Improving Mining Outbound Logistics with Agent-Based Simulation Modeling

Improving Mining Outbound Logistics with Agent-Based Simulation Modeling


One of the largest resource companies in the world, with over $80 billion in sales, decided to enter a new market. It was planning to build a new potash mine and export 90% of production. They wanted to design a reliable supply chain, with high speed replenishment, and the ability to recover, or even benefit, from disasters, both natural and man-made. Amalgama and Goldratt were contracted to design the potash mining operations and a full supply chain for outbound logistics.

Before initiating the project, it was important to understand the bottlenecks resulting from the current simulation system, built earlier by another company. This old system did have some benefits; however, the model behaved like a black box and produced results without reasoning, they could not be queried. The new project, with simulation modeling, was to visualize the supply chain processes and bring confidence to results, helping:

The wrong decisions could lead to hundreds of millions of dollars of profit loss over a 20-year period.


The model was required to:

AnyLogic simulation software fulfilled these requirements. Allowing engineers to create a model of the supply chain, flexible and configurable as needed. AnyLogic modeling clarified the processes inside locations (ports, hubs, etc.), and showed how different elements work and interact.

The mining logistics process starts at the plant and mine storage facilities. After the products are mined and ready to be transferred, a decision is made whether to ship the product to an export channel or keep it for the domestic market. The products got to either a hub or port by train and are then shipped abroad or sent for local distribution.

Mining Supply Chain Simulation Modeling

In the agent-based model, sea ports and mines, as well as trucks, trains, and vessels, acted as stand-alone agents, interacting with each other. The model also includes different sources of randomness; for example, strike action, weather delays, production disruption, customer demand variability, etc. The graphs in the model show output statistics for the supply chain and its components.

Mining Logistics Simulation Model

Using the model, sensitivity analysis was performed to define the best policy for the supply chain – Push, Hybrid, or Pull. The analysis considered adding rail cars into the system (from 2.5 thousand up to 5.5 thousand rail cars), changing the amount of storage capacity at the mine and ports (from 150 thousand up to 500 thousand tons), and altering the service level. The world-class service level was predefined as 98%, green, and lower service levels were marked as red and yellow.

The graph shows that the Push scenario does not give any high-grade results. The hybrid scenario provides the required level of performance; however, it is better provided with the Pull policy, using 3,500 rail cars of 300-kiloton capacity or 4,500 rail cars with a 250-kiloton capacity. The system turned out to be very sensitive in terms of storage capacity.

Mining Agent-Based Simulation Model

After defining the optimal policy, complexity and volatility factors were added to the model to see the effects on service level. The Push policy was negatively impacted by adding new products, customers, hubs, or ports, whereas with the Pull strategy, high service levels were maintained regardless of any factors.

Each policy was then tested to see how the cost per ton changes when variability increases. Push almost always had the highest cost per ton index. However, the graph shows that as volatility and complexity rise, the cost per ton also increases, over time, for Pull.

Finally, the results were compared against each other using different parameters (service level, working capital, stock in hub and port, etc.), and the policies ranked.


AnyLogic simulation modeling visually represented the supply chain processes and proved the Pull policy as optimal. This policy provided a higher level of service at a lowest cost per ton, with lower working capital and investment requirements at the same time. It also showed how the additional storage capacity would help. Other major benefits with the Pull policy are:

Supply Chain Sensitivity Simulation Analysis

The Push policy, applied by the company before, provided a poor level of service because it did not consider demand variability. The company used a multiproduct supply chain, and when customers started demanding a product, it could be missing due to the lack of free storage space. The Pull policy algorithm acts differently. It decides when to safely reduce stock or increase it, depending on demand, without incurring a penalty.

The model capabilities include:

The latter provided detailed results for various model parameters. For instance, the difference of Delta cost per ton/sold for Push and Pull policies was three dollars a ton. At 13 million tons per annum, this would mean $39 million of net profit loss if the wrong policy was chosen. For the Tons Sold parameter, there was a 4.1 million-ton difference between Push and Pull policy results, when using the same capacities and volatility. Multiplied by $300 per ton, this would translate to 1.2 billion dollars of revenue loss from the wrong choice of policy.

When the analysis was presented at the executive level, the pull strategy was chosen for business development.

Project presentation by Dr. Alan Barnard and Dr. Andrey Malykhanov

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