Tackling climate change is top of the agenda. Already 20% of the 2,000 world’s largest publicly listed companies have signed up to Net Zero 2050 plans. The race is truly on to make a difference to the planet. Emissions must be reduced by 57 gigatons of CO2 to reach targets, and it’s AI-powered technology that will play a crucial role in making it possible says Sarah Nicastro, VP of customer engagement at IFS and creator of the Future of Field Service podcast. The field service industry, although often believed to be unavoidably energy-intensive, must make significant improvements before it’s too late—and it will require advanced AI technology that will ensure sustainable optimization is taken to the maximum.
Greenhouse gases have taken the world by storm; in 2021, 40.8 billion metric tons of greenhouse gases were produced, with the U.S. to blame for almost one sixth of this. The U.S. has now promised to reach net-zero targets no later than 2050. The Long-Term Strategy (LTS) for reaching this target states that cutting energy waste is one of the critical ways to achieve these ambitious but necessary goals for climate change.
How Field Service Can Play a Part in the Battle
The transportation industry is the largest contributor to U.S. emissions, so it has an important role to play in the transition to net zero. In just one year, U.S. vehicles produce 1,098 million metric tons of carbon dioxide equivalents—almost one-fifth of the nation’s total carbon dioxide emissions. Reducing drive time is a crucial way to reduce the nation’s carbon footprint.
While we see more and more service organizations adopting a remote-first approach, which will reduce the amount of on-site visits necessary for field technicians to complete, companies agree that there’s no foreseeable future in which field service doesn’t include an on-site component. Where and when service visits remain necessary, optimized resource utilization and route scheduling provides an impactful way for field service organizations to tighten fuel consumption, and reduce energy waste and carbon emissions.
However, this is easier said than done. Matching team resources with fluctuating demand over multiple time horizons is a tough challenge. The task of managing customer expectations with unexpected delays and unforeseen events in real-time must in most circumstances take priority over perfectly optimized resource utilization.
But AI-powered Planning, Scheduling & Optimization (PSO) technology can save field service organizations many hours of time, countless miles and hundreds of thousands of dollars, ensuring operations are as efficient and sustainable as possible without sacrificing customer experience.
Uncover Hidden Inefficiencies With PSO Technology
Real-time optimization, scalability and built-in intelligence are key when it comes to workforce planning and scheduling. The AI technology embedded in advanced PSO technology finds and fixes invisible inefficiencies that businesses cannot see. It automates the optimization of workforce planning, scheduling and routing for a more streamlined, efficient and environmentally friendly version of field service operations.
PSO automatically schedules service time slots based on resource availability and prioritizes jobs depending on the level of urgency so that the most pressing and time sensitive jobs can be scheduled as soon as possible, without interfering with pre-existing jobs. This means the right resources are available at the right time for the right job without the risk of double-booking or the need for technicians to make multiple trips.
When there are sudden changes in either demand and urgent requests or resource availability for instance, due to illness, PSO can immediately absorb incoming workload imbalances by automating capacity and the movement of resources, minimizing the number of trips a technician must make, and maximizing operational efficiency in real-time.
Unlock New Efficiencies With Dynamic Route Optimization
The dynamic route optimization function of PSO technology assigns jobs for technicians that will optimize drive time by taking the most efficient route. It achieves this by using AI to calculate time needed to complete each task based on existing data for each technician, so that an appropriate timeframe is given to jobs that are more complex or have a larger scale, to guarantee there is enough time for completion and to prevent overruns. Routing is also optimized based on geographic parameters, such as, operating in urban versus rural catchment areas and assigning jobs that are as close together as resource availability will allow. This ensures the order of jobs is not only time optimized but route optimized so that each field technician travels the minimum amount of distance necessary.
Numbers are Facts
Businesses have reduced average technician travel time by 35%-50% when implementing IFS PSO. One example is CoolSys, a U.S.-based HVAC/R services parent company with marquee customers, such as Amazon, Target, Starbucks and Walmart. The company was able to reduce average technician travel time by 35% with the use of IFS PSO.
As you can imagine, the reduction in travel time and mileage has a significant environmental impact. Take, for example, a field service organization with a workforce of 1,000 technicians that completes a total of 780,000 jobs a year, covering approximately 14 million miles. With medium-sized trucks averaging 8 miles per gallon and every gallon of gasoline releasing 22 pounds of CO2 emissions into the atmosphere, the typical field service business can produce up to 38,500,000 pounds of CO2 every year. After deploying PSO and cutting travel time by 35% to a total of 9.1 million miles, annual carbon emissions can be reduced by 65% to just 13,475,000 pounds per year.
Along with these important emission reductions comes significant business cost savings. For instance, if fuel prices average $5 per gallon, a 35% reduction in travel time from planning and scheduling optimization could provide a fuel cost saving of, in this example, $3,087,000 per year.
The Benefits Continue: AI and Machine Learning Get Smarter With Age
The cost-savings and eco benefits of PSO technology only get better over time, thanks to AI and machine learning capabilities. The machine learning within the optimization engine continually improves the accuracy of job durations by self-learning how long each technician takes to perform various activities and creating a fingerprint for each field service engineer, while enabling the system to match the best engineer to each specific job. This means PSO becomes completely tailored to each business’ specific operations, providing a truly customized optimization system that offers an increasingly greater ROI over time.
PSO Technology is the Perfect Tonic to Field Service Optimization
The benefits of PSO are three-fold; from substantial cost-savings and minimized carbon footprints, businesses can be confident that customer experiences have not been jeopardized in the process. Finding a better suited AI application would arguably be a difficult task.
