Tips to improve reliability via solid data management

March 2, 2007
Data management is an oft-abused reliability practice resulting in numerous company failings, including large-scale asset management mistakes. These tips from our Reliability Expert Daryl Mather will ensure that your plant is gathering and analyzing the right set of critical data.

This month’s column focuses on an area that you either love or hate; data manage-ment. I think it is worthwhile speaking about as it is one of the vital areas that support modern asset management. 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. 

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Technological advances relating to how asset managers capture, organize, and use asset related data is, without a doubt, the most important advance that we have made within the 20 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.

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. Recently, I have seen many corpo-rations spending millions of dollars on areas of asset data management that are dubi-ous at best, counterproductive at worst.

The humble CMMS is the centre of most asset maintenance efforts to capture, store and analyze asset data. In larger operations, this has been superseded by Enterprise Asset Management, EAM, and Enterprise Resource Planning, ERP, systems but the goal remains the same.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 increas-ingly easy to use. Yet there are still asset maintenance departments that operate with-out 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 there to truly effective and dynamic work flow management. They remain stagnant, going in ever decreasing analytical circles focusing 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? Because it can highlight areas of inefficiency, aid effec-tive reliability management when applied through a framework of RCM logic, reduce inventory, provide defensible asset replacement plans, and increase profitability. These benefits, however, turn out just to be ancillary to the main benefit.

Even today, many maintenance companies make 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 made based on strength of charac-ter, political manoeuvring, and coalition building. In summary, we can basically apply the old cliché “the squeaky wheel gets all the grease.”

When a company begins to make decisions based on data rather than on opinion, the entire dynamic of the company changes. Instead of influence and story-telling, deci-sions become based on fact; anecdotal benefits are replaced by provable and support-able 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 iden-tifiable 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 make asset mainte-nance and asset management decisions based principally on data? The following are some tips. I have used them over the years to help numerous companies to advance their decision support frameworks.

1.  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 pro-grams aimed at perfecting data quality, integrity, and on monitoring volumes of data. If this needs to be done, and often that is a point for debate, then it should only be done after you have identified the critical data to be analyzed.

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 sup-porting 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.

2.  Be careful of trying to lock users into a 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 en-ter 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 re-quires 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 alto-gether. A better approach it to train people on what to do and why, ensure that excep-tion 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 were made.

3. 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 make decisions and ex-ercise judgement over asset information.  It can be done through training clerical peo-ple, but this will always be limited in application.

Asset maintainers and managers need to be the ones reviewing asset data exception reports to ensure they meet minimum criteria. The practice of 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 fol-lows existing guidelines

None of these are particularly difficult if they are done regularly. The alternative is to hire an 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 about some of the work orders, redefining the priorities, and requests for further information.
Backlog reports – Regular reviews of the work order backlog to ensure that garbage work orders are identified and either corrected or eliminated. This is useful for the weekly or bi-weekly planning meetings.

Work order closure reports – Run daily and checked to make sure that the informa-tion is correct and can be of future use. A separate but similar report is end-of-month cost reports. In a 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 do-ing any activity based reviews always bear in mind how the data could be used in the future.

4. Check out mobile and other automatic data capture technologies.

Today there are a lot of technologies available that are not terribly expensive to im-plement. 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.

There are many ways to get better data without spending a fortune doing so. Like eve-rything else in asset maintenance, the core of it focuses on process development, be-ing 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 are small, and the gains include taking a fact-based approach to asset maintenance in your company.

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