Using Your Maintenance KPIs to Plot Distribution Curves as Business Performance Indicators of the success of Your Maintenance Performance Improvement Efforts

The email discussion with David tells how to use maintenance KPI distribution curves to show whether your maintenance performance improvement efforts are working. A maintenance improvement initiative needs to improve maintenance performance results. You can immediately see how successful you are in the KPI probability distribution curves of your maintenance KPIs.


Dear Mike,

I am Mechanical Maintenance Engineer in a manufacturing company. I am struggling in writing a convincing Engineering Report. What really is it that I must include in the report?



Hi David,

What are you reporting on? Normally in an engineering report you describe your findings and recommendations, supported by your analysis of the historic data you collected.



Thank you Mike,

I am required to report on the performance of the maintenance department. In other organisations I have worked for I used to include all maintenance KPIs in the department monthly maintenance report. But my boss at this company seems to want something else, however the requirements are unclear. Could there be a generic way of doing maintenance KPI reports?

The organisation is rapidly growing from being a small family business into a large state-of-the-art corporation. I am still developing and formulating an equipment improvement program and its step-by-stepimplementation.

By the way, your website is my knowledge bank when it comes to information on maintenance and reliability.



Thanks for the kind words about the worth of the material on the LRS Global website, and also for your ongoing support.

I can suggest you do one more thing with your maintenance KPI’s. Plot each maintenance performance KPI probability distribution curve and also show those curves to your manager.

Divide the historic KPI range or span into five percent proportions and count the number of times a historic maintenance KPI value fell into a range. The total count of values in a range represents the frequency the respective maintenance KPI value occurred in the historic data. You then plot the curve of the historic frequency distribution. Put the five percent ranges on the x-axis and the frequency count on the y-axis.

Try and get many historic data points for each maintenance KPI to enable you to identify the widest range of values that arise with each maintenance performance indicator you use.

Your KPI frequency distribution plot is a reflection of the probability or chance of where the next KPI value you report to your manager will fall. The shape of each distribution curve is a perfect reflection of your past maintenance performance. It is also an indicator of your future maintenance performance.

Each time you report the maintenance KPI to your manager also show its position on the KPI distribution curve. The location of the KPI value on the curve will tell you immediately whether your maintenance KPI improvement initiatives are working successfully or not. If they are successful the KPI value will fall into the ‘good outcome’ bands of the performance distribution plot. If the KPI result falls into the wrong part of the curve you will know that nothing good has yet happened from what improvements you have done.

Showing the distribution curve of each maintenance KPI gives a manager a complete overview of what results he or she can expect from the operation. Very often the KPI distribution curves are unsettling because the business performance they indicate is so bad.

The shape of the maintenance KPI distribution curves have meaning. A wide distribution curve indicates an unstable operation full of randomness. Such results can only come from the use of unstable business processes in the company. The location of the peak will be at the most commonly repeating results. Those KPI results are the performance your maintenance processes will most commonly produce. A high peak in the poor region of the curve indicates you have many problem causes that randomly impact your maintenance KPIs. A tight, high curve in the good KPI results region of the plot is what you want.

Once you model the probability of maintenance KPI outcomes you see the real truth of what your company is experiencing from its maintenance efforts. You can then investigate what events caused the shape of the distribution curve and put into place the right maintenance process improvements to drive your KPI probability curves to the right position.


My best regards to you,

Mike Sondalini
Managing Director
Lifetime Reliability Solutions HQ


P.S. If you require advice on industrial asset management, industrial equipment maintenance strategy, defect elimination and failure prevention or plant and equipment maintenance and reliability, please feel free to contact me by email at


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