Predictive Field Service Management – Why is it important?

Predictive Field Service Management – Why is it important?

Predictive technology is a rapidly growing area of interest and its benefits are being felt by consumers and businesses alike. But what is it exactly and why is predictive field service management so important?

First, let’s take a look at the technology itself, predictive tech uses data mining to identify and quantify what will happen and why. It does this by synergistically combining available datasets with domain knowledge and mathematical sciences.

That’s a heavy definition, to make it a little simpler predictive technology refers to a set of tools used for forecasting patterns in data while keeping in perspective the previous records/data. Interesting fact, it originated in predicting the weather and the stock exchange!

Field service management is about employees who provide services in the field and effectively managing their schedules, tasks and activity. It’s not easy to juggle all the aspects of a large field service tea, which is why predictive field service management plays a vital role in improving service delivery.

The Future of Predictive Technology

Predictive technology is already part of our lives, for example, Google Maps uses travel data to let us know travel times, sending a notification to our phones. Predictive technology will continue to improve our lives directly and in tangible ways by predicting problems and dangers around us before they occur.

Other technologies that continue to emerge and transform how we see the world include:

  1. Google Predict

    – This clever technology would mean that your car would know your commute and use the information to run on electric power only as you get closer to home, thus saving fuel.

  2. Booking Cheaper Travel

    – We all know how difficult it is to try and get the best value prices when it comes to flights and travel, the future could see predictive technology automatically booking travel at the cheapest times. We like the sound of that one!

  3. Smarter Computers

    – This might sound like a broad statement, and it is! Computers are continually evolving, and the future of predictive analytics could see your computer recognising the information you use most, saving it to flash memory and leaving long term memory on the hard drive. Making your experience quicker and more intuitive.

The future could see apps working in the same way, favourite apps are recognised and their data stored, if there is a problem your favourite app is saved as a priority.

  1. Energy usage

    – This is a common predictive technology already dominating the market, smart homes are becoming increasingly popular, but the future could see sensors monitoring themselves and reporting if one is malfunctioning.

Predictive technology has the power to evolve industries like healthcare, by using data to predict conditions and take preventative action. Or to provide local governments with more data to predict housing needs – the possibilities are endless.

What is Predictive Field Service Management?

Predictive field service management uses data to ensure that your field service teams are working at their optimum, using data to predict parameters that would affect service delivery and provide more accurate estimates for work completion.

The future could see predictive field service management be able to monitor tools and equipment, knowing when it is coming to the end of life or in need of service and automatically schedule a repair.

It would allow organisations to deliver pre-emptive services before an unfolding situation becomes critical. When something reaches critical level, it is then costlier to rectify as resources and equipment need to be available as soon as possible, or there is a breach of SLA’s. Instead with predictive field service management, a better service is delivered for the customer at a lower overall cost of delivery, as it is pre-emptive rather than reactive.

Dynamic scheduling could in the future use predictive analytics to highlight which settings are optimal for the organisation and its field service. The data can be used to actually solve scheduling problems before they occur, even potentially estimating travel times and how quickly jobs are completed.

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