Case Study · Logistics
Tighter routing and reporting for a delivery operation
Real-time routing, automated reporting, and demand forecasting cut delivery times 20% and fuel cost 16% while removing manual dispatch work.
- Client
- A regional delivery operation, 45 vehicles
- Market
- Dallas, TX
- Timeline
- 7 weeks to launch
Anonymized and illustrative of a typical engagement.
- 20%
- faster deliveries
- 16%
- lower fuel cost
- 12+ hrs
- dispatch time saved weekly
- 7 wks
- to launch
01 / The challenge
Where the time was going
- 01Dispatch was a whiteboard and a veteran dispatcher's memory. It worked until volume spiked or the dispatcher was out, and then routes got inefficient and drivers idled.
- 02Status reporting ate hours: someone manually compiled where everything was for customers and management. Demand planning was reactive, so staffing and stock were always a step behind.
- 03Exceptions, a late truck, a failed delivery, surfaced only when a customer complained.
02 / The build
What we shipped
We turned dispatch from memory into a system and added forecasting so the operation could plan instead of react.
- 01Real-time routingRoutes and order batching are optimized continuously against live traffic, capacity, and priority.
- 02Automated status and reportingCustomer and management status updates generate themselves from live data instead of manual compilation.
- 03Demand forecastingA model trained on the operation's own history projects volume so staffing and stock can be set ahead of demand.
- 04Exception alertsLate or failed deliveries escalate the moment they happen, with context, instead of surfacing as complaints.
03 / The results
What changed
The operation got faster and cheaper at the same time.
Deliveries ran 20% faster and fuel cost dropped 16% from tighter routing. Dispatch reclaimed 12+ hours a week, and the business shifted from reacting to exceptions to planning around forecasts.
- −20%
- delivery time
- −16%
- fuel cost
- 12+ hrs
- weekly dispatch time saved
“We used to react all day. Now we plan the week and the system handles the routing.”
Operations Director, delivery company
04 / The stack
Built with, and what you own
The operation owns the routing configuration, the forecasting model, the dashboards, and the data.
- AI Supply Chain OptimizationStreamline procurement, logistics, and supplier management with AI that optimizes your entire supply chain.
- Predictive AnalyticsUse AI to forecast trends, anticipate customer behavior, and make data-driven decisions with confidence.
- Workflow AutomationStreamline repetitive business processes with intelligent automation that saves hours every week.
05 / FAQs
Questions about this build
Can it forecast for our specific operation?
Yes. The model is trained on your own history, routes, and seasonality, not a generic average.
Does it replace our dispatcher?
No. It makes dispatch repeatable and resilient, and frees the dispatcher from manual compilation to manage exceptions.
06 / More
Other builds
Want a result like this for your team?
Name the work that is costing you the most time. We will map the build, show what is worth doing first, and what it costs. If there is no fit, we will say so.