From The Editor | April 13, 2018

The Building Blocks Of Predictive Service

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Sarah Nicastro

By Sarah Nicastro, publisher/editor in chief, Field Technologies
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Predictive Field Service

Today’s field service organizations are on a mission to migrate from the traditional break/fix service model to a predictive service model — it’s simply what customers expect and where the industry is headed. As Jack Rijnenberg, director of global customer service at Markem-Imaje put it when I interviewed him recently, “There’s a clear movement in the service industry toward predictive service, and that’s the direction we need to move in.” But the reality is, a successful transition from break/fix to predictive service requires well thought out strategy and execution.

You have to have a solid foundation in place from which to build your predictive service model. Your first step should be your people, or employees. A major business shift such as the move from break/fix to predictive demands that your frontline employees be in the know and have an opportunity to ask questions and provide feedback. By starting with your people and giving them perspective on why a business shift is necessary, you set yourself up for success as you execute your plan. They may very likely have useful input for you as well.

Master Field Service Automation

Before you can deliver on a predictive service model, you need to have your basic field service functions automated. This includes things like scheduling and dispatch, work order management, parts and logistics, customer communications, and so on. Not only do these functions need to be automated, but they need to be clean and fully functional. If ever there is a time to clean up poor processes or fix a solution that’s not quite working right, it’s before you layer additional technology and processes on top of it. The automation of these processes gives you data coming in that will be necessary to execute predictive service.

Make sure you have systems in place to effectively analyze that data, and make business decisions with it in mind. From this point forward, the data you are receiving will grow exponentially – and while that is crucial for predictive service, it can be overwhelming if you don’t have the proper means for making use of it. Markem-Imaje is in the midst of a major initiative to standardize and automate service processes and delivery, because the company realizes those steps are essential in ultimately achieving the predictive service goal.

Incorporate IoT

Once you’ve mastered field service automation, the next step is to layer in IoT. Monitoring your assets using IoT will provide you with a wealth of new data that can be used to streamline troubleshooting so that field technicians are more prepared when they arrive on site, to maximize remote resolution so that fewer trips are necessary, and to collect data on asset performance that can provide valuable business intelligence. Incorporating IoT is what really enables a field service organization to move away from a reactive service model, because it gives you the insight you need to become more proactive and ultimately predictive.

Layer On Artificial Intelligence

With field service automated and assets connected, the next step is to layer on artificial intelligence (AI) through machine learning (ML). While incorporating IoT data will get you closer to the predictive service goal, AI is what will take you over the finish line because it is what allows you to draw conclusions on the IoT data you’re receiving to ultimately deliver predictive service.

Does this sound like a lot to take in? It is, but success is achieved in taking it one step at a time. As Rijnenberg of Markem-Imaje points out, to achieve true success in field service optimization, there is no cutting corners. In fact, shortcuts will almost always backfire and set you back significantly. “At the end of the day, these projects are hard work. You have to do that hard work and realize there’s no free lunch,” says Rijnenberg.