A regional transport fleet operating hundreds of shipments per week was struggling with inefficient load distribution. Despite strong demand, trucks often left partially filled due to inconsistent planning and manual processes. The team lacked a clear view of historical patterns, which led to frequent empty miles, missed consolidation opportunities, and unnecessary fuel expenses.
These inefficiencies not only affected profitability, but also strained logistics staff and increased the environmental impact of their operations.
We deployed a custom machine learning engine trained on two years of historical shipment, route, and volume data. The system identified patterns in order types, routes, vehicle capacity, and delivery windows. It then recommended load combinations that maximized space utilization and reduced the number of trips needed to fulfill weekly volumes.
This predictive model was integrated into their daily planning tool, providing real-time suggestions for optimal consolidation based on upcoming orders and available assets.
By unlocking the value hidden in their historical data, the fleet operator turned inefficient load planning into a strategic advantage. With better utilization, lower costs, and greener routes, the company saw measurable impact within weeks. AI transformed their daily operations from reactive to optimized, improving both margins and sustainability.
Interested in using AI to boost your load utilization and logistics performance?
Contact us today to learn how we can help.
We were skeptical at first — AI felt like a buzzword. But seeing the case studies on QanaFlow’s site changed that. Their work with companies facing challenges just like ours gave us the confidence to move forward. The results they delivered for others weren’t just impressive — they felt achievable. That transparency made all the difference.
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