Determining Optimal Production Capacity by Modelling the Greater Coal Network
The Customer

The customer is a coal mining company which operates a mining complex in central Queensland.

The Challenges

The client was in the process of increasing their product output through a series of de-bottlenecking exercises for their underground operations. They were also planning upgrades to increase the capacity of their coal handling preparation plant (CHPP) and the efficiency of their train load-out (TLO).

The client’s mines operate within a system where multiple producers share rail infrastructure and depend on the same haulage providers to transport coal from their TLOs to export terminals. Before investing heavily in infrastructure improvements, the client needed to be certain that the greater network would be able to handle expected increases in output.

The Solution

Polymathian used publicly available data, industry information, and the customer’s extensive experience to model the complex system using RACE, our proprietary decision support tool for rail-based supply chains.

Multiple scenarios, such as different TLO configurations and impact of external factors, were then tested within this simulated system revealing the changes and upgrades that would pay off in the real world. By the end of the six-week project, a year’s worth of cloud computation time had been used to analyse the client’s operations under many alternative scenarios.

The Benefits
Reliable insights

Educated guesses and industry estimates have been quantified and verified using data-driven analysis.

Full system overview

The client has mathematically accurate insight into current and future capacities and constraints for their operations.

Strategic planning

The client can now identify which operational improvements will result in the highest long-term value.

Improved service levels

Contracts can consider the opportunities and limitations of the wider network for more reliable order fulfilment.

Decision Support

RACE enabled planners to make more intelligent and strategic planning decisions for scenarios such as:

  • What TLO system is required to achieve target production volumes to maximise profits and ensure delivery on customer contracts?
  • Given system capacity constraints, how can ROI be achieved if a second TLO is built?
  • Which upgrades will reduce bottlenecks and achieve desired throughput?
  • How will TLO performance be affected if network throughput ramps up?
  • What are the optimal train load-out configurations, loop arrangements and recharge times?

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