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SIM Risk Analysis

We consider a supply chain comprising:

  • Shoes supplier in Qui Nhon
  • Port in Vung Tau in Vietnam, through which the products are shipped
  • Port in Kobe
  • 2 sites located in Itami and Tokyo
  • 50 customers in the largest cities of Japan

The demand is proportional to the population of the cities.

Supply chain operations are tightly connected with risks and uncertainties, which must be considered for proper strategic supply chain design and planning. This complicates the planning process and sets high standards for the agility and robustness of the supply chain. The risks must be identified to properly design a delivery network, define policies, and plan actions in case of an emergency.

Question

What will happen if the rainy season starts in Vietnam, production suspends or demand changes?

If you want to stress-test the supply chain in emergency situations you can assess particular “what-if” scenarios with the Risk Analysis experiment.

Result and answer

The result contains estimated risks, allowing you to design a network that will minimize possible losses and increase supply chain resilience.

If the rainy season starts in Vietnam, production suspends, or demand changes, then the Service Level will drop.

In the History by Replication statistics we can observe the replications with low service levels. We will further analyze the data on replication with the worst service level.

Now we will analyze data in the Events and Recovery tab.

  1. In the Events Table statistics we can see that three events took place:
    • Factory failure (on the 138th simulation day)
    • Increase in demand (on the 174th simulation day)
    • Raining season (on the 201st simulation day)
    The Factory later recovered, the raining season ended, but the demand never decreased.
  2. In the Recovery Time statistics we can see that the service level dropped below the Failure service level (defined in the parameters of the experiment) on the 211th day of simulation, and never recovered

By thoroughly analyzing the data on total cost, revenue, profit, etc., which is available in the corresponding tabs below the currently open Events and Recovery page we can receive more accurate information revealing the actual reasons for the decrease in service level.

This information can be used to improve the scenario input and avoid risks.

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