As a tradie and field service professional, there are probably times you’ve pondered over the problem of having technicians scrambling around trying to resolve a last-minute scheduling problem. Maybe the job was assigned to the wrong technician? Maybe the job was assigned too late, and you’ve had to deal with irritated customers? Sometimes, you’re lucky and your technician might be able to foresee the problem before it occurs. However, when it isn’t the case, and you’ll end up finding yourself fighting a fire that could have been prevented if you had the power of clairvoyance.
Fortunately, there is a way you can predict the occurrence of such issues before they happen: Machine Learning.
Techniques like predictive maintenance use machine learning tools to assess data and draw relevant conclusions that can be used to prevent disruptions and issues before it happens. And while it may seem risky to rely on a computer to make business decisions, these decisions are based on analysing past behaviour and learning from mistakes made then.
So, what are the advantages of machine learning and why is an important technology that field service companies should look at?
Speed
Machine learning technology works by processing a huge amount of data in a short period of time to make high-probability predictions. Arrays of high-powered computers simulate millions of scenarios to determine which one will give the best feasible outcome when executed. Such kind of processing power is beyond the scope of human intelligence, which makes it a valuable asset for any enterprise that needs a competitive edge.
Accuracy
Because machine learning relies on years and years of user data, it can make extremely informed decisions that would have taken humans hours of complicated analytics to come to the same conclusion. Through machine learning, organizations can eliminate the human bias. No more will good decisions be ignored because a key decision maker ‘felt’ it was the wrong call. Machine learning relies on hard data and cannot be swayed by external factors that would otherwise affect a human’s decision making.
Reliability
While humans need about 8 hours of sleep a day to stay functional, the same limitation isn’t when it comes to machines. Computers can stay running 24×7, which means they fall very high on the spectrum of reliable assets. Servers that are set up for machine learning are usually high-end computers that have industry-leading specifications and kept in special containment centres where they can run uninterrupted. This way, machine learning algorithms can run at an optimum level, combing through stacks of data without skipping a beat.
So, does this mean machines will eventually replace humans in the field service industry? Probably not. When you need to make a nuanced decision or have to interact with an irritated customer, you would definitely prefer a human instead of an impersonal machine. However, by implementing smart machine learning solutions to make important business decisions, you can provide your in-field technician teams with the tools required to attend more calls and maximise customer satisfaction.
