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Optimizing Dispatch: Artificial Intelligence and Actual Intelligence at Work

By: Paul Whitelam

In today’s competitive business environment, product differentiation is becoming more and more difficult. Service has become a critical way to stay ahead of competitors. As companies increase efforts to distinguish themselves based on customer service, the experience provided by a mobile workforce becomes increasingly important. While there are various aspects of field service that can tip the scale toward success or failure, the largest opportunity to increase the productivity of your human capital lies in making sure that the scheduling and dispatch of your mobile workforce are optimized.

There is a lot riding on making the best decisions for scheduling. The choices you make impact every interaction with your customers or the assets you are servicing. And margins are under pressure due to multiple factors: the need to accommodate VIP customers with urgent problems, unexpected traffic, issues with site access, and customer cancellations, as well as many other challenges that arise on the day of service. Even the best-planned schedules are invariably disrupted.

Regardless of the challenges, scheduling still represents an area where companies can achieve a major positive impact on their bottom lines by improving the efficiency of the scheduling and dispatch process. In this quest for efficiency, there are two levers to consider. The first is automation—that is, making decisions in an automated fashion that improves response times and can reduce overall labor costs. The second is the use of machine learning to analyze historical data to make better predictions creating optimal routing and scheduling decisions.

Achieving real results from automation

Having the capacity to automate scheduling decisions can be a game-changer for field service teams. By using artificial intelligence to immediately identify the optimal resource allocation, organizations are able to dispatch jobs in a way that maximizes the chance of a first-time fix, ensuring customer satisfaction, and also reducing the cost of service by minimizing travel time and other elements. The ability to continually optimize a schedule as service requirements change also has a major payback. For instance, instead of leaving white space in the schedule when a customer cancels, an automated system will assign an alternative task immediately rather than leave a resource idle. This leads to improved productivity and more satisfied customers.

Another way automation delivers tangible benefits is by understanding the particular urgency of work and SLAs. This way, if an emergency comes up, low-priority work like preventative maintenance can be automatically rescheduled to another time within the SLA window without adversely impacting customer experience.

Machine Learning powers automation

While optimal automation cannot happen without sophisticated artificial intelligence, there is an additional advantage that can be delivered through the use of Machine Learning.

Machine learning (ML) is a type of AI that uses historic data to improve the quality of decision-making without explicit programming. One of the greatest attributes of ML is its ability to process large amounts of data from many different sources in ways beyond the limits of the human brain. 

Through ML, organizations have the ability to use data about previous disruptions to help with future planning. For example, ML can analyze historical weather conditions throughout the year and, at times when there is a higher probability for snow, the system can schedule lower-priority jobs to preemptively mitigate scheduling disruptions should there be a storm and resulting cancellations. With a solution that



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