Case Study

With several facility leases nearing expiration, the company needed to determine optimal quantity and location for distribution centers and evaluate its CPU freight strategy

Network Optimization Project Objectives:

    • Reduce overall costs in storage, distribution and transportation
    • Reduce complexity and increase customer service by exploring opportunities to consolidate facilities and outbound lanes
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Quickly Evaluate a Large Number of Alternate Scenarios to Select Optimal Distribution Network Design


A global processor of agricultural products and food ingredients was experiencing rapid growth in its U.S. operations in California, and with several facility leases nearing expiration, it was an ideal time to evaluate how many warehouses should be in the network and identify optimal facility location to support current and future growth of the business, reduce costs and improve customer service.

The existing transportation strategy and network also needed to be scrutinized. Because of its relative small size compared to some of its domestic customers, the company relied on customer freight networks and fleet to transport more than 80 percent of customer freight and was building a lot of allowances into its pricing. Could cost be reduced and service improved by taking on some of the outbound freight and consolidating shipment locations? Could moving some shipments to rail from truckload provide another cost-saving opportunity?


With more than 2,000 products, seven production locations, 15 forward warehouses and seven cold warehouses, all operating under extreme seasonal variability; a holistic view of the end-to-end supply chain was required in order to identify opportunities. The LLamasoft® solutions team was brought in to lead and execute the network optimization project. The team began by building a 12-period baseline model to represent monthly seasonality, with cost inputs such as warehouse fixed and variable costs per pound and transportation lane costs in order to evaluate the cost impact of potential site moves.
By running a network optimization on the baseline model using Supply Chain Guru®, a number of “quick win” opportunities were identified, including reducing multiple movements of product between warehouses and limiting movement of products not yet in a finished state.

Next, 13 network scenarios representing different combinations of facility closures, consolidation and locations were identified and run in order to evaluate the impact of each.

Comparison of cost trade-offs showed that Scenario 1 provided the greatest cost reduction by reducing variable warehouse cost.


The final recommendation of the study:

  • Expand one particular facility to support on-site production to save 10 percent of warehouse and freight cost ($3.5 million annually)
  • Consolidate/close forward warehouses wherever possible based on the customer relationships
  • Explore viability of converting customers from CPU (customer pick-up) to private fleet
  • Relocating a distribution facility near a rail hub in order to ship more of its products via rail would in fact not provide the cost savings necessary to make up for converted CPU business

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