How AI Turns Delivery Data into Profitable Routes
How AI Turns Delivery Data into Profitable Routes
We all know that artificial intelligence (AI) can help us find the “fastest route”. But with margins shrinking and customer expectations rising, distributors are looking at more ways AI can improve profitability.
Here’s how businesses are using AI to build realistic routes that enhance customer experience and reduce cost per stop.
When “Good enough” Routes Are Not Good Enough
Do you find that routes look good on paper but leak costs when it comes to execution?
Often, the root of the problem is inaccurate service time predictions, which leads to one of two scenarios:
- Routes are overscheduled:
Estimated arrival times (ETAs) are inaccurate and labour costs are unpredictable. - Routes are underscheduled:
The cost per stop is high and you miss the opportunity to serve more customers.
Either way, deliveries are needlessly expensive and customer retention suffers.
Distributors can address these issues by putting data to work, rather than relying on static assumptions.
Protect Profits with Reliable Routes
Service time is the amount of time it takes to stop and serve each customer. For small parcels, that might be less than a minute. In wholesale delivery, duration can vary significantly based on order volumes, product type, account type and added services.
A lot of distributors estimate service times based on gut instinct or historical averages. But a static “fifteen minutes per stop” doesn’t cut it when some deliveries need 60 minutes or more.
Machine learning (ML) can model service times based on historical stops. The result is realistic estimates that vary based on account, order volume and driver skillset, and other factors.
With reliable service time estimates, distributors can build balanced routes that serve more customers at a lower cost per stop.
Retain Customers with Dynamic ETAs
While accurate service times provide a realistic starting point, ETAs can shift once deliveries are underway.
Even so, wholesale customers expect reliable updates throughout the day. Businesses rely on arrival estimates to plan labour, manage inventory and keep service running smoothly.
When providing updates, the trick is to balance precision with accuracy. A highly specific ETA is useful only if it’s correct.
AI-driven models use historical and real-time data to refine ETA predictions as the driver executes the route:
- Early in the day, businesses can provide a broader delivery window (“Your driver will arrive between 13:00 and 15:00”) with a high level of confidence.
- As the route progresses, it’s possible to provide a more precise estimate (“Your driver will be with you in 30 minutes”).
These dynamic ETA updates keep customers in the loop, without making untenable delivery promises.
Conclusion
The point here is not to layer on exciting new technology for its own sake. It’s to make better use of the data distributors already generate every day. Service times, geocodes, and actual driver behaviour provide untapped signals that can feed into route planning and execution.
By applying ML to this data, businesses are making practical improvements that increase profits over time.