A client in the retail industry had a fleet of vehicles delivering produce at different times of day. They used third party logistics software to plan the delivery schedules, however an element of the delivery schedules that was hard to plan was the unloading time of the vehicle when it arrived at the store.
Fortunately there was a system in place for recording vehicle ignition events, GPS location, and geofencing to identify the arrival and departure times of delivery vehicles, and past schedules were available to identify the quantity and type of product delivered on each drop, which driver was in charge, and the time of day and type of vehicle used.
Using this trove of logged data I was able to train a simple regression model that would predict the unloading time of any future delivery at the time that the schedule is being generated.
This allowed the client to save money on driver overtime, disruption caused by late deliveries, and fines due to drivers working longer than their legally permitted hours.