Optimizing Dispatch: Artificial Intelligence and Actual Intelligence at Work the use of IoT sensors increases, field organizations can be alerted to a problem before the customer even realizes that something is amiss.
uses AI and ML to handle job planning, scheduling and execution, field service technicians encounter less downtime and fewer work disruptions and are consistently assigned jobs that match their skill sets—which can improve productivity by up to forty percent.

When these technologies are strategically applied to connected equipment and sensor devices, valuable data about performance, environmental conditions and more is constantly transmitted and processed. ML can analyze the collected data to preemptively identify issues before they even occur, avoiding downtime and saving time and money for businesses and customers.

Providing a differentiated customer experience

The most experienced dispatchers and service managers still confront a limit to the number of variables they can consider when making scheduling decisions. With AI capabilities, however, calculations and changes are instantaneous, adjusting in real time to minimize disruptions and maximize the organization’s desired outcomes and KPIs. The majority of these changes and problems are addressed in the background without the need for human intervention. This level of automation enables an organization to deliver an optimized and differentiated customer experience as compared to a field team that relies solely on manual processes.

AI can also improve customer communication, which is key to a good experience. AI enables your team to share an accurate arrival time based on current travel conditions as well as send details about the technician and his real-time status and location. This information keeps customers from feeling “in the dark” about the appointment and eliminates variables that can result in customer no-shows and last-minute cancellations.

Lastly, as the use of IoT sensors increases, field organizations can be alerted to a problem before the customer even realizes that something is amiss. An alert can be sent to your FSM system, allowing for the schedule to automatically be adjusted in real time to dispatch a qualified service technician while also filling in any gaps that might occur as a result of this change. While providing such seamless service enhances the customer experience, it also enables field organizations to develop new revenue streams by selling monitoring services or up-time guarantees. Also, studies have shown that customers are actually willing to pay more for a better experience, so this might provide some pricing flexibility for your organization, helping to stabilize margins.

Hope for the best, but prepare for the worst

Delivering on a service request means having to deal with the unexpected. Factors like last-minute cancellations, sick calls, changing weather conditions, and shifting traffic patterns will always remain out of your control—and will inevitably impact field service operations. While these variables can’t be eliminated, they can be managed through technologies like AI and ML. True AI and machine learning enable a scale and speed of schedule and dispatch optimization otherwise impossible to attain with mostly manual processes.

The benefits of schedule automation through the use of AI and ML are real and only get better over time. The constant stream of inputs and refinements—and the feedback loop created by adherence to or deviation from the optimized routes and schedules—teaches your system to make better decisions in the future. The more data you provide, the more refined and focused your operations will become over time. The ability to run simulations and crunch massive amounts of data empowers service leaders to test-drive process changes and weigh variables to understand what will work best. So, what are you waiting for?


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