Push almost always had the highest cost per ton index. 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.Įach policy was then tested to see how the cost per ton changes when variability increases. The system turned out to be very sensitive in terms of storage capacity.Īfter defining the optimal policy, complexity and volatility factors were added to the model to see the effects on service level. 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 graph shows that the Push scenario does not give any high-grade results. The world-class service level was predefined as 98%, green, and lower service levels were marked as red and yellow. 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. Using the model, sensitivity analysis was performed to define the best policy for the supply chain – Push, Hybrid, or Pull. The graphs in the model show output statistics for the supply chain and its components.
The model also includes different sources of randomness for example, strike action, weather delays, production disruption, customer demand variability, etc. 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 products got to either a hub or port by train and are then shipped abroad or sent for local distribution. 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 mining logistics process starts at the plant and mine storage facilities. AnyLogic modeling clarified the processes inside locations (ports, hubs, etc.), and showed how different elements work and interact. Allowing engineers to create a model of the supply chain, flexible and configurable as needed.
#Anylogic get parameters of enter agent software#
The wrong decisions could lead to hundreds of millions of dollars of profit loss over a 20-year period.
#Anylogic get parameters of enter agent full#
Amalgama and Goldratt were contracted to design the potash mining operations and a full supply chain for outbound logistics.īefore initiating the project, it was important to understand the bottlenecks resulting from the current simulation system, built earlier by another company. 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. It was planning to build a new potash mine and export 90% of production. One of the largest resource companies in the world, with over $80 billion in sales, decided to enter a new market.
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