Why Predictive Maintenance is the Future of Industrial Efficiency

The majority of industrial plants are still operating with a reactive maintenance approach (i.e. run-to-failure), although no plant manager wants to admit that’s the case. For plants employing a preventive maintenance strategy, time-based maintenance tasks are the best alternative to a reactive approach to maintenance. However, these time-based approaches do not take into account how each individual piece of equipment is used and the conditions it performs under. The time-based approach doesn’t optimize the schedule of performing maintenance. For example, there is likely to be time when a piece of equipment is running (and consuming raw materials or producing product) when it was scheduled to be stopped for maintenance.

Why Calendar-Based Maintenance Falls Short

Conventional maintenance is time-based with reliance on OEM recommendations. Equipment is serviced at specific time intervals, regardless of the operating condition of the component. This preventive approach can result in unnecessary costs if a component shows no signs of wear. On the other hand, preventive maintenance can decrease overall equipment availability because the maintenance was either not needed or the maintenance interval was not frequent enough, resulting in additional repairs and downtime.

Data is the Foundation, Not the Technology

People often overlook the fact that the quality of the input data is just as important as the tools themselves. Clean and accurate data is essential to the success of any predictive maintenance strategy. For heavy equipment fleets, the most basic and essential data point is operational hours. Hour meters on forklifts and other industrial assets record actual run time under load, which is a far better proxy for wear than calendar days.

The problem is that many operators don’t know how to read them correctly, or they don’t understand what the numbers mean for service intervals. Learning to interpret forklift hour meter data properly is where usage-based maintenance begins, it’s the manual entry point into a system that can later be automated with telematics and sensors. Once you have clean, consistent hour logging, you have a baseline. That baseline is what machine learning algorithms need to start identifying patterns.

What Predictive Systems Actually Monitor

Once you have those foundational vibrations and temperatures, you start applying advanced pattern recognition and machine learning. That’s where maintenance starts looking less like calendar-based guesswork and more like a major step change. Any machine that has run hot or produced the wrong vibration pattern will have failed in predictable ways. That means there’s a chunk of historical repair order records that you can map. Clutches on this type of forklift have always been replaced in this window of running hours. Here’s the sequence of temperature delta and spike in vibrations we’ve seen prior to every forklift drive motor failing. And so on.

Idealized predictive maintenance doesn’t spring from the ether. It takes a solid foundation of correct data and enough time for the algorithms to learn. But when it arrives, you hit the maintenance sweet spot. You’re not too early. You’re not too late. You don’t waste needless time or resources on an engine that’s far from failure. But non-negligible dollars aren’t blown on the costs of a sudden bad event either.

Extending Asset Life Through Operational Control

An often-overlooked advantage of predictive maintenance is the increased longevity an asset can experience at the end of the lifecycle. Well-managed heavy equipment doesn’t just spare itself from catastrophic failure, it spares the cascading failure effect, where one well-worn component induces rapidly increasing wear in adjacent parts. For example, a misaligned drive axle doesn’t just wear itself out, it wears out tires and transmission components.

Keeping equipment in the appropriate part of its performance envelope consistently throughout the lifecycle of the asset also enhances its overall usable life. This is highly relevant in a world where lead times for acquisition of replacement units have stretched out into months, and the cost of acquisition has risen dramatically. It’s much easier to justify the ROI of keeping a five-year-old forklift running well when the other choice is a five-month wait for its replacement.

Total Productive Maintenance frameworks formalize this concept by engaging operators directly in early fault detection. The people driving/operating the equipment every day are often the first to sense changes in performance, unusual vibration, slower response, strange sounds, etc., before the sensors detect them. Formalizing this observation into a reporting loop makes the human side of what the telematics unit is capturing a more robust pinpoint solution.

Where to Start

You don’t need a complete digital transformation to start implementing predictive maintenance. Just ensure that you are collecting accurate, consistent data on operational hours for all your equipment. Start by instilling the habit of regularly checking and recording the usage of your equipment. This establishes the data basis that any other more advanced system will require anyway, and already enhances the quality of your maintenance decisions.