Almost everyone today relies on one navigation app or another to get to where they are going, and not necessarily because they don’t know the way. Google Maps and Waze are essential for busy commuters who want to avoid traffic and minimize delays. Most people want to know exactly what time they’ll arrive at their final destination and what time they need to leave to arrive on time. No one wants to waste precious time in traffic, especially when we all have things to do. If the customer can do this already, they’ll expect field service providers can do the same—or better.
With service providers under pressure to meet customers’ rising expectations while balancing costs and other important business considerations, every minute of the day matters. The ability to correctly account for travel time allows businesses to make more precise commitments to customers about when their technician will arrive. And being able to find the most efficient route possible to jobs decreases idle time and allows for greater productivity throughout the day.
So today, the same underlying technology in navigation apps is optimizing field service routing and influencing critical scheduling decisions. These intelligent applications use historical and live traffic information to help service organizations improve schedule accuracy, reduce travel costs, and improve efficiency. However, the technology that makes this possible hasn’t always been here. To see how we arrived at today’s standards, let’s first take a look at how field service routing and travel time calculations have evolved over the years.
“As the Crow Flies”
Even today, many scheduling applications dispatch technicians as if they are traveling from job to job strapped to the back of a bird. In other words, these solutions don’t account for any traffic or roadblocks that are likely to get in the way.
The “as the crow flies” methodology of routing is a point to point, straight line calculation that does not take into account factors like turns, bodies of water that need to be crossed, or what type of road will be traveled. It goes without saying that this method doesn’t yield very accurate results. For example, let’s say there’s an emergency and an automated system searches for the nearest technician to respond. The options are: John who is two miles from the jobsite, and Linda who is three miles away. The system selects John because he’s closer, but doesn’t consider that he needs to cross a river with a drawbridge. Linda may be farther away, but she’s on the same side of the river and would actually get there faster.
Street-level routing is another point to point method, but it also takes into account the turn-by-turn directions required to reach the destination. It’s is a much more accurate way of calculating travel time than “as the crow flies” because it considers real environmental factors that will affect travel.
If you consider the scenario from above, John would quickly be eliminated as the nearest technician because the system would factor in the river and the extra turns he would need to take to cross the bridge.
One flaw in street-level routing is that it does not factor traffic in its travel time estimation. We all know that driving into a city center during rush hour takes much longer than it would if we were to attempt the same drive mid-morning. Recent advances in machine learning and analytics have paved the way for predictive travel, which uses historic traffic data to predict how long it will take a driver to get from point to point at different times of day. A scheduling system integrated with predictive travel takes these drive time decisions into account when creating the schedule, ensuring that technicians are spending more time with customers, and less time behind the wheel.
Live Traffic Updates
While predictive travel makes it possible to plan the schedule in advance, what happens if there is an accident on the day of service? Or a last-minute road closure? Even the best laid plans are subject to change. Paired with live traffic updates, an automated field service management solution can scan real-time road conditions for unforeseen traffic and proactively update the schedule accordingly. Jobs that are in jeopardy of being missed, can automatically be rescheduled to another resource with a clearer route to the job.
In an industry that relies on a mobile workforce, this level of travel time accuracy and routing efficiency affects everything from scheduling and resource utilization to employee satisfaction and user adoption for field service management solutions. Today, almost everyone, including field service professionals, have navigation apps on their phone. Imagine the field service professional’s frustration when—without the schedule being proactively updated based on live traffic—he realizes that he’ll be late to his next job because the back office is not aware of unforeseen road conditions. With the use of live traffic updates, the back-office is connected to the field to provide a real-time information and automation that optimizes the schedule even before the field service professional opens his navigation application.
The Optimal Combination
Ultimately, achieving optimal scheduling accuracy requires a combination of predictive travel and live traffic updates. Predictive travel helps organizations create an accurately planned schedule, anticipate traffic patterns, and flag road conditions before the day of service. Live traffic updates facilitate execution on the day of service by getting ahead of unforeseen changes to road conditions and updating the schedule accordingly. It’s only with the real-world insight and actual intelligence into travel time and routing that allows service organizations to achieve the scheduling accuracy necessary make proactive decisions.
To learn more about how live traffic updates make field service travel more efficient, watch this video.