Captured by Data Part 1

by Mr. Mather
Posted 8-1-06

Enterprise Asset Management Systems (EAM) and the aims of modern maintenance
Since the late 1980’s EAM vendors throughout the world have pitched their products based partly on the ability to capture, manipulate, and analyze, historical failure data. Part of the stated benefits case is often the ability to highlight the causes for poor performing assets, provide the volume and quality of information for determining how best to manage the assets, and informing decisions regarding end-of-life and other investment points.
This benefits case covers the principal drivers for most maintenance managers today and it has been used to justify millions of dollars worth of investment, and has place the modern EAM system at the centre of corporations that are driving to improve asset performance. On the surface it appears to be a logical approach for problems relating to asset performance, and using this approach companies do, of course, achieve results.
The implementation of these products, when bought for these reasons, often focuses on optimizing processes to capture the dynamic data on asset failures, which is then used throughout the system. MRO style inventory management algorithms, for example, use this information as one of the key inputs to determine minimum stocking levels, reorder points and the corresponding reorder quantities.
If we want to understand the validity of this line of thinking it is necessary to first explore the aims of maintenance, and how asset data can be used to further those aims.
“Maintenance” is a term generally used to define the routine activities to sustain standards of performance throughout the in-service, or operational, part of the asset life cycle. In doing this, the maintenance policy designer needs to take account of a range of factors. These include the complexities of operating environment, the available resources for performing maintenance, and the ability of the asset to meet its current performance standards.
In the past, this would be the extent of the maintenance analysts’ role. One of the realities they face is that at times assets are under a demand greater than, or extremely close to, their inherent capabilities. As a result analysts often find themselves recommending and analyzing activities of not only maintenance, but also other areas of asset management, namely those of asset modification and operations.
Safety and environmental compliance play their part in creating the drive for this activity, particularly given the changing legal and regulatory frameworks around these two areas; in some industries they are even the principal drivers. However for most businesses the goal remains that of maximum value from their investment. This means getting the maximum performance possible from the assets, for the least amount spent.
In the original report and appendices that produced Reliability-centered Maintenance (RCM) the authors defined critical failures, initially, as those failures with an impact on safety. Today the term “critical failure” is often used to group failures that will cause what companies consider to be high-impact consequences, a definition that is too variable for a general discussion. For the sake of simplicity, “critical failure” in this paper refers to all failures that will cause the asset to perform to a standard less than what is required of it.
If an asset management program is aimed at maximum cost-effectiveness over an assets life, then it must look at the management of critical failures. By definition, this approach is centered on the reliability of the asset. (Or reliability-centered)
So, in essence, the role of the policy designer can be defined as the formulating cost-effective asset management programs, routine activities and one-of procedural and design changes, to maintain standards of performance through reducing the likelihood of critical failures to an acceptable level, or eliminating then. This is also the essence of modern RCM.

The data dilemma
Immediately we start to see a contradiction between the aims of maintenance, and those often quoted of EAM systems. Non-critical failures are those of low or negligible cost consequences only. These are acceptable and can be allowed to occur. Therefore a policy that focuses on data capture and later analysis as its base can be used effectively. Over time the level of information will accumulate to allow asset owners, and policy designers, to determine the correct maintenance policy with a high degree of confidence.

However, critical failures, those that cause an asset to under perform, have unacceptable consequences and cannot always be managed in a similar way.  For example, if a failure has high operational impact or economic consequences, then allowing it to fail prior to determining how to manage them is actively counterproductive to the aims of cost effective asset management. Moreover, recent history reinforces the fact that failure of assets can lead to consequences in safety or breaches of environmental regulations.
So, if our policy for determining how best to manage physical assets is based around data capture, then we are creating an environment that runs counter to the principles of responsible asset stewardship in the 21st century.
The underlying theories of maintenance and that of reliability are based on the theory of probability and on the properties of distribution functions that have been found to occur frequently, and play a role in the prediction of survival characteristics.
Critical failures are, by their very nature, serious. When they occur they are often designed out, a replacement asset is installed, or some other initiative is put in place to ensure that they don’t recur. As a result the volume of data available for analysis is often small, therefore the ability of statistical analysis to deliver results within a high level of confidence is questionable at best.
This fundamental fact of managing physical assets highlights two flaws with the case of capturing data for designing maintenance programs. First, collecting failure information for future decisions means managing the asset base in a way that runs counter to basic aims of modern maintenance management.  Second, even if a company was to progress down this path, the nature of critical failures is such that they would not lend themselves to extensive statistical review.
By establishing an effective, or reliability centered, maintenance regime, the policy designer is in effect creating a management environment that attempts to reduce failure information, not increase it. The more effective a maintenance program is, the fewer critical failures will occur, and correspondingly less information will be available to the maintenance policy designer regarding operational failures. The more optimal a maintenance program is, the lower the volume of data there will be.