Driving Savings from Wegmans’ Transportation Network
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- Blue Yonder’s transportation modeling
Tech Stack
- Transportation modeling
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Retail
Applicable Functions
- Logistics & Transportation
Use Cases
- Fleet Management
- Supply Chain Visibility
Services
- System Integration
- Data Science Services
About The Customer
Wegmans is a family-owned supermarket chain headquartered in Rochester, NY. With over 100 stores across the northeast and midAtlantic states and almost $10 billion in revenue, Wegmans is both one of the largest private companies in the U.S. and a customer favorite for its European-like open-air market displays and broad selection of organics, vegan offerings and restaurant-quality prepared foods. They pride themselves on a “near telepathic” level of customer service. To get all of those fresh, frozen and dry foods and ingredients to their network of stores, Wegmans employs a private fleet.
The Challenge
Wegmans, a family-owned supermarket chain with over 100 stores across the northeast and midAtlantic states, operates a private fleet to transport fresh, frozen, and dry foods and ingredients to their network of stores. However, with a network of this size and complexity, Wegmans wondered if they were getting the most utility possible from their fleet investments. The challenges they faced included tight store delivery windows, which limited opportunities for order consolidation or routing options, routing restrictions by type of commodity being shipped, and virtually no visibility to cost savings available from the use of backhauls.
The Solution
Wegmans turned to logistics consulting firm, enVista, and Blue Yonder’s transportation modeling capabilities to help them model potential transportation strategies. Blue Yonder’s modeling was utilized to model the complexities of Wegman’s transportation network. Its extensive functionality enabled enVista to accurately capture multiple constraints and parameters in Wegman’s network and allowed efficient and accurate future state analyses. enVista used Blue Yonder’s transportation modeling capabilities to measure the impact of adjusting the delivery windows by three hours to enable order consolidation and routing opportunities to be evaluated. They also used the Blue Yonder modeling capabilities to analyze different combinations of commodity groups during routing while still abiding by restrictions such as maintaining product temperature and humidity limitations that required certain types of vehicles. Finally, Blue Yonder’s modeling capabilities enabled enVista to analyze Wegmans’ current and potential backhaul vendors to see where additional opportunities for cost savings might exist.
Operational Impact
Quantitative Benefit
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