Worked example · Logistics
Every day, orders came in by email, through customer portals and over the phone. Two transport planners typed them in by hand, planned the routes and rang the drivers. Now the AI reads the orders, suggests routes based on the firm’s own rules and sends each driver their list. The planners check it and sign it off.
A worked example, pieced together from typical projects. Names and details are generalised, and the figures are typical values.
30 minutes: where do the orders come from, how does a route get planned today, and what happens when something changes?
Clarity on whether it’s worth it, and where
The next day, a first prototype reads a real day’s orders and suggests a route.
Early proof instead of slides
We sit in with the transport planners for a morning and collect the rules: loading times, vehicles, customer time slots. We put hours and euros against every step.
A plan covering effort, savings and payback
Orders from emails and portals are captured and collected automatically, with a flag wherever something’s missing.
No more retyping
Route suggestions based on your rules, driver lists on their phones and training for the planners.
Handed over for day-to-day use
Once a month, 90 minutes on what’s working, where things are stuck and what will pay off next. Then you get the monthly report.
Optional, minimum term 6 months
Example calculation
10 daily plans a week, 20 instead of 90 minutes each
Pays for itself inabout 7 months
Hourly rate: what an hour of work really costs, including holidays, sick leave and employer on-costs. Value per year: hours saved × hourly rate × 46 working weeks.
The plan is ready sooner, and drivers set off on time more often.If one of the planners is off, a colleague can take over: the rules are in the system.Changes are slotted in within minutes, and every driver knows about them.Let’s take the next step. Now.
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