Are You Benchmarking Your Mining Fleet Management System Dispatchers?
Updated: Nov 4, 2020
There are many metrics available to benchmark a shift crew or an individual operator. Less commonly discussed are metrics specifically designed to understand how well any particular crew’s Dispatch Team are running the mining fleet management system.
This follow-up article focuses on a specific fleet management solution: Cat MineStar Fleet, and considers how data generated by this system can be harnessed to benchmark, measure and improve your Dispatch Team's performance - meaning that the system is running smoothly, efficiently and generating cleaner data.
Using Cat MineStar Fleet Data for Dispatcher Benchmarking
The Cat MineStar Fleet historical database is rich in data that is incredibly useful for benchmarking, and within this database, assignment data is possibly the most helpful for creating KPI that can be used for benchmarking your Dispatch Team.
'Assignment' data is a simply dataset containing a history of truck assignments. For example: “Truck 10 load at Shovel 9” or “Cannot assign Truck 10 to dump as material cannot be determined”.
Tying this information to standard truck cycle data is a great way to deliver metrics on how well the system is being used. Here are a few basic examples of how Cat MineStar Fleet data can be used to understand dispatcher performance.
Fleet Data: Manual Assignments
A high proportion of 'Manual Assignments' indicates that MineStar Fleet's assignment engine is not being used to its full potential, because the Dispatchers are telling the trucks where to go, rather than the system calculating the optimal assignment. A high number of manual assignments will likely result in suboptimal load and haul productivity. As a consequence, production, blending and other goals may not be met.
The Mine Model is MineStar's virtual representation of the physical mine, and all of its assignment decisions are based on the Mine Model (amongst other things!). Therefore, the road network needs to reflect the reality on the ground.
If waypoints are in the wrong position and trucks "miss" them (e.g., the trucks do not drive through the waypoint), it can throw out MineStar Fleet's travel time calculations. Therefore, in a similar fashion to Manual Assignments, we can measure 'Missed Waypoints' as a way of understanding how well the dispatchers are monitoring and managing the Mine Model during their shift.
MineStar Fleet: Incomplete Cycle Count
Ensuring that your mining fleet management system is producing clean and accurate cycle data is essential for all reporting, especially reconciliation. It is therefore of critical importance that the Dispatch Team monitor MineStar's Assignment Events for incomplete or incorrect cycles during the shift; trying to perform this task this retrospectively is time-consuming and prone to error. Benchmarking the team on 'Incomplete Cycle Count', where the target is always zero, is a good way of monitoring for any problems.
Understanding % Edited Cycles
Of equal importance is ensuring incomplete cycles never occur in the first place. To monitor these we like to utilise a second KPI, '% Edited Cycles'. In a well-run MineStar Fleet site, no more than around 5% of cycles per shift should need to be edited.
Sometimes the FMS requires Dispatch intervention before an assignment can be given; for example, if it is missing loaded material information. Timely intervention will ensure a truck cycle is not delayed. The time it takes for a dispatcher to respond can be measured using 'Reaction Time(s)'.
Finally, we always like to measure 'Assigned vs Actual Sink' as this determines whether the Dispatch Team are monitoring whether trucks are dumping their loads in the place specified by the assignment.
Mining Industry Software
A small caveat is that other factors, such as problems with the radio network or onboard hardware, can occasionally distort the metrics, making the Dispatch Team's performance look worse than it actually is. Nevertheless, when trended over time vs. production, the metrics are still a useful indicator of how well Cat MineStar Fleet is being utilised. On the flip-side, these same metrics can be used to justify investment in improved radio network coverage or additional fleet management technicians at a site.
Mining Industry Software: How Should the Data be Presented?
It is important that Dispatch Team get the opportunity to see how they are performing in terms of production and system benchmarking throughout the shift. We recommend using a wall-mounted dashboard that updates every few minutes with the latest stats. If any of the metrics begin to go into the red, the team will have the chance to make adjustments on the shift, rather than waiting to hear the bad news in a report generated at the end of the shift.
We also recommend using certain gamification techniques. Why not have a crew comparison report? This creates an element of healthy competition which often improves dispatchers' performance. Finally, there should be a weekly or end-of-roster scheduled report to the management team. This will allow for long-term trends to be addressed.
How easy is this to do?
A reliable system for benchmarking dispatchers does take a little time to set up properly. Different mine operating practices and system configurations all make this a very site-specific implementation. The skill sets of the dispatchers being benchmarked also has a large impact - for example, having dozens of "strict" KPIs for a greenfield site makes little sense. Once a dashboard or report is built, some coaching and change management will be required. Is this worth doing? Absolutely!
Here are some quantifiable benefits we've seen at sites recently:
Increase in truck productivity (Manual Assignments KPI)
Reduction in misdirected loads (Assigned vs Actual Sink KPI)
Increased assignment accuracy (Missed Waypoints KPI)
Improved mining reconciliation (Missing and Edited Cycle KPIs)
What’s the best way to get started?
Start with the basics, focusing on the most critical areas. A well laid-out and planned mining technology audit will quickly identify what to concentrate on as a first pass.
It is possible to get on top of very specific problems by using complex measures using assignment, cycle and production event data. But we recommend doing this at a later date, otherwise people will sink in the sudden deluge of KPIs.
We hope this blog has provided a useful overview of the benefits of benchmarking your dispatchers. We would love to hear your feedback - please contact us here!