Schedule Optimization

Optimizes job scheduling from historical task data so teams can assign work faster and more accurately.

Business impact

• Schedule accuracy — The article says the system provides more accurate predictions for job duration and cancellation likelihood, supporting more accurate schedules.
• Service speed — The article reports faster and more efficient service.
• Customer experience — The article says the company improved customers' experience by helping solve problems quickly.

Data requirements

• Historical job data (tabular) — Used to train predictions for scheduling.
• Historical task data (tabular) — Includes task duration and cancellation likelihood.
• External sources — The article says the predictive field service solution combines historical data with external sources.

AI methods and techniques

• Machine learning — Used to analyze historical jobs and tasks and generate more accurate predictions for scheduling.
• Predictive analytics — Used to estimate task duration and cancellation likelihood before the schedule is finalized.
• Decision-making algorithms — Used to automatically produce the best possible schedule across many jobs, resources, and locations.

Industries

Real-world evidence

2 documented case studies on record.

Companies using this: AstraZeneca, Suramericana.