![]() Then the team could design the whole international supply chain and determine the optimal location of the DC, taking all agents into account. The reports on the model runs for each DC could be exported as an Excel file.Īfter the experiments, the model was extended with the following agents to adjust for an international supply chain: The scenario's results showed about a 45% cost reduction in the supply chain. Scenario #4 included the original DC, rerouting products through another already existing DC on the west coast and a new DC in an optimal GIS-location. In Scenario #3, the network with the original DC was incremented with a new DC in optimal GIS-location. The scenario resulted in a 42% cost reduction. In Scenario #2, the original DC was used, rerouting the shipment to other existing DCs, located on the east and west coasts of the US. Scenario #1 included the original DC and outlier customers, and did not show shipping cost reductions regarding products distributed from the DC. This experiment is available at anyLogistix, the multimethod software for supply network optimization, design, and analysis. For this purpose, the developers conducted supply chain Greenfield analysis. This could be a reason for additional transportation costs, which is why the team estimated an optimal location for new DC regarding the wholesalers' disposition. From there, the cargo was distributed to wholesalers directly or via transit DCs. The company’s head DC was located in the south-east US. To estimate and visualize the existing transportation routes, the team used AnyLogic GIS capabilities, which linked the agents of the model to their locations. Last year demand indicators were used as model input. The team also considered shipments the customer received per year and the demand per shipment in units per customer. These distributors used truckloads and rails. When building the model, the team first looked at shipment data, focusing on the locations of high demand customers, as they made up 85% of all the company’s shipments. It enabled abstraction, simplifying a complex system by focusing on relevant details and estimating them. The team found advantages of applying simulation at different stages. The input data (number of DC, suppliers, etc.) was different for each variation. With AnyLogic, it was easy to test multiple scenarios and their variations. Trucks and trains (location, units, owner, destination).Customers, or wholesalers (location, demand rate, total shipments, distance, shipment type etc.).Distribution centers (location, number of units, overhead cost, startup cost).The team put the description of the supply chain into the model, and defined the following as interacting agents with their own goals and rules, individual behavior, and interaction policies: No small packages or end-users were involved in this case.ĪnyLogic simulation software was chosen due to its agent-based modeling capability. Focusing on wholesalers, the company considered distributors as customers. The contractors decided to simulate the whole supply chain in order to visualize DC locations on a GIS map, and the supply networks between them. The company was expanding, and the executives wanted to know if it would be beneficial, in terms of shipping costs, to add a new distribution center (DC) in the US, or to redistribute products to a pre-existing DC. Fruit of the Loom (FOTL) is one of the largest US apparel manufacturers and marketers.
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