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Case Study:

Network Optimization / Global Logistics Powerhouse

Network Optimization - Global Distribution Case Study

Applying AI to optimize hub-and-spoke national distribution network

Major US distributor Relocates Transportation Hub to Deliver Faster Service, Higher Profit

Challenge:

Changing Customer Shipment Patterns Impacting Existing Network Efficiency

Original hub network was efficient and served customers well. Over time, however, new customers came on board and existing customers’ needs changed, resulting in higher costs than necessary. The management team suspected some of the hubs were in sup-optimum locations. However, quantifying the problem, and finding the solution, remained elusive, even after working with outside experts. Specifically:

  1. Reduced competitiveness: Greater distance between customers and hubs increased shipping time
  2. Operational inefficiency: Trucks and drivers were servicing longer than optimal routes, increasing the need for both
  3. Reduced profitability: Longer shipment times increased costs in the face of competitive price constraints
  4. Daunting mathematics: The toughest challenge was to effectively quantify the location problem to make it possible to optimize the hub locations.

Solution:

Teranalytics network optimization guided by industry experts

  1. Initial Optimization: The Teranalytics team used customized algorithms to analyze the given hub and spoke network, leveraging historical shipment data. This exercise identified hubs that were no longer well-positioned, recommended alternative locations, and confirmed that other hub locations remained optimal.
  2. Data matching intuition: Teranalytics early results confirmed what the company experts’ believed but had not been able to support with data. The management team decided to conduct a deep dive to further study the impact of relocating one hub that was predicted to return the maximum business and financial gain.
  3. Deep dive: Relocating hubs is both expensive and risky. To further test the outcome of the simulation, the Teranalytics team pulled extensive cargo movement and financial data related to the chosen hub. The team also customized an algorithm to evaluate the full range of location-specific constraints, demand fluctuations, and economics, providing a close fit with actual business conditions.

Results:

Customer management team confidently relocated a major regional hub, realizing significant cost savings as predicted

Unambiguous outcome: The deep-dive study solidly quantified the expected level of savings for relocating this hub, giving the Executive Team confidence to progress with moving the hub.

  1. Financial impact: Teranalytics predictions indicate that relocating this one hub will increase profitability by $1.5 million per annum while continuing to enable exceptional customer service
  2. Ability to see the big picture: The management team now has the ability to analyze their hub locations throughout the network on an ongoing basis and redefine their service regions as traffic loads evolve
  3. Positioned for greater optimization: CEVA’s next initiatives will be the analysis of their international ground transport hubs, as well as air and ocean netwoks.

Client Feedback:

“Previous consulting teams claimed to understand our business, but delivered questionable recommendations. By contrast, Teranalytics listened, learned, and customized their algorithms in complete alignment with our business. The excellent results speak for themselves.”

— Chief Operating Officer