Finding a 58% improvement in mining productivity using Theory of Constraints

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This paper provides a case study of how TOC to drive operational improvement of a large underground mining complex.

Our case study project comprises feed from both a large open pit mine and large underground mine. The underground material is of a substantially higher grade than the open pit material. Because of this the operations preference is to process as much of the underground material as possible, which in turn puts a lot of pressure on the underground operation to generate as much ore as possible . Therefore the underground mines capacity to produced ore is considered the constraint of the operation and, if improved, would improve the overall system. The study focussed on the flow of ore, waste, and backfill through the underground mine from the various sources to the final destinations. A basic material flowchart is presented in Figure 1.

Material flow in mine
Figure 1   Material flow in mine

A number of opportunities to improve the underground mining productivity were identified and modelled using Discrete Event Simulation to quantify the potential improvement, measured in tonnes per day. Based on the material flow modelling most opportunities focused on improving the productivity of the underground trucking fleet.

Because policy and other changes cannot easily be carried out in busy, complex underground mines the key to the success of this project was producing an accurate model of the existing system which specifically models the key processes and their interactions so that proposed changes can be evaluated with confidence. In the case study almost half of the time was spent making observations of the current mine and building a model that could appropriately reproduce those outcomes, only after this was complete could TOC be applied.

Step 1 – Identify the constraint

As is often the case in underground mining the complex interaction of processes meant that the inspection of the mining operation found no immediately obvious constraint. In this instance, it is useful to select a likely constraint (e.g. truck utilisation) and work through the process. This will typically either expose policy constraints which will either confirm the constraint or make another constraint obviously apparent. In this case, underground truck haulage was selected as the constraint.

Step 2 – Exploit the constraint

A number of opportunities were identified to exploit haulage. These were:

  • Routing: As there were two possible mine exit points (a shaft and a portal) trucks were strictly routed to the shortest cycle time alternative.
  • Payload: The trucks weren’t being loaded to capacity. By installing weightometers on the loaders to ensure the trucks were fully filled, the tonnes per cycle were increased.
  • Shift length: A range of options were considered to reduce the length of the pre-shift meetings and prioritise transport of loader and truck operators at shift commencement.

In combination, these opportunities were modelled to increase system productivity by 18%.

Step 3 – Subordinate to the constraint

In addition to exploiting the constraint, there are a number of downstream and upstream processes that could be subordinated to further enhance productivity:

  • Traffic system: Analysis of truck time allocations found a significant portion of the shift being spent waiting for other traffic to clear in the decline, including waiting for light vehicles. There is an opportunity to change the traffic system to prioritise haul trucks.
  • Surface bins: Currently at the surface, the skip needs to wait until a surface truck is available before dumping the load. Installing surge bins allows this to be decoupled with an increase in skipped tonnes per day and decreases truck delays
  • Shaft uptime: Currently, the shaft has downtime while materials are delivered. Re-routing materials through the portals meant that shaft downtime was reduced, so trucks could remain on the shorter hauls to the shaft rather than being re-routed to the portal during material movement runs.

In combination, these opportunities were modelled to increase system productivity by a further 15%.

Step 4 – Elevate the constraint

The final opportunity analysed was the purchase of additional trucks. The purchase of four trucks was found to increase system productivity by a further 30%.

Step 5 – Repeat

It is likely that such an increase in productivity would mean that the constraint would move to loading, development or drill and blast.


Snowden demonstrated the productivity of proposed changes through discrete event simulation, initially configured to recent actual production. The overall impact of this approach was a 58% improvement in productivity for a relatively minor capital investment. Approximately half of the improvement could be gained with also no capital.

Productivity improvement
Figure 2   Productivity improvement

If you would like to discuss improving your project through application of TOC, please contact us at .

In the next post we describe the Theory of Constraints (TOC) and its potential to benefit mining projects as an “out of the box” way of thinking.

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