Preventive maintenance may not be a rather glamorous subject, however there are big savings to be had in doing this right. One oft-cited study assesses the financial value of preventative maintenance in buildings and it places the ROI at 545%.

Now, thanks to the use of sensors, systems and analytics referred to as the Industrial Internet of Things (IIoT), the potential for increased uptime and upkeep savings is even higher. 1 way to realize that potential is through predictive maintenance; that is, using data to predict and fix problems before they happen.

How Predictive and Preventive Maintenance Saves Money

Businesses can save an average of 12-18percent through preventive maintenance (replacing parts before they wear out) compared to reactive maintenance (e.g., something breaks, you either fix or replace it). Unscheduled maintenance is very demanding and disruptive to employee productivity.

Consider that 80 percent of workers waste an average of 30 minutes each day regaining information. And if something does go wrong, employees will spend even longer time seeking to locate and understand the issue. The bigger the business, the greater the time drain-and the more it affects your budget.

Sensors Save Time and Manpower

The absolute variety of field service technicians indicates the scale of the potential savings for preventative and predictive maintenance. In 2016, Forbes noted that there were more than 20 million these 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 deliver 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 have to dispatch a maintenance employee, who is often required to drive long distances into a distant location, just to gather information from a system that may well be operating normally. Together with the IIoT, technicians may review information, without the need to travel.

As soon as your IIoT technology is in place, you just need to dispatch technicians when preventative maintenance measures are actually required. For some devices, problems may be corrected remotely as well. For example, pumps and drives can be taken offline to prevent damage before it occurs, while in other cases, software updates may be issued within the air.

Predictive maintenance entails another step: analyzing sensor data using predictive analytics to determine when a unit is likely to eliminate 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 because moving into a predictive maintenance model, one medical gear 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 Function of IIoT at Preventive Maintenance

Perhaps the best way to demonstrate the advantages of preventive maintenance is to provide a specific, real-life example. Some school campuses keep the hot water flowing during winter break to prevent pipes from freezing.

Campus maintenance crews dread a scenario where the plumbing break amidst cold temperatures, especially if the building is unoccupied at the time the damage happens. It is often difficult to access such leaks, and they may require 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 to a leak based on data from the aggregate usage meter.

In this case, carefully-chosen and implemented sensors give campus crews the information to discover and repair small leaks long before they become major issues.

Now take that one step further: based on data about water usage in comparable situations, it is likely to use predictive analytics to gain the insight you want to replace elements 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 universe where water systems are anticipated to shed up to 50 percent of their payload to leaks, the value of preventing such issues becomes apparent. Nonetheless, it takes solid data and powerful predictive analytical software versions to do so effectively.

With the IIoT, this approach can be applied even in distant or hard-to-access locations–again, saving time and ultimately, money. Sensors may even be put to report in their particular health and status, such as battery life.

Together with good data and predictive analytics, preventive maintenance may be timed for maximum uptime and efficiency. If it comes to the sensor data, it is important to say that the amounts aren’t all.

Instead, they are only part of the preventive maintenance picture. In order for data to have significance, it has to be analyzed. The results of that analysis can, in turn, inform better business decisions.

About the Author

Santiago Picco

Partner @ 4i Platform - Data Driven Innovation Electronic Engineering specified in control automation. Master in Stategic Management of Techology. Data scientist. Industry 4.0. IIoT and Digital transformation.

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