Apparel Company Chose Location for New Distribution Center Using Simulation Modeling

Problem:




Fruit of the Loom (FOTL) is one of the largest US apparel manufacturers and marketers. 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) on the east/west coasts of the US, or to redistribute products to a pre-existing DC. The contractors decided to simulate the whole supply chain in order to visualize DC locations on a GIS map, and the supply network between them. Focusing on wholesales, the company considered distributors as customers. No small packages or end-users were involved in this case.


Solution:




AnyLogic simulation software was chosen due to its agent-based modeling capability. The team fit 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:

  • Producers (Cotton farmers)
  • Processors (Yarn mills)
  • Companies (FOTL distribution centers)
  • Wholesalers
  • Retailers


With AnyLogic, it was easy to test multiple scenarios and their variations. The input data (number of DC, suppliers etc.) was different for each variation.


The team found advantages of applying simulation at different stages. It enabled abstraction, simplifying a complex system by focusing on relevant details and estimating those.


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. These distributors particularly used truckloads and rails. The team also considered shipments the customer received per year and the demand per shipment in units per customer. Last year demand indicators were used as model input.


To estimate the distance from DC to distributor, the team used GIS, which linked the agents of the model to their locations. Visualized data helped see the routing currently occurring throughout the supply chain and create more efficient routes.

The model presented a supply network and comprised the following agents with given parameters:

  • Distribution centers (location, number of units, overhead cost, startup cost)
  • Customers (location, demand rate, total shipments, distance, shipment type etc.)
  • Trucks and trains (location, units, owner, destination)

In order to minimize costs and determine optimal DC locations, location-allocation analysis was performed with several scenarios. It was based on customer demand, weight, and distance from the DC to the customer. Now AnyLogic has a supply chain and logistics specific tool called anyLogistix which tackles network optimization more efficiently.


Scenario #1 included the original DC and outlier customers, and did not show shipping cost reductions regarding products distributed from the DC. Then, the original DC was replaced with a new one at the same location. The variation concluded a cost reduction of $6000.


Supply Chain Simulation Modeling

In Scenario#2, the original DC was used with rerouting the shipment to other DCs, located on the east and west coasts of the US. The scenario resulted in a 42% cost reduction.


Supply Chain Computer-based Modeling


In Scenario#3, the network with the original DC was incremented with a new DC in GIS-location, defined by location-allocation analysis. Cost reduction appeared as 32%.


Supply Chain Simulation Software


Scenario#4 included the original DC rerouting products through another already existing DC and a new DC in optimal GIS-location. The scenario's results showed about a 45% cost reduction in the supply chain.


Agent-based Simulation Software


The report on the model runs for each DC could be exported as an Excel file.


After the experiments, the model was extended with the following agents to adjust to international supply chain:

  • Manufacturers
  • Additional distribution centers
  • Loading ports
  • Ports of discharge
  • Vessels
  • Additional trucks


Then the team could design the whole international supply chain and determine the optimal location of the DC, taking all agents into account.


Outcome:




While decisions made on people’s expertise, without diving into the data, may be considered as biased, data-driven insights from GIS and AnyLogic, paired with business knowledge, helped develop supply chain recommendations.


For this case, simulation modeling was used as an exploratory research tool to assess the suggestions on placing DCs throughout the country and prove their economical effectiveness in terms of manufacturing and customer’s supply chain.


The developers highly rated AnyLogic user-friendliness, which allowed them to drag and drop the elements and customize or expand the model to any conditions, which would benefit Fruit of the Loom’s international business.

Project presentation by Elizabeth Tyrie, Data Science Manager

 

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