Key takeaways
- AT&T reported using H2O to build models that aligned local technician skills with local jobs while minimizing mileage, across a fleet of 7,000 trucks.
- Time series forecasts estimate crew demand by area, job type and day, so installs and repairs are staffed before the queue builds.
- Models rank candidate technicians for each job on skill fit and drive distance; your dispatchers keep the final call.
- H2O MLOps flags forecast drift, so a model is retrained before your dispatchers stop trusting it.
Tomorrow's schedule is built by hand, every day
Every evening someone on your team builds tomorrow's schedule. They look at open installs and repair tickets, check who has which skills and certifications, and guess how long each job will take.
The guess is often wrong. Send too few technicians and appointments slip; send too many and crews sit idle or cross town for a job someone closer could have done. Job types shift with sales campaigns, weather and outages, and a fixed rule set cannot keep up with that much variation.