Effectively Measure PM
EP Editorial Staff | October 13, 2020
Uptime improvement and cost reductions are only two of the benefits of a predictive-maintenance program.
By Bryan Christiansen, Limble CMMS
A predictive-maintenance strategy is a goal for many manufacturing sites and for good reason. A PwC, New York, NY, (pwc.com), report found that 60% of operations with predictive-maintenance programs realized an average uptime increase of 9%. Along with the uptime improvement, these companies saw cost reductions, asset lifetime extension, and risk reduction. Another report by Teknowlogy Group, France, (teknowlogy.com), found that predictive maintenance could reduce costs as much as 40%.
With today’s tools, starting a predictive-maintenance program is easier than ever before. Once you’ve deployed a predictive-maintenance strategy, how do you know if it’s paying off?
A traditional method of measuring maintenance is to track the overall cost of the department. Like a reactive-maintenance strategy, this is outdated and simplistic. The problem with only tracking maintenance costs is that the metric simply steers you in the direction of lowering costs. This is not always the best way to maximize profits, as unplanned downtime often is a much bigger cost to an organization. Of course, maintenance cost is something to be aware of and track over time but, as one of several indicators of maintenance performance, not the sole indicator.
CMMS is Vital
As you move from reactive and preventive to predictive maintenance, a CMMS (computerized maintenance management system) becomes indispensable. It will help to schedule preventive-maintenance tasks, simplify tracking maintenance activities, and can generate reports to measure performance. Without a CMMS, many advanced metrics will be extremely difficult to track.
Once you establish your baseline data with the help of a CMMS, you can start to establish metrics, indicators, and goals. It may take some time to see progress, but with specific and realistic goals, you can focus the improvement of your maintenance department.
KPIs vs. Performance Metrics
KPIs (key performance indicators) and performance metrics may sound interchangeable, but they are not the same thing. KPIs track progress toward a key maintenance objective. Performance metrics will follow the status of a certain process. KPIs can be thought of as strategic measures, where performance metrics are tactical.
Examples of KPIs are maintenance backlog in hours or maintenance schedule compliance. Performance-metrics examples are overtime or open work order hours. Here are some KPIs to consider:
• Percentage of work hours generated by predictive methods—the higher the percentage, the more likely the program is working
• Maintenance cost as a percentage of replacement asset value—world-class operations have this percentage at less than 2%
• Maintenance cost per production unit—with the caveat about maintenance spend tracking, this is perhaps a better measure of maintenance efficiency
• Maintenance work efficiency—assuming you have an idea of how long a task should take, you can see if your technicians are spending the appropriate amount of time on work. This can lead to theoretical workloads, overtime plans, and other metrics.
• Overall Equipment Effectiveness (OEE) is one metric many businesses use for a comprehensive measure of the operation. This measure is calculated by multiplying availability (inverse of downtime), performance (how much output versus target), and quality (yield of good output).
• Mean Time to Repair (MTTR) is calculated by dividing the downtime length by the total number of downtime events. MTTR can determine criticality of an asset or areas where preventive tasks need to be added.
• Mean Time Between Failure (MTBF) is the average time between failures on an asset. This is an important determinant of maintenance priorities.
• Mean Time to Implement Predictive Recommendations is another twist on MTTR and MTBF.
• Percent of Predictive Recommendations Implemented can be a measure of how much importance has been granted to the predictive models and whether they are providing valuable work.
• Maintenance Backlog—the backlog in work orders or labor time. If it grows over time, this KPI could indicate a further issue.
• Spare Part Inventory Turns—measuring the inventory turns will indicate whether the inventory is needed or tying up capital on a shelf.
When enacting any of these KPIs, start with a baseline measurement of the indicator. Depending on what you are measuring, it can take a few weeks to a few months to gather a large enough sample.
With more intelligent KPIs such as these, you can set intelligent goals for your maintenance department. It may seem appealing to start tracking all the KPIs above, but it is more meaningful when a few reasonable and specific goals are established.
For example, you could set a goal to increase the percentage of predictive maintenance on one asset from 0% to 50% in 12 months. Another option would be to improve the MTTR in an area by 50% in six months. The idea is to have something to strive for that can be easily measured and understood by all involved.
As you move forward in your predictive-maintenance journey, understanding the effectiveness of a program is important. Time wasted on ineffective predictive maintenance can cause many downstream effects and turn many employees off to the strategy.
With intelligent KPIs, specific goals can be established and tracked. By using this strategy, a predictive-maintenance program can be measured and improved, taking your maintenance to the next level. EP
Bryan Christiansen is the founder and CEO at Limble CMMS, Lehi, UT, (limblecmms.com). Limble is a mobile CMMS software that aids in organizing, automating, and streamlining maintenance operations.