and using a common language that eliminates complexity and inconsistencies to allow for meaningful conversations and data-sharing that enables enhanced mine operations.

Opportunities To Participate

We’re looking for:

  • Subject Matter Experts with knowledge and experience to contribute to the project
  • GMG Members to join the steering committee to lead the project

Upcoming workshops:

Watch this space for future workshop dates and sign up for our newsletter to get updates.

Project Updates

Discussions amongst participants at prior workshops included some specific considerations for this project, such as:

  • Sharing target ranges and industry averages rather than looking at individual companies
  • Maintaining anonymity
  • Security of data sharing
  • Different existing standards
  • Data capture specific to energy per tonne so it’s accurate and repeatable, and drive value
  • Different stages of sharing (aggregated to raw data; data origin, integrity, and sharing)
  • Operating metrics
  • When data should be shared
  • Value of the data