How preventive maintenance can backfire and harm your assets

No one questions the perfect day to repair or replace an asset: the day before your asset breaks down. But science hasn’t developed a crystal ball yet.

You’re likely following guidance from the original equipment manufacturer (OEM) about how to maintain your asset. But is that guidance intended to maximize the output and life span of your asset, or is it tailored to the OEM’s desire to make sure the warranties are kept up — or to prod you to invest more into the asset or buy a new one before it’s necessary? How was the asset tested to develop those guidelines — and how does that compare with how you’re actually using it?

Consider your car. You’re told to get an oil change after a certain number of miles or months. But what matters more than these metrics are the viscosity of the oil over time, its ability to handle heat, the length of your drives and how often you start the car. The most important factors aren’t continually monitored, so you’re left to follow imperfect thresholds that are up for debate — for instance, do you need an oil change after 3,000 miles or 3 months, or is it more like 10,000 miles or 6 months?

The answer is: well, it depends. Unless you have your own data on how the asset is performing and understand how it degrades, you have nothing else to go on but OEM guidelines. These guidelines are often flawed: IBM research shows that 30% of preventive maintenance activities occur more frequently than necessary, and 89% of asset failures occur at random, not because the asset has reached a certain age.

But now you can have the data; Internet of Things (IoT) sensors and other emerging technologies monitor how your assets are performing. You can move from preventive maintenance to predictive maintenance, according to predetermined conditions, instead of an often arbitrary schedule.

The power of predictive

IoT sensors on your asset produce data about factors such as pressure, vibration or viscosity. Artificial intelligence (AI) and machine learning monitor the data and trigger warnings when human intervention may be required. Using certain thresholds that you define, you can give yourself a cushion on maintenance depending on the risk level of failure and what the impact of that failure would be — whether it’s unplanned downtime at the more minor end of the spectrum or catastrophic damage on the other. The setup creates an automated way to schedule inspections and replacements, reducing or preventing asset failure while boosting equipment uptime.

IDC Manufacturing research shows that predictive maintenance plans can cut expenses up to 20% while boosting asset availability by 20%. Within the oil and gas industry, which is heavily reliant on expensive assets, IoT-enabled maintenance cut costs by 30% and equipment downtime by 45% and helped raise production output by 25%, the U.S. Department of Energy says.

“Predictive maintenance offers a 21st-century solution to a timeless concern among executives: how to get more from assets by reducing downtime and extending life cycles,” says Jim Perrine, Principal, Americas Enterprise Asset Management Leader, Ernst & Young LLP.

Aside from costing money and causing downtime, unneeded maintenance can backfire and deliver the opposite of what’s intended, reducing the life of your asset. Maintenance technicians cannot take apart an asset and put it back together as well as it was originally manufactured: every time they do so, a new failure point is potentially introduced.

Consider your home’s heating, ventilation and air conditioning system. Perhaps you pay a technician to clean and inspect your compressors every year. Every time you do a pressure test, you lose refrigerant, and, over time, you’ll eventually lose enough that you’ll then have to pay for more. But, with IoT-enabled sensors, such maintenance won’t need to occur until you’re notified that the refrigerant pressure is becoming too low.

There’s also an impact on safety, since some maintenance is potentially harmful to your workforce. In the manufacturing industry, 25% to 30% of workplace deaths are related to maintenance — due to shocks, burns and injuries from moving parts, for example — according to a report from BLR. Minimizing maintenance also means you’re not exposing your workers to unneeded risk.

And when you have the data, you’re set up for even more proactive possibilities. For instance, when you develop confidence in your AI capabilities, the technology can automatically start looking for replacement parts when needed and determine the availability from vendors if you don’t have them in stock.

Putting standard solutions to work

The good news is that you don’t have to start from scratch to get these capabilities up and running. The software and hardware are on the market today, and you can get expert help in setting them up. The packaged software typically includes defined business processes intended to work across the board, regardless of sector or company size.

Companies can gain significant benefits by following these processes, yet some feel more inclined to bend the software to fit their existing processes through customization. Perhaps there’s a good reason for this, but the software will cost more to implement and require higher ongoing support costs. More importantly, these businesses won’t be able to tap into industry group benchmarking and effectively leverage data and optimization models honed over time, from similar assets, for analytics that drive better decision-making.

There’s a familiar adage: “If it ain’t broke, don’t fix it.” Through preventive maintenance, many companies are doing the opposite — and the outcomes can be measured in greater downtime, shortened asset life spans and ultimately lost revenue. Emerging technologies offer a different way forward — based on real-time data of your assets in the moment, not on schedules developed with no consideration of your unique goals, processes and use cases — that also enable smarter and safer ways of working and new predictive possibilities.

Jim Perrine, Principal, Americas Enterprise Asset Management Leader, Ernst & Young LLP, contributed to this article.