Technologies like artificial intelligence (AI), machine learning and the Internet of Things (IoT) are setting the bar for efficiency and forcing field service companies to revisit business plans to find ways to incorporate them. One area that is already reaping the benefits from these technologies are manufacturing based field service organizations.
In particular, these technologies offer the opportunity to optimize business processes, predict future problem and increase technician utilization rates. Ultimately, more efficient factories mean higher profits and lower costs when used correctly.
AI and Field Service Scheduling
Artificial intelligence is especially well suited for scheduling. The process of sending technicians to repair critical equipment is time consuming, tedious, and, if done using yesterday’s processes, an inefficient use of resources. That’s because a variety of factors affect the need to reschedule a service appointment, including inaccurate estimated travel time and work duration, incorrect or missing parts and even weather conditions. Schedule adjustments are typical, but for efficiency’s sake, they must be done quickly, and humans don’t always have the complete data to solve the problem expediently. As a result, small issues turn into large logistical problems.
Incorporating AI into your scheduling allows managers to estimate travel time and optimize the technician’s route, taking into account weather and traffic conditions. Based on history and task type, it can also flag customers that are a higher risk for cancellation and respond proactively and efficiently. This saves valuable time, not only for the technician, who can now attend to other work, but for customers too.
AI and Predictive Maintenance
When combined with the Internet of Things, AI can also help proactively schedule appointments based on maintenance history. Manufacturing companies cannot afford to lose valuable time and productivity when there are unplanned equipment failures. Predictive intelligence provides an alert before machines break, enabling the company to preemptively schedule time to repair or replace a part without suffering any downtime, keeping the factory floor running on time and limiting disruptions.
While some service organizations are beginning to make use of sensors and intelligent machines, we have only scratched the surface. As more connected machines are deployed, organizations will be able to aggregate historical performance data on hundreds of thousands of units, enabling machines to learn and identify patterns in their own performance to predict and prevent problems. Ultimately, we can expect artificial intelligence to eventually bring equipment downtime to zero.
The next step after optimizing scheduling and moving to preventative maintenance models is improving the effectiveness of technicians and, again, AI plays its role here too. The ratio of work orders completed in one visit—a first-time fix rate—against the total work orders increases a service providers’ opportunities for additional revenue due to increased capacity. Customer satisfaction is also improved by increased productivity and a reduced average time taken for a given repair due to a shorter outage duration, which in turn increases revenue.
AI and machine learning also help make sure the correct person is assigned to a job. These technologies can analyze the past history, skills, location, priority, tools, and availability of the workforce to ensure jobs are dispatched to the right person. This complex process with multiple variables and contingencies would be too difficult for a human to undertake.
One of our customers, Diebold Nixdorf, benefitted from our FSM solution. Before implementing Click, Diebold’s schedulers needed inputs from multiple programs: CRM, logistics, and scheduling. The AI algorithms in Click’s program solved this challenge by automatically managing all of the information, enabling Diebold to make better scheduling suggestions.
As a result, Diebold was able to increase the number of daily jobs technicians performed by 33.1% and to better share resources among branches, further increasing operational efficiencies.
Future Technology is Here Today
True AI and machine learning enable a scale and speed of optimization that would be impossible with mostly manual processes.
The most common use cases for AI in field service include smarter scheduling and route optimization to reduce travel time. But the real advantage comes in mapping optimization to business goals—from SLA compliance to revenue growth—and letting the AI find the right path to each desired outcome.
It’s not science fiction and it’s not a vision for the future. It’s practical AI-based solutions aimed at solving real workforce management problems. AI enables organizations to limit the time human workers spend on repetitive and time-consuming tasks and optimizes the entire field service workflow to maximize efficiency, cut costs and maintain a competitive advantage.