A leading producer of avocados is experiencing a dramatic increase in demand throughout
their North American market. As the company grows to support the spike in consumer
demand, they must evaluate their current network and determine how to best serve their
customers. The company seeks to minimize costs while maintaining a high level of service
by evaluating the current distribution centers, potential locations for expansion and border
crossing points. Additionally, the company anticipates a shift in regional demand, and would
like to incorporate that projection in their network design project.
With continued growth on the horizon, the company’s traditional method of using
spreadsheets to perform network analysis is no longer sufficient. The company determined
that they need a tool to design an effective supply chain network that can support both short-term
and long-term growth. Using LLamasoft Supply Chain Guru, they were able to do just
The LLamasoft team partnered with this produce company to ensure a successful project.
Using Supply Chain Guru network optimization, they were able to visualize the company’s
current network footprint and determine where potential cost savings opportunities may
lie. The company identified three regions of demand and used the projected growth in each
region to scale their short-term demand model. With the help of the LLamasoft team, they
then ran between 70 and 100 network optimization scenarios with varying distribution center locations, border crossings, and service levels with the aim of sharing five or six with the company’s leadership team. The LLamasoft team helped the company understand their
current DC capacity, provided candidate locations for future expansion and identified the
impacts of closing an existing DC or opening a new one. The team also performed a risk
analysis by looking at existing border crossing locations and running scenarios using alternate locations to gauge the impact that relocating border crossing points would have. Lastly, they evaluated the effect that changing service levels would have on the overall network costs. By running scenarios at various levels, they determined that there was not a dramatic cost reduction associated with lowering their targeted level of service.
The network optimization project revealed that the company could save between $11M
and $12M in overall costs. Through eliminating redundant intercompany transport that
was uncovered by the network optimization and utilizing additional ocean transport, the
company can dramatically reduce its transportation and distribution costs, while maintaining its 90 percent service level. In addition to cost savings, the company now has insight into alternative DC locations to get out of unfavorable situations that they face with their current DCs, or to use for future expansion.