Advanced Analytics To Help Field Service Industry
I spoke with Ryan Martin, Senior Analyst, ABI Research to discuss the future impact of predictive analytics. As a big data and mobile technology specialist, Martin was clearly an advocate of the concept, but highlighted that it might not be a wise investment for everyone at the moment.
General Electric offers predictive maintenance services for more than $1 trillion worth of connected industrial equipment. From jet engines to medical machinery, data means that GE, and companies who also use predictive analytics, can not just foresee the what, but increasingly the how and when as well.
With companies now able to connect machines with advanced analytics, they can not only increase profitability, but also mitigate risk. It’s a phenomenon set to turn once traditional industrial enterprises into software and analytics companies. But why is it having such a profound impact?
“The proliferation of Internet-connected devices is creating a framework for analytics – whether descriptive, predictive, or prescriptive – to become much more granular in nature. And this, in turn, makes the leap from descriptive to predictive analytics much more accessible - more sensors can help to capture more information to identify root cause,” says Ryan.
An example of a company which has already used this development to its advantage is TVH. At this year’s Mobile World Congress event, Kalman Tiboldi, Chief Business Innovation Officer at TVH, discussed how predictive maintenance will change the way customers view them as a company. He states: “It will mean that OEM’s aren’t selling or we [TVH] aren’t renting anymore the machine, but the result of this machine,’ later adding, “having these machines functioning as efficiently as possible is crucial, and this is all about the IoT.” By using sensors on mobile applications, data collection will make it possible for issues to be spotted ahead of time.
With the above example, there is a clear ROI for TVH. For organisational objectives which aren’t so cut-and-dry, however, predictive maintenance must be approached with caution, Martin states: “Generally, in predictive maintenance applications, if the ROI isn’t clear, the problem that’s trying to be solved isn’t big enough.”
“The extent to which a given organisation can justify the ROI of predictive – rather than corrective or planned – maintenance solutions depends on a number of factors,” he says. These include the value of the equipment and the cost to replace it, the level of complexity and difficulty there is to get a machine up and running once broken down, geographic location and whether it’s critical to the company’s overall goals.
He does add that while the ROI must be defined, there are opportunities for predictive maintenance implementation without connectivity. “But it’s also important to note that the implementation of predictive analytics in maintenance doesn’t necessarily require data connectivity between the equipment and the analytic backend; the process can be enhanced simply by analyzing historical log data from operational historians, or by making assumptions about asset usage and condition relative to changes in the surrounding environment,” says Martin.
The power of big data is not in its size, but its quality. Ryan states: “Finding success in the market for predictive maintenance solutions isn’t just about big data, it’s about better data.” This highlights the need for analytics systems which can make sense of the data in an efficient manner.
In this month’s market report we use more members of our extensive network of mobility professionals to look at the impact of predictive analytics and big data on field services, as well as wider trends in retail and the healthcare sector. Follow this link to download it.