Leading us into better performance

Developing and implementing a measurement program for managing performance at your site is always a good idea. Regardless of how good you think you are doing, or how little downtime you actually have, measurement programs can always highlight areas of improvement and areas where you could take a closer look.

However, one of the common problems of relying on measurement systems is that it is often like driving down the highway looking in the rear view mirror. Instead of seeing where you are going you are looking at where you have been. How many times have you looked at the metrics for that month and thought “It would have been good to know about that before it happened!”
This is the fundamental problem of performance indicators as a guide to plant performance, because they work by displaying historical transactional data everything you see in a metric has already occurred, this is because for many years metrics have been seen as lagging indicators of performance.

Even though it is reactive it still gives us an insight into the causes of problems, their frequency and a range of other information that we can use for improvement. But if we wish to use metrics and measurement systems as part of a corporate asset management approach, then there is a need to use them to tell us about problems before they happen.
These types of indicators are termed leading metrics, and as the name suggest the lead performance, or tell us what is likely to happen with some aspect of performance in the future. Defining leading indicators is often challenging, and requires a totally different view of measurement and how it can assist you.

Here are some tips for developing your own leading metrics to manage your physical asset base, if you think of any additional ones I would like to hear about them so please send me an email. The underlying principle of all of these techniques and areas is that they all lead performance.

• A new look at old metrics – Often companies are employing leading metrics without even knowing about it. A metric that I often quote is that of schedule compliance. At first glance this metric is telling us how we did in completing last weeks schedule as planned.

But if we look at it another way it can also be telling us about the level of risk that we are faced with. In the area of routine maintenance, all of the tasks we do are set at a certain frequency for a reason.
At that frequency we can be sure of capturing the early signs of failure, reduce the likelihood of an in-service failure, or ensure the risk of a multiple failure is to a tolerable level. So if they are done late, or deferred, then you know that an element of risk facing the plant has increased a little bit higher.

It doesn’t tell you exactly what and when, bit it is leading because it indicates that things could start to go wrong. As the number of missed schedules increases, so too does the risk. A report like a “Missed Schedules Report” or something similar is often of use when it is used in this way.

• Tying in with predictive technologies – Every single application of predictive technologies is leading in nature. All of them are looking for the warning signs of failure. The signs that something very specific is about to go tragically wrong.

Try to tie in with any online condition monitoring information sources that are available and displaying them in a way that warns of potential dangers or use captured data from visual inspections to grade how likely a failure is.

• Using predictive techniques – Weibull, RBI, and RCM are all methodologies that contain an element of predictive thinking about them. .Aside from the condition monitoring element of each of these, there is often an attempt to try to gauge remaining life and calculate risk accordingly.

There are often doubts abut the accuracy of these due to the nature of failure data in asset management, however in this instance they do not need to be 100% accurate, they just need to warn of potential dangers.
Setting up some form of standard Weibull calculator on failure data of critical assets, with a view to predicting end-of-life is not as difficult as it once was. Like other methods it could be done through modern reporting tools and a CMMS, or through dropping data into a spreadsheet of database system locally.

• Look at the process – This is the area where probably the most proactive measures can be gleaned from. By measuring elements of the work processes in place we can get a view of when they are going wrong and use that to infer future asset performance.

For instance, most company categorizes corrective and reactive work orders with priorities. These are often linked to the severity of the consequences over a period of time. An example could be a high level of vibration on a pump calling for a replacement item. If the work order is a priority 2, say, then it could mean that if no action is taken within two weeks the risk of failure rises considerably.

Again this is not 100% accurate, but it doesn’t have to be. A late corrective work order tells us that performance could take a nose dive soon, so it provides us with an early warning system.
Another example could be a growing percentage of delay codes of some sort or other in work order reports. This could be warning us that there is a growing bottleneck in the process that is going to impact on our time to return to service.

Conclusions: Although some of these are difficult at first, all of them are achievable using even modest systems available in today’s information marketplace. I hope this has been useful for you to shake up your thinking about how metrics can be used to predict performance.

However, another point I wanted to make is the vital importance of asset data to modern asset managers.

Our area produces reams and reams of data, and if we are going to effectively and efficiently manage physical assets then there is a need for us to tie into that resource to make high confidence decisions regarding asset performance.