One of the KPIs of critical importance in plant maintenance is Preventive Maintenance Compliance- PM Compliance. It is measured as a percentage of PM jobs completed to the number of PM jobs planned in any week/month. Any score above 98% is considered world class. Hence invariably, there are special efforts by the maintenance department desirous of having high equipment availability to have the PM compliance above or close to 98%. However, it is often noticed that despite of very high PM compliance, there are rampant equipment failures across many of such plants raising doubts over the efficacy of the figures. It is also observed that such companies often indulge into adding more PMs as an immediate measure to reduce breakdowns. But there is no respite from failures.

A close analysis would indicate that majority of these failures are random failures and not to be age related. As a matter of fact, any arbitrary increase in number of PMs results in more failures. Infant failures creep in the reasons being wrong or defective spares parts, working conditions, poor craftsmanship, human errors etc.

The right solution to this problem would be to take up Preventive Maintenance Optimization, PMO. This exercise dives into each of the failures and the corresponding maintenance tasks taken to arrest these failures. The unwarranted tasks are taken off, additional tasks are added wherever required, frequencies of the tasks are modified and maintenance strategies are changed if the existing strategy found not desirable or not working. PMO exercises when done correctly eliminates wastes and improves the effectiveness of the PM program.

While PMs are rightly suited for age related failures, majority of the equipment failures in any industry are usually random failures. These failures can be taken care of by predictive maintenance and condition monitoring. Hence this maintenance strategy needs to be given more and more importance. While many companies have increased their focus in this area and the use of AI is on the rise, measuring the compliance and effectiveness is still not being given due importance.

Under the circumstances it makes sense to introduce an additional separate KPI that tracks Predictive maintenance/ Condition monitoring work. The KPI could be the number of predictive maintenance work identified vs the actual numbers of completed in a week. As more and more jobs of condition monitoring viz Vibration analysis, Ultrasound, Thermography etc.  are identified and the corrective work therein planned and subsequently executed, there shall be a continual reduction in failures.

Thus such an approach of measuring the PM compliance and PdM compliance separately shall help to measure the effectiveness of each of these strategies. This would also help to give due importance to Predictive maintenance and arrest the extra importance as being given to Preventive maintenance.