Network Optimization for Post-Acquisition Long-Range Capacity Planning
A well-known global food and beverage producer had acquired a new line of snack foods. Given this addition, the company wanted to know how their current production and distribution network was positioned to meet the increased volume and when and where capacity problems could occur.
The company’s current planning was Excel-based. Besides being difficult to manipulate, the spreadsheet method drew from numerous unknown data sources and could only calculate the outcome of specific scenarios, rather than optimize a variety of alternatives. The company needed a more advanced and easy-to-use tool to perform the long-range capacity planning required.
LLamasoft® Supply Chain Guru® was selected as the tool of choice. Working together with the company’s supply chain team, LLamasoft team members built a multi-period network optimization model with existing production locations and work centers. LLamasoft’s applied research team quickly created custom constraints to meet the specific requirements of the project and ensure results were true and accurate. The model showed customer, capacity, product and demand data forecast for the next six years. Given inputs including raw material, sourcing and production costs, transportation costs, fixed operating costs and startup costs, when and where would it make sense to open new production lines to ensure all the demand for the product would be met while keeping total cost at a minimum?
Many people at the company were surprised by the optimization results, which recommended a much earlier date for new facility openings than was generally believed to be optimal. Many assumed that to wait until it was absolutely necessary to expand would be the best solution. However, by viewing detailed output graphics of the total landed costs, employees could see and understand that when end-to-end costs are considered, the optimal solution oftentimes is not the one that was predicted. Supply Chain Guru showed a potential savings of $7 million in labor and transport savings by opening facilities earlier than the company had originally planned.
Almost daily requests to change forecasts, costs and constraints were incorporated easily, while with Excel, this might have taken days. Based on the success of the project and the ease of changing data sets within the model, the company plans to use the established model for ongoing network analysis—running frequent scenarios to determine the optimal answer to what-if questions in other regions around the world.