If your company is considering adding electric vehicles (EVs) to service fleets, whether to save some green or be more green, you can now do so confidently with the help of disruptive technology.
As large telco operators with thousands of service trucks and vans look to incorporate electric vehicles into their fleets in the coming years, how can they manage all the intricacies of keeping an EV charged and on schedule?
While the benefits of EVs are great, allowing telecom organizations to reduce fuel costs and fulfill sustainability missions, the challenge of managing EVs in time-sensitive, intricate, and SLA-driven daily schedules have prevented companies from making the shift. As any EV car owner knows, you must plan your driving routes to ensure that you have access to charging stations and account for the battery recharging time. Now, multiply those requirements by thousands or tens of thousands of service vans and trucks, and you see the challenge.
This is why software designed using artificial intelligence and machine learning that optimises the planning and scheduling of field technicians is being updated to include EV fleet optimisation. As they are known in the field service management (FSM) software market, these scheduling optimization engines are popular amongst telco operators with large field workforces and service vehicle fleets. The solutions enable operators to efficiently plan and manage daily field engineer and long-range project schedules, ensuring that all consumer appointments are handled on-time and service level agreements with business customers are met. In essence, they can ensure that the right field technician with the right parts and skills is always sent to the right place at the right time.
Because these software solutions are infused with AI and machine learning, they can use powerful algorithms to process complex mathematical equations in a matter of minutes. For example, some of the most powerful optimisation engines can intelligently schedule 500,000 field service activities in under an hour. In other words, far faster than any human dispatcher can and without any error.
The benefits include reduced technician travel time by as much as 50% and higher first-time fix rates, all of which translates into lower labor costs, lower fuel costs, lower carbon emissions, and improved customer experiences. So, why not extend the intelligence of these workforce optimisation solutions to include fleets of electric service vehicles? That’s exactly what software vendors like IFS are doing.
Now, in addition to data inputs like customer service level agreements, daily appointment schedules, required drive time between locations, and even field engineer work breaks and time off, this planning software can consider everything needed to keep an EV on the road, including location of charge points, type, capacity, speed of charge and range. The software automatically plans EV charging requirements along with daily technician schedules, and it is nuanced enough to only use EVs in urban areas with more charging stations or for certain journeys that are shorter distances. The next wave of innovation will be supporting IoT-connected EVs that will have real-time battery usage tracking.
If your company is considering switching service fleets over to include EVs, you now can do so confidently. You can even prepare for that future with IFS’ embedded predictive planning tool that allows you to test how your business could cope with a wide range of scenarios including adding EVs into your fleet. It lets you easily visualize your simulated impact on resources, KPIs, and work demand.
IFS is proud to be pioneering innovation in electric vehicle fleet optimization, helping telco operators reduce operational costs, meet corporate sustainability goals, simplify ESG compliance and reporting, and drive efficiencies towards net zero carbon emissions.