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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.

Good luck with your leading metrics programs!

First published by www.plantservices.com

As a seasoned professional you have probably had your fair share of experience with selecting and implementing reliability analysis programs for your company. Every time it starts off great, the pilot program runs well, and the forecast benefits seem out of this world. Why wouldn’t your company take this forward? It seems the obvious thing to do.

Then after a while the benefits don’t materialize, the boss is keen to know where the ROI is for the pilot program, and you are facing an increasingly difficult time in getting any further improvement programs approved. Sound familiar?

In my experience many reliability programs fail because of poor implementation. What we sometimes neglect to consider is that implementation of reliability initiatives, particularly those aimed at optimizing or improving the maintenance regimes, doesn’t stop at the production of maintenance regimes; we need to turn these into reality and monitor their execution before we can truly say we have implemented a reliability program.

In my book, The Maintenance Scorecard, I detail why it is so important that maintenance schedules be executed according to the frequencies they have been assigned. Maintenance scheduling is often linked to improvements in workforce efficiency, in fact I have come to the belief that if your company is looking to get sustainable improvements in operating efficiency, then short term capacity scheduling is one of the must have capabilities within the team. However, there is another side to the benefits of maintenance scheduling, that of minimizing the risk of an unacceptable failure.

When we establish reliability regimes, say on an electrical power transformer with a tap changer device, each of the tasks and the frequencies have been chosen for a good reason. First they represent the best value for money, second they reduce the likelihood of unacceptable failure to a level that we can tolerate, and third they are driven by the characteristics of the failure mechanisms they are managing.

With a power transformer there are a range of tasks that should be done regularly. A good example of this is oil analysis. This can tell us a range of things regarding the state of the transformer in general, as well as the state of the oil itself. Testing the oil for impurities, and break-over voltage in particular, tells us a lot about the rate of degradation of the insulation materials on the coil and the rate of breakdown of the oil over time.

Using the basic method for establishing condition monitoring inspection intervals, the oil is tested at a frequency which we are sure will allow us to capture the initial warning signs of failure and to allow us time to act when we do find something. So, what happens if we execute the task late? We risk not catching the warning signs in time, so there is the risk of an unpredicted and unexpected failure on a large scale. Also, we risk not having enough time to do anything about it even if we did capture the warning signs in time!

Everywhere you look in your plant there are similar examples of this. Other condition monitoring examples could include not testing the motor bearing for vibration in time or not testing H.V. joints for excess heat with thermo-graphic test equipment. In both cases we are reducing the time we have to act on information, and we are increasing the likelihood that there will be an unpredicted failure event.

Even preventive maintenance regimes, as opposed to predictive and other maintenance types, there are examples of slipping maintenance with a direct effect on the likelihood of failure. For example, failure to grease a bearing in time increases the exposure to the risk of damage to the bearing due to breakdown of the lubrication film, a failure to check and adjust belt drives on time also exposes the plant and equipment to an increased likelihood of an unplanned failure event.

All of these lead to a greater level of risk that we are going to experience an equipment failure that we would prefer not to have, one that could cost us a lot of cash, or worse if there are safety and environmental concerns.

But it doesn’t end there. As all of you will know, good maintenance resource management relies on the principles of capacity scheduling. That is, scheduling the maintenance workforce to the realistic limits of its available working hours. This means not only the routine maintenance tasks, but also the corrective and lower priority reactive works.

Corrective and reactive work (yes, I differentiate between the two) are allowed to enter the maintenance backlog on the condition that they are not going to have an imminent adverse effect. It would be great if we all had the workforce hours, spare parts availability, and unlimited downtime allowance to fix everything when it is first noticed. But we don’t, and we never will have. So realistically the maintenance backlog will always exist.

Prioritizing work in progress something your workforce will do anyway as part of the way they manage physical assets, often without even realizing it. The priority program will be related to time in some fashion, therefore is imperative that your maintenance schedule always includes the highest priority corrective tasks first.

For example, a noisy pump is okay at the beginning, maybe even a week later it will still be okay. But what about three weeks later, when the noise has become a lot louder? Now it is one of the things that keep you awake at night now, you realize that if this task is not done soon, then your plant is at risk of an unplanned, and unwanted, failure event.

Understanding how to carry out capacity scheduling is a skill that most planners require and is relatively easy to master. What is difficult is having the ability to convey exactly why this is required, and to have the level of discipline within the workforce to execute it continually.

There are several results that I have seen when capacity scheduling is applied. First there is a change in reliability, worker efficiency and the bottom line of maintenance. This is even more apparent when the maintenance regimes have been optimized. Second, and more noticeably, there is a stark awakening about how much (or how little) time there is in each day that is available for tool time.

This doesn’t even take into account the impact of work delays, stock-outs and other issues. So even before the first nut is tightened we become immediately aware of the potential for getting even greater efficiencies’ from the maintenance workforce.

The blog is usually dedicated to issues relating to asset management and how to overcome some of the problems that we are all being faced with in our day-to-day duties.

However, every now and then I like to take some time to introduce you all to a charity that my family and I support called water aid.

WaterAid and its partners use practical solutions to provide safe water, effective sanitation and hygiene education to the world’s poorest people. They also seek to influence policy at national and international levels.

www.wateraid.org is where you will find them.

We have been supporting this charity for many years and they do great work. One of the things that grabbed me with regard to this organisation is that they focus on infrastructure, on which everything else is based.

I hope you would consider it and recommend it to your companies.

Best of luck.

This month’s column focuses on an area that you either love or hate; that of data management. I think it is worthwhile speaking about as it is one of the vital areas that support modern asset management, and it is an area that we often neglect, creating opportunities for people with less than adequate levels of asset management knowledge to make large-scale mistakes.

Technological advances relating to how asset managers capture, organise, and use asset related data is, without a doubt, the most important advance that we have made within the twenty years that I have been consulting in this area. The implications of this are immense and it has the ability to permanently change the way that your company manages its physical asset base.

On the other hand new technologies can also bring with them new dangers. Not the least of which is the potential for allocating a lot of time and money on tools, services and activities that do not support the central goals of asset management. In recent time I have seen many corporations spending literally millions of dollars on areas of asset data management that are dubious at best, counterproductive at worst.

The humble CMMS is the centre of most asset maintenance efforts to capture, store and analyse asset data, in larger operations this has been superseded by Enterprise Asset Management, EAM, and Enterprise Resource Planning systems, ERP, but the goal remains the same. These are supplemented by mobile working and bar coding solutions, GIS integration, RFID tagging systems, online condition monitoring, plant management systems, and a range of detailed analytical software tools.

These technologies are now abundantly available; their costs are becoming affordable in terms of generating a good return on investment from them, and they are increasingly easy to use. Yet there are still asset maintenance departments that operate without even a CMMS, or worse, use only a fraction of their existing system.

A common issue is where companies implement a system, starting with asset register information, and then never progress from here to truly effective and dynamic work flow management. They remain stagnant, going in ever decreasing analytical circles focussing on collecting reams of static data, often without a real cost-benefit analysis of how they are going to use it.

So why is this so important? If you ask any of the hundreds of companies specialising in data management and cleansing they will tell you all about how it can highlight areas of inefficiency, aid effective reliability management when applied through a framework of RCM logic, reduce inventory, provide defensible asset replacement plans, and increase profitability. But all of these are actually side issues.

Even today, many maintenance companies take decisions, often very large decisions, based on expert judgement, opinion, and anecdotal information. If we look at this critically we can see that often these decisions are taken based on strength of character, political manoeuvrings, and coalition building. In summary we revert to the situation where “the squeaky wheel gets all the grease”.

When a company begins to take decisions based on data rather than on opinion, the entire dynamic of the company changes. Instead of influence and story-telling, decisions become based on fact; anecdotal benefits are replaced by provable and supportable business plans.

One of the key benefits of data based decisions is that projects stop being initiated based on spurious claims and start being judged based on their ability to achieve identifiable targets. This alone is a strong reason for any company to seriously get into the data-capture and analysis business!

So how does your company get to the point where they are able to take asset maintenance and asset management decisions based principally on data? The following are some tips that may be of use to you, I have used them over the years to help numerous companies to advance their decision support frameworks.

• Don’t focus on the quality, volume and integrity of the data until you know what data should be collected!

It has been my experience that a great deal of money is spent, often unwisely, on programs aimed at perfecting data quality, integrity, and on monitoring volumes of data. If this does need to be done, and often that is a point for debate, then it should only be done after you are sure what data you should be collecting in the first place.

Many companies spend several man years in collecting static, nameplate, data that will be of little or no use. And while doing so they often ignore other areas where data can give an immediate effect and impact.

If you want to define your data sets, then I strongly recommend that you look at what you need it for first. This is a top-down approach that has helped me immensely in the past. By defining the goals and requirements, you are easily able to define the supporting information. (See The Maintenance Scorecard for more information on how to apply this practically)

As a side note, ensuring you have a good understanding of the baseline maintenance regimes through something like RCM will help significantly in this exercise.

• Be careful of trying to lock users into specific way of working. Flexible ways of working, combined with process controls and exception reporting will always provide better results over the long term than locking an interface.

I mention this because I have seen many cases where a lot of time and effort has been dedicated to locking an interface. The goal is to ensure that users only fill out certain fields, to make sure they fill all of the fields out, and to make sure that they don’t enter things that don’t comply with the rules you have imposed.

The intention is great; the reality is less than great. When faced with a screen that requires certain information before allowing them to proceed, users often get frustrated and annoyed and instead of entering what they are required to; they enter garbage or nothing at all!

So instead of improving the asset information this approach often reduces it altogether. A better approach is to train people on what to do and why, ensure that exception reporting is part of somebody’s role so that errors in data entry can be picked up in the short term, and make sure there are feedback loops in place to tell people where mistakes where made.

• Integrate data quality activities into the day-to-day work of existing roles, don’t put it off to one side and assign it to an “asset data” department, or worse, to the IT department.

Asset managers and maintainers are always the best placed to take decisions and exercise judgement over asset information. It can be done through training clerical people, but this will always be limited in application.

As such asset maintainers and managers need to be the ones reviewing asset data exception reports to ensure they meet minimum criteria. So tying this activity into their day-to-day roles is often an easy thing to do. (As long as it doesn’t become yet another data review and survey exercise) Areas where exception reporting is useful include:

• On work order creation, ensuring that all the relevant minimum information has been listed, that the coding represents how the processes were intended to be used, and that any free text meets “good information” standards.
• During planning to ensure that planning codes and information is correct
• During work order completion to ensure that all relevant information is in the correct place
• Review static information on change to make sure that new information follows existing guidelines

None of these are particularly difficult if they are done regularly, otherwise there may well be a need for a very expensive consultancy to come in a “scrub the data”.

Some tools that may be of use:

24 hour reports – A report of all of the corrective and reactive works that have arisen within the last 24 hours to management for their discussion at morning meetings. The outcome will be agreement over some of the work orders, redefining the priorities, and requests for further information. All aids in making sure the data is right from the get-go.

Backlog reports – Regular reviews of the work order backlog to ensure that garbage work orders are identified and either corrected or eliminated, useful for the weekly or bi-weekly planning meetings.

Work order closure reports – Run daily and checked to make sure that the information is correct and that they are going to be of use for future use. A separate but similar report is that of end-of-month cost reports. In similar fashion these allow planners and managers to run through all of the items to ensure they have been attributed to the correct cost centres. When doing any activity based reviews always bear in mind how the data could be used in future.

• Check out mobile and other automatic data capture technologies.

Today there are a lot of technologies available that do not cots an arm and a leg to implement. This is particularly true in the field of mobile technology, barcode scanning, and smart tags. Depending on how they are used all of these items can assist you to capture reams of important data without one key being pressed.

• Do not focus on volume!

One of the great misconceptions about asset data is that rising volumes of data is a good thing. In some areas it is okay while in other areas it indicates second rate performance.
Part of the role of any maintenance department is to effectively manage critical failures. This means that they put in place strategies, plans, and initiatives that are aimed at reducing the amount of critical failures an asset has. So, instead of increasing asset failure data, over time it should be decreasing.

Other areas are different but similar principles apply. For example, work order volumes have nothing at all to do with any measure of maintenance performance. Every company uses work order differently, some at the system level, some at the plant level, some at the asset level and some at the component level. Furthermore, some companies have different work orders for different disciplines even though they are on the same task. Volume is irrelevant, hours worked is vital!

There are many ways to get better data without spending a fortune doing so. Like everything else in asset maintenance the core of it focuses on process development, being smart about the way we use our resources, and having a good understanding of why we are doing certain activities in the first place.

The costs of doing this should be small, and the gains include taking a fact based approach to asset maintenance in your company. Definitely a good return on investment!

I hope this information is of some use to you and good luck with your data improvement initiatives!