Why Predictive Travel Brings More Value to Field Service
Humans can’t predict the future. That’s why even the most fleshed-out dispatch schedule doesn’t always work out. It’s often the expected but unpredictable issues, such as traffic and severe weather conditions, that slow down field service.
Fortunately, there’s technology that can make predictions for us. Predictive-travel uses historical time-of-day and day-of week traffic data to estimate future travel times. With more accurate predictions, you can schedule your technicians for the maximum ‘realistic’ jobs per day. And it’s done well in advance of the service day. This makes business more efficient and leaves customers satisfied. Real-time traffic is valuable, but only relevant on the day of service. It’s not relevant when planning several days or weeks ahead.
Perhaps it’s time to trust in artificial intelligence (AI) to do what humans can’t. Use predictive travel to bring more value to field service.
Here’s just three reasons why you should:
1. Reduction in Travel Time & Increase in Fuel Economy
Predictive travel creates the best routes for field technicians, based on the likely traffic patterns. More critically, it determines the best time to schedule jobs based on travel predictions.
This reduces both travel time and miles traveled. And less mileage means less fuel used. Just think of the money you’ll save on fuel alone with an optimized route.
Travel Time Reduction & Fuel Economy Increase in Los Angeles Study, With and Without Traffic Information
(Source: Fastest Route Guidance Systems, NISSAN Motor Systems 2011, http://www.nissan-global.com/EN/TECHNOLOGY/OVERVIEW/drgs.html)
Studies show that predictive travel improves these factors better than other methods. For instance, Nissan Motor Systems studied data from driving in Los Angeles, California. It compared drivers with no updated traffic information to drivers who had the historical and real-time information available. Travel time was reduced by over 16 percent, while fuel economy increased about eight percent.
So why spend time and money that you don’t have to?
2. Greater Workforce Efficiency
Now think of the affect that a reduction in travel time has on other aspects of field service. Time saved from travel to one location means more travel time to another. Now you can reduce time spent not working by putting your technicians on more jobs per day.
Likewise, artificial intelligence (AI) can prioritize jobs based on predictions and boost scheduling optimization. Let’s say your AI predicts heavy traffic one morning on the way to a job. Meanwhile you have other tasks to complete closer by. Instead of waiting in heavy traffic to get to a site, your AI can prioritize one of your other tasks. It can then schedule the other job to another field service professional later in the day when traffic eases up.
Now the workforce runs more efficiently. Field technicians arrive at the job site faster, leaving more time to get to the next one. Likewise, businesses avoid downtime by scheduling jobs in time that would be spent traveling. This may eventually lead to real budget impacts, scaling the business without having to hire more field employees.
3. More Accurate Than Other Methods
Right now, predictive travel provides greater overall accuracy than other methods, including historical and real-time.
Last year the Massachusetts Institute of Technology (MIT) conducted a study based on Singapore’s city areas and highway networks. In comparing travel time calculation, the study found predictive travel offered the lowest percent of error for travel times over historical and real-time methods.
Predictive Travel Method Has Smallest Error % vs. Historical & Real-Time in Singapore:
Error % by Method
(Source: Travel Time Estimation Using Speed Predictions, MIT 2015, Narayanan, Mitrovic, Tayyab Asif, Dauwels, Jaillet, http://web.mit.edu/jaillet/www/general/ITSC2015-travel-time.pdf )
Luckily, this is not a zero-sum game. You don’t need to choose one method for travel time and route calculation. Predictive travel offers the best forward-looking view of what is most likely to occur. That said, when scheduling work in advance, predictive travel is the most relevant. And it will enable the most optimized schedule. But there’s no substitute for real-time traffic on the day of service.
Categories:Artificial Intelligence, Field Service Management, Fleet Management, Workforce Management Trends