Four ways predictive maintenance changed how maintenance departments work

Predictive maintenance insights

• Predictive maintenance equates to scheduling optimization, allowing for a more efficient workflow and less downtime.
• Both predictive and preventative maintenance work hand-in-hand because predictive works to learn when to do preventative maintenance.

Maintenance is difficult to get right. Even the best machinery and equipment can be unpredictable, and, as assets become more technologically sophisticated, the complexity of maintenance increases. Being able to predict more precisely when assets may need maintenance can do much for streamlining a maintenance department’s workload. Predictive maintenance (hereafter referred to as PdM) facilitates that effort by using artificial intelligence (AI) to assess operational data in real time. It primarily achieves this with smart sensors in or near assets that continually monitor their current status, thereby enabling the prediction of potential malfunctions or failures. This article will explore four ways that PdM has changed how maintenance departments work – and for the better.

Courtesy: Limble CMMS

Predictive maintenance = less unplanned downtime

Unplanned downtime seriously impacts both production and the financial bottom line in industry/manufacturing, as well as the tech and services industries. Research has shown that 82 percent of companies have experienced unplanned downtime within the past three years, costing companies as much as $260,000 an hour. It gets worse – costs from unplanned downtimes averaged $2 million. It is therefore imperative for any business that unplanned downtimes be minimized. PdM achieves just that.

PdM-related technology itself helps one understand the ability of this maintenance to greatly minimize the risk of unplanned downtimes. For example, sensors focused on vibration analysis and acoustic analysis mean that mechanical assets are far less likely to become faulty due to excessive vibration or grinding/jarring between components. Likewise, thermal sensors can help detect hotspots in technology that may denote overheating and can help thwart future breakdowns due to scorched parts or circuits in machinery. The bottom line is that PdM decreases the risk and occurrence of unplanned downtime.

Predictive maintenance = improved productivity

Maintenance departments all too often work on very tight schedules. One major breakdown can play havoc on maintenance scheduling, meaning that teams run the risk of falling behind on their planned/scheduled maintenance work. The ongoing skills shortage in the maintenance industry has only put maintenance departments under greater time constraints and logistics-related stress. Sound PdM practices will mean less asset malfunctions or breakdowns, and less maintenance surprises will result in improved productivity for maintenance workers.

Productivity is an inherently time-related factor – the more productive you are, the more time you have to do work properly. Therefore, a key benefit of improved productivity due to PdM is that it affords maintenance workers the time and leeway to concentrate on other maintenance issues as they arise. Importantly, it also provides maintenance teams with other benefits, such as the time to improve their skills through training and up-skilling.

The bottom line is, more predictable maintenance equates to scheduling optimization and less negative hits on productivity within the maintenance department

Predictive maintenance = better bottom line

There are various ways in which PdM can improve the financial bottom line. For example, sensors in machinery can help detect potential issues before they lead to breakdowns. As such, PdM can improve the mean time to repair (MTTR) rate by up to 60 percent.

Furthermore, the same sensors can help verify that maintenance or repairs undertaken has been successfully done. PdM as a maintenance focus results in better asset utility and increases the lifespan thereof. This can only save money in the longer term.

PdM leads to less waste in the broadest sense of the word, whether in terms of labor, components, energy and other costs, not to mention increasing the lifespan of assets that work better. Less waste means that maintenance departments can operate and deliver services more efficiently per person hour.

Bottom line is that more intelligent, technology-enhanced maintenance automatically equates to an improved financial bottom line.

The predictive/preventive relationship

Preventive maintenance (PM) is based on pre-planned, time-based maintenance on a regular, time-based basis. This form of maintenance could be viewed as being somehow outdated in a maintenance world that is increasingly technology-driven and focused on real-time data and solutions. It is generally accepted that, although PM has its place within any proactive maintenance regime, PdM is better-suited to the fast-evolving realities of modern maintenance within the context of Industry 4.0 and smart manufacturing, particularly as the step before prescriptive maintenance, as the graphic below shows:

PdM should be an improvement on PM because less time is wasted. That is because, unlike PdM, PM is fixed, with maintenance going ahead regardless of the actual condition of the equipment. That will certainly mean that some of this maintenance may be unnecessary.

PdM functions on the basis of real-time data that is continuously being gathered. The data, therefore, is precise and relevant at all times. Conversely, PM is planned in advance and therefore relies on historical and aggregated data that may not accurately reflect the current status of assets.

Bottom line is that preventive maintenance is, of course, better than reactive maintenance, but predictive maintenance is better still.

As production has gotten smarter – courtesy of AI, machine learning (ML), the Internet of Things (IoT), and automation – so has maintenance. Predictive maintenance is the bridge between traditional preventive maintenance and future-forward prescriptive maintenance. It has done much to change the way in which maintenance departments work. It has certainly made the maintenance function smarter and more efficient.

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