Industrial situations depend heavily on regularly scheduled maintenance to remain fully productive and free from costly, time-wasting mechanical breakdowns.
 
The term “preventive maintenance” covers a wide range of proscribed activities and general tasks. Each production component within a system will require some level of regular servicing, and that equipment typically will need to at least be cleaned and lubricated. In other situations, more extensive servicing may be required — involving the heavy reconditioning, repair or even replacement of certain parts.
 
At a higher level, preventive maintenance also involves providing upkeep for the physical plant that houses the various production systems. General tasks associated with this type of preventive maintenance include ensuring the HVAC system is in good working condition, all electrical systems are functioning and compliant with code standards, and all necessary lighting is operating correctly.

There’s often an impulse to regard preventive maintenance and predictive maintenance as completely distinct entities. Unfortunately, this attempt to frame the relationship in simple terms of preventive maintenance vs predictive maintenance misses a key point.
 
In reality, predictive maintenance is a more evolved form of preventive maintenance. Both types try to proactively anticipate and prevent mechanical failures. But predictive maintenance takes the concept even further.
 
Consider a single piece of industrial equipment. If we were practicing preventive maintenance on that equipment, we might use general information about that make and model of machine to formulate rough time estimates about when regular maintenance should be carried out on it. We would know approximately when maintenance should occur.
 
Predictive maintenance, on the other hand, is considerably more precise, and because of this, requires substantially more data. Information about the expected lifecycle of that equipment model is combined with historical data about the performance of that particular unit. Once armed with that extra data, predictive maintenance models can churn powerful predictions that let operators know with certainty when system failures will occur. 
 
And because repairs scheduled through predictive maintenance occur exactly before they’re needed (and not according to a general timetable), no unnecessary repairs are made — which keeps maintenance budgets leaner. 
 
Predictive maintenance flourishes in conjunction with IoT. With machines generating constant updates about their activities and condition, predictive maintenance models are now getting the abundance of data they need to produce crucially needed maintenance predictions.

One main aspect separates preventive maintenance from reactive maintenance: timing. Reactive maintenance adopts a “run-to-fail” policy whereby maintenance doesn’t occur at all until a piece of equipment actually stops working. Then the needed repair must be accomplished as soon as possible. On the other hand, preventive maintenance tries to anticipate equipment failure and take corrective action before mechanical breakdown occurs.
 
The method selected — preventive maintenance or reactive maintenance — wouldn’t matter nearly as much except for the fact that reactive maintenance can easily become far costlier than preventive maintenance. Consider the analogy of car maintenance, and the example of the car that doesn’t receive regularly mandated service, only to suffer a catastrophic, multi-system failure that suddenly demands extensive and expensive repairs.
 
Although adopting preventive maintenance measures requires budgeting in regular service activities and may necessitate the adoption of a computerized maintenance management system (CMMS), in an industrial setting it’s usually worth it, especially since when an industrial operation is down for unscheduled repairs, it can quickly cause stalled production or even lost revenues.

Increasingly, deploying AI and IoT technologies in operations has resulted in continual optimization of both the assets and activities that drive the industrial sector. From the perspective of assessing the future of preventive maintenance, it’s clear the use of remote monitoring and analytical modelling has already resulted in a net reduction in the amount of resources allocated to executing preventive maintenance tasks. 

From an asset perspective, more extensive data collection and analysis — made possible with today’s AI and IoT solutions — has enabled manufacturers to get valuable data that has improved the reliability of their operations and products. Asset operators can truly understand the quality of the assets they are deploying into operations, driving new asset lifecycle strategies that remove poorly performing assets from their operations and ultimately reducing downtime and costs.