Industrial IoT Predictive Maintenance Results Depend on Predictive Analytics

Industrial IoT Predictive Maintenance Results Depend on Predictive Analytics
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Preventive maintenance may not be a very glamorous topic, but there are big savings to be had in doing it right. One oft-cited study assesses the economic value of preventive maintenance in buildings and it places the ROI at 545%. Now, thanks to the use of sensors, systems and analytics known as the Industrial Internet of Things (IIoT), the potential for increased uptime and upkeep savings is even higher. One way to realize that potential is through predictive maintenance; that is, using data to predict and fix problems before they occur.

How Predictive and Preventive Maintenance Saves Money

Businesses can save an average of 12-18% through preventive maintenance (replacing parts before they wear out) compared to reactive maintenance (e.g., something breaks, you fix or replace it). Unscheduled maintenance is very demanding and disruptive to employee productivity. Consider that 80% of employees waste an average of 30 minutes per day retrieving information. And when something does go wrong, employees will spend even more time trying to locate and understand the problem. The bigger the business, the greater the time drain—and the more it affects your budget.

Sensors Save Time and Manpower

The sheer number of field service technicians indicates the scale of the potential savings for preventive and predictive maintenance. In 2016, Forbes noted that there were more than 20 million such workers worldwide. Currently, many manufacturers dispatch technicians to tend to compressors and other devices in an attempt to collect data because the equipment isn’t IIoT-enabled. Adding a data gateway to send relevant data on already-installed equipment saves time and money, while providing you with the data you need to unlock significant cost savings. You will no longer need to dispatch a maintenance worker, who is often required to drive long distances to a remote location, just to gather information from a device that may well be operating normally. With the IIoT, technicians may review information, without the need to travel. Once your IIoT technology is in place, you only need to dispatch technicians when preventive maintenance measures are actually required. For some devices, problems may be corrected remotely as well. For instance, pumps and drives can be taken offline to prevent damage before it occurs, while in other cases, software updates may be issued over the air.

Predictive maintenance involves another step: analyzing sensor data using predictive analytics to determine when a device is likely to lose efficiency or fail. This type of data analysis gives technicians the ability to adjust operating parameters or replace the equipment or device before failure occurs.

The above-mentioned Forbes piece noted that since moving to a predictive maintenance model, one medical equipment manufacturer saw a 78% jump in the number of service calls that could be diagnosed and repaired remotely. They subsequently saved a great deal of time and money by avoiding the costs associated with unnecessarily dispatching a field technician.

The Role of IIoT in Preventive Maintenance

Perhaps the best way to demonstrate the advantages of preventive maintenance is to give a specific, real-life example. Some college campuses keep the hot water running during winter break to prevent pipes from freezing. Campus maintenance crews dread a scenario where the pipes break amidst cold temperatures, especially if the building is unoccupied at the time the damage occurs. It is often difficult to access such leaks, and they may need heavy equipment to lift large pipes for repair.

Recently, campus maintenance crews started installing in-pipe sensors to measure system-wide flow rates. That’s a use of the IIoT that’s far more precise than trying to zero in on a leak based on data from the aggregate usage meter. In this case, carefully-chosen and implemented sensors give campus crews the information to detect and repair small leaks long before they become major problems.

Now take that one step further: based on data about water usage in comparable situations, it is possible to use predictive analytics to gain the insight you need to replace components before they wear out. This insight would allow for the replacement of parts at a fraction of the cost of performing repairs after the part fails. In a world where water systems are expected to lose up to 50% of their payload to leaks, the value of preventing such problems becomes apparent. But it takes solid data and robust predictive analytical software models to do so effectively.

With the IIoT, this approach can be applied even in remote or hard-to-access locations—again, saving time and ultimately, money. Sensors may even be set to report on their own health and status, such as battery life. Combined with good data and predictive analytics, preventive maintenance may be timed for maximum uptime and efficiency. When it comes to the sensor data, it is important to note that the numbers aren’t everything. Rather, they are only part of the preventive maintenance picture. In order for data to have significance, it must be analyzed. The results of that analysis can, in turn, inform better business decisions.

The Key to Useful Data and Analysis

Primitive Logic gives you the power to get the most out of the IIoT. Let us help you gather data from your existing equipment; we’ll then help you analyze and make smart business decisions based on this information.

To learn more about how the IIoT and predictive analytics can transform your approach to preventive maintenance, while simultaneously making your business more efficient and profitable, contact us today. Our elite team is happy to guide you toward a higher ROI thanks to our expertise in the latest technology.

Kevin Moos, June 2017