How Can Field Service Professionals Use Big Data To Make Their Jobs More Efficient

Contributor: Simon Barton
Posted: 05/09/2016
innovation
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Big data obtained from connected devices has allowed field service professionals to take a more proactive stance to maintenance and increase customer satisfaction.

As companies attempt to move away from corrective maintenance, a number of new opportunities have emerged. New business models, increased operational efficiency, improved customer engagement and new revenue streams could all be achieved by the development and implementation of predictive maintenance.

Corrective maintenance – where field service professionals are told when a machine has broken down - is still commonplace in many organisations. While still a data-heavy process, it’s reactive by nature. Kalman Tiboldi, Chief Business Innovation Officer, TVH states: “Collective maintenance has become outdated, but predictive maintenance allows for remote diagnosis, where we can predict problems before they happen – this saves time and is more cost effective.”

The opportunities presented by big data are considerable. Sensors embedded in equipment could automatically trigger a field service professional to repair something, or alert them when a machine is due for maintenance. Field service companies already collect enough data to accurately predict when a machine will need maintenance, which gives their customers more time to develop contingency plans to cope when their equipment is being repaired.

Eugene Signorini, Senior Analyst, Enterprise Strategy Group, refers to General Electric (GE) as a company leading the way in predictive maintenance, stating that the company is already reaping the rewards from its use of big data and analytics.

GE switched its jet engine business model around a few years ago so that instead of selling engines to airlines, they leased and operated them. This made an efficient field services strategy imperative. And by connecting power plants, manufacturing bases and even jet engines, Eugene Signorini described how GE was able to leverage important insights. He says: “GE started noticing that certain engines were having issues earlier than they should have, and when they used data analysis, they discovered that it’s happening in engines in hotter environments – like South-East Asia and the Middle East – where different environmental impacts were having a detrimental effect on jet engine performance.”

As mentioned, collective maintenance is still commonly used, but for the field services industry, the impact of predictive maintenance is not too far away. Eugene Signorini says: “The amount of companies implementing predictive maintenance will vary from industry to industry, but we’re already seeing some of it today – the technology and data is available, but it could be just a period of years before these projects are rolled out.”

The field services industry is being reshaped by connected devices, big data and improved analysis, and the power of predictive maintenance will have a substantial impact on the industry. Interested in Big data? Find out how it's impacting retail and healthcare as well.


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Simon Barton
Contributor: Simon Barton