Enhancing Field Service Decisions using Machine Learning
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Man doesn’t possess the ability to gain information about an object, person, location, or physical event through extrasensory perception. However, advanced machine learning (ML) has made it possible. It enables computers to take decisions when unexpected situations arise; by recognizing patterns based on gathered and stored information. Amongst a host of industries leveraging its manifold advantages, field service management is one arena that is leveraging machine learning to find out the shortest route for service, improve scheduling and dispatch, ensure driver safety amongst others. For example, during morning hour rush it takes a technician twice as long to get a service appointment, but AI/ML can tell him exactly how long will it take and the shortest route to get there. Machine learning, in fact, removes the biases and ‘gut feeling’ human beings are vulnerable to.
ML is becoming the mainstay in field services as they empower organizations with predictive insights and data-driven decision making, resulting in better service.
Field Service decisions become easier and smarter with Predictive Field Service (PFS) powered by machine learning. PFS draws conclusions from data that allows service businesses to get ahead of interventions and problems before it occurs.
The Field Service Industry continued to transform rapidly in 2017 with emerging technologies like the Internet of Things (IoT) and predictive analytics that increasingly changes the way field service suppliers manage their workload. And this year will continue to bring changes and innovations where new technologies reshape the service operations and delivery.
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