Case Study

Global Electrical Supply Company Uses Route Optimization and Hub Selection to Identify Over 10 Percent Annual Cost Savings

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Global Electrical Supply Company Uses Route Optimization and Hub Selection to Identify Over 10 Percent Annual Cost Savings

Challenge

A large global provider of electrical supplies wanted to expand its U.S. network and needed a tool to analyze current and potential future network configurations to understand potential savings opportunities to build the business case. They needed to gain a better sense of what was and was not working in the current network in order to pinpoint and prioritize opportunities for improvement. The company decided to first focus on its Florida network, a key region for the business. They sought to first identify optimal hub locations for each Florida region, determine the optimal number of trucks to operate at each of the hubs, and identify a list of zip codes that each truck would service on their
daily routes.

Solution

The company used LLamasoft transportation optimization to identify areas of improvement. Analysts first ran a baseline model of the median week as a starting point for analysis. Within the large Florida network, they broke down six sub-regions to identify the hub location for each sub-region. They ran multiple fleet optimization scenarios to determine the optimal cross dock location by region. In order to create a model for each region, they utilized LLamasoft’s visual data analytics tool, Data Guru®, to create a project for each region. The tool was essential for automating the model-building process which led to more rapid analysis.

Using new hub selection and pool modeling technology, analysts broke down their hub and fleet into appropriate locations. They were able to identify possible hubs as well as the optimal number of trucks for a given route. Additionally they analyzed both the peak shipment periods versus a median activity week along with the number of trucks needed on a daily route. The analysis showed that four of the pool sites identified in the baseline model could be consolidated into one. This would decrease both the number of miles driven and trucks needed without impacting service levels.

Results

By reducing the number of trucks utilized in the fleet as well as the number of miles driven, the company was able to identify potential savings of 11-20 percent annually. Seeing this level of potential savings in just one region in the network, the company is now conducing similar analyses throughout its operations to drive further improvements.

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