- 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.
How field dispatch optimization works
Your operations team trains models on your own job history and feeds the predictions into the scheduling system you already run.
Step 1: join the history
Combine completed jobs, open orders and tickets, technician skills and certifications, shift calendars, job locations and weather.
Step 2: forecast demand
Forecasting with time series models estimates how many jobs of each type each area will need, by day, so you size crews ahead of time.
Step 3: predict each job
Regression models estimate job duration, and classification models estimate the chance a job is finished on the first visit.
Step 4: rank technicians
For each job, the models rank candidate technicians on skill fit, predicted duration and drive distance.
Step 5: write back and decide
Predictions go to your scheduling system as scores. Your dispatchers review the proposed plan, change what they need to, and approve it.
Step 6: watch for drift
H2O MLOps tracks model drift and flags a forecast for retraining before it quietly goes wrong.
Results
AT&T, an H2O customer, described a fleet of 7,000 trucks whose technicians had different skills and worked at varying distances from the day's jobs, with a service mix that changed daily. AT&T reported using H2O to build models that aligned local skills with local jobs while minimizing mileage, staffing the right technician on the right job.
Your result depends on your job mix, territory and data, so the first model is tested on your own past schedules.