Case Study

Production Footprint Analysis

LLamasoft Article


Put simply, the “footprint” represents the physical facility and quantity in whicheach product is manufactured, along with the capacity required to make it happen. Oftentimes demand for products shifts over time to new regions or different

quantities, and suppliers and cost structures change as well. As these changesoccur, the production footprint should also change to keep in-sync. This may mean investing in additional capacity in certain locations or perhaps completely
moving production capacity to other facilities within the network. Modeling the production footprint and
analyzing varying scenarios helps a company balance existing capacity with the investmentrequired to add additional production.


Case Example: Product Placement and Balancing

A large food manufacturer had already made investment decisions around facility locations and production footprint. The next question was how best to utilize that footprint. Over time, demand for its product evolved and migrated to different
geographical regions and
the company wanted to evaluate the impact of shifting locations from which raw materials

were source in order to provide a lower total cost. For example, if the company has 10 plants where a certain kind of product is made, where and in what quantities should the product be made, based on current raw material sourcing costs,
transportation and facility costs? By
utilizing production modeling to simply balance variables and capacity, the company uncoveredover $50 million in cost savings in just one year, without any changes to the physicalproduction footprint.


Case Example: Distribution Capacity Planning

A global apparel manufacturer has a multi-million square foot warehouse in northern Europe utilizing automated conveyors and high-bay storage systems. Even with all their sophisticatedautomation equipment, the company was experiencing significant capacity shortages andthroughput issues. To address the issue, they created a multi-time
period model to identify capacity bottlenecks within the DC and to determine the right staffing levels. Optimization

was used to propose actions to level the workload by bringing shipments forward, delaying or re-routing them to direct delivery. A short-term version of the system considers requirements week by week, while a long-term version looks forward over two years. The newsystem replaced a host of spread sheets and enabled more accurate matching of capacity to

requirements, thereby reducing costs.