Captured by Data: Enterprise Asset Management Systems (EAM) and The Aims of Modern Maintenance
Captured by Data: Enterprise Asset Management Systems (EAM) and The Aims of Modern Maintenance
Captured by Data: Enterprise Asset Management Systems (EAM) and The Aims of Modern Maintenance
Unacceptable: High
cost, impacts on safety
or management of the
environment
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 iv or
breaches of environmental regulations. v
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. vi
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. vii The
more optimal a maintenance program is, the lower the volume of data there will be.
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There are still other key reasons why data from many EAM implementations are of
limited value only. Principal among these is the fact that even with well-controlled
and precise business processes for capturing data, some of the critical failures that will
need to be managed may not yet have occurred. An EAM system, managing a
maintenance program that is either reactive or unstructured, will only have a small
impact on a policy development initiative.
At best they may have collected information to tell us that faults have occurred, at a
heavy cost to the organization, but with small volumes of critical failures and limited
information regarding the causes of failure. RCM facilitates the creation of
maintenance programs by analyzing the four fundamental causes of critical failures of
assets, namely:
Poor asset selection (Never fit for purpose)
Asset degradation over time (Becomes unfit for purpose)
Poor asset operation (Operated outside of the original purpose)
And, exceptional human errors (Generally following the GEM viii
principles)
The RCM Analyst needs to analyze all of the reasonably likely ix failure modes in
these four areas, to an adequate level of detail. Determining the potential causes for
failures in these areas, for a given operating environment, is in part informed by data,
but the vast majority of the information will come from other sources.
Sources such as operators’ logs are strong sources for potential signs of failure, as
well as for failures often not found in the corporate EAM. Equipment manufacturers’
guides are also powerful sources for gleaning information regarding failure causes and
failure rates. However, all information from a manufacturer needs to be understood in
the context of how you are using the asset, and the, often conservative, estimates of
the manufacturer. For example, if there are operational reasons why your pumping
system is subject to random foreign objects, for whatever reason, then failure rates for
impeller wear can become skewed.
Other sources of empirical data can be found in operational systems such as SCADA
or CITECT, commercial databanks, user groups, and at times consultant
organizations. Similarly to information from manufacturers there is a need to
understand how this applies to the operating environment of your assets. As asset
owners require more and more technologically advanced products, items come onto
the market with limited test data in operational installations, further complicating the
issues of maintenance design through data.
The factors that decide the lengths that an RCM Analyst should go to collect
empirical data is driven by a combination of the perceived risk, (probability x
consequence), and of course the limitations set on maintenance policy design by
commercial pressures. Even when all barriers are removed from the path of the RCM
Analyst, they are often confronted faced with an absence of real operational data on
critical failures.
The vast majority of the information regarding how the assets are managed, how they
can fail, and how they should be managed, will come from the people who manage
the assets on a day-to-day basis. Potential and historic failure modes, rate of failure,
actual maintenance performed (not what the system says, but what is really done),
why a certain task was put into place in the first place, and the operational practices
and reasons why, are all elements of information that are not easily found in data, but
in knowledge.
This is one of the overlooked side-benefits of applying the RCM process, that of
capturing knowledge, not merely data. As the workforce continues to age, entry rates
continue to fall in favor of other managerial areas, and as the workforce becomes
more mobile, the RCM process, and the skills of trained RCM Analysts, provides a
structured method to reduce the impact of diminishing experience.
Predictive Maintenance
As detailed in Figure 3 below, Predictive Maintenance (PTive) tasks are established to
try to detect the warning signs that indicate the onset of failure, thus allowing for
actions to be taken to avoid the failure. Yet there is also another aspect of PTive tasks
that is often overlooked. That of the corrective, or Predicted (PTed), task once
warning signs have been detected.
Immediately following the analysis, the information established at this point can be
used for creating proactive whole-of-life costing models that are directly tied to
performance and risk.
Functional Failure
Preventive Maintenance
Where Predictive Maintenance tasks cannot be applied, for whatever reason, the next
two options on either side of the decision diagram are Preventive Maintenance tasks.
These are tasks that are aimed at either restoring an assets resistance to failure (PRes),
or replacing the asset at a time before the failures can occur. (PRep) Thus preventing
failures. These tasks have limited use and are based on age, usage, or some other
representation of time.
When applied correctly these tasks are part of the approach to maintenance that, by
necessity, reduces the volume of failure data available for statistical analysis.
However, with the component out of the operational environment, it can safely be
tested to try to establish the extent of its remaining economically useful life.
Whole-of-life cost of an asset, or component, subject to Preventive Maintenance tasks
(Cost (PRes) - or - (Cost (PRep)
This is an additional task and one that would not be generated from the RCM analysis.
Yet it represents another aspect of responsible data capture and is an important
element of businesses where confidence in statistical life prediction, and whole of life
costing models, are of importance. xi
Detective Maintenance
As with predictive maintenance tasks there are actually two tasks that are being
implemented here. First the detective (DTive) maintenance task, and second the
detected (DTed) maintenance task. The result of this is the same as with the predictive
maintenance tasks. That is, it provides further information about the likely failure rate,
collected in a responsible manner, which can be used to inform decisions regarding
optimizing the frequency of this task.
Run-to-Failure
The last policy option, aside from redesign and combinations of tasks, is that of run-
to-failure. This option is for the acceptable, or low / negligible cost, failures detailed
in figure 1.The EAM will allow these failures to be captured for analysis to inform
whole of life cost models, spending forecasts, and to be used in reviewing
maintenance policies when relevant.
Figure 6. Tasks involved in Run-to-Failure policies
Along with the responsible data capture forced by these policy options, configuring
and managing the EAM in line with RCM thinking will also allow visibility of
exceptional failures.
Due to the way that RCM is, by necessity, carried out, there is the possibility that
some failures may be missed. Modern methods of execution have expanded the
original default method of team-based analyses to include expert analysis sources
outside of the team, but there always remains the possibility that the analysis will miss
a critical failure despite the best efforts of the analyst and those involved.
In these circumstances the data recorded in these exceptional failures will provide the
impetus for the analyst to revisit the analysis to factor in this failure mode and to put
in place a relevant management policy. It is not an area that is used for capturing data
for statistical analysis and is, as the name suggests, the exception rather than the rule.
It can be seen that part of the role of the modern RCM Analyst is not only to minimize
the volume of failure data that is collected for later analysis, but also to maximize the
quality and usability of data that is captured via collection methods that support the
principles of responsible asset stewardship. It can also be seen that advances in
modern technology, combined with the growing needs of asset intensive companies,
have enabled this information to be used in newer and more comprehensive ways than
originally conceived of and correspondingly, not mentioned in previous work on
RCM.
In particular it fuels the shift by the company away from the Static methods of life
cycle costing, and towards the Proactive methods of whole-of-life costing. This is a
step that enables companies to set up the data capture techniques and practices
required to propel it towards the Stochastic, or probabilistic, model of whole of life
costing.
Yet the data collected through establishing an effective maintenance program allows
the company to generate a range of leading indicators. Measures that lead
performance, or tell you that something is likely to begin to perform badly before it
actually does.
The diagram in Figure 7 depicts the relative impact of these areas of leading
indicators, and the smaller impact of performance measures established in the
traditional lagging approaches. These are the key areas of the RCM Scorecard, a tool
first published in the book, The Maintenance Scorecard xii , and the subject of a
separate article in this area.
However, the basic thrust of the RCM scorecard is to allow companies to measure the
effectiveness of their maintenance policy initiatives. Through applying measures to
the data captured in the course of doing the day-to-day work, RCM Analysts are able
to establish things such as:
Is it more cost-effective to manage the asset, over its whole-of-life profile, or not?
(Leading to incorrect whole-of-life management, not just costs)
Was the task really more cost effective than the estimates of failure? (Leading to
incorrect whole-of-life costs)
Was the cost of failure really more cost effective than the estimated costs of the
maintenance policy? (Leading to incorrect whole-of-life costs)
Are the tasks actually predicting or preventing failures?
What is the increase in risk due to late performance of DTive tasks? (Leading to
higher than acceptable levels of risk exposure)
What is the increase in risk due to late performance of PRes or PRep tasks?
(Leading to higher than acceptable levels of risk exposure)
What is the increase in risk due to late performance of PTive tasks? (Leading to
higher than acceptable levels of risk exposure)
The actual measures contained within The RCM Scorecard are detailed fully within
the book. It provides, arguably, a stronger level of benefit to a company than direct
measures because it allows them to tap into the results of mainly leading indicators,
thus heading off poor performance before it appears on the management report.
Regardless of the actual measures used, the point remains that this is only possible
due to the creation, in the first instance, of the effective maintenance program.
i
MRO stands for Maintain, Repair, and Operate and is an acronym widely used within the EAM/ERP
industry and associated with inventory management from an asset perspective rather than from a
production perspective. The difference is that with ERP style inventory management the focus is on
“just-in-time” methods. While MRO style inventory management focuses on “just-in-case”, or
probabilistic methods.
ii
The author acknowledges that the definition of what is an acceptable, or unacceptable, standard of
performance is an extremely complicated area and one that would take several articles to cover in
adequate detail.
iii
Within asset management cost-effectiveness is not merely low direct costs. Rather the minimum
costs for a given level of risk and performance. (Maximum value)
iv
The Iowa Division of Labor Services, Occupational health and Safety Bureau, issued a citation and
notification of penalty to Cargill Meat Solutions, on the 30th of January of 2006. This citation and
notification or penalty required corrective actions such as the establishment of a preventive
maintenance program and training of maintenance personnel on potential failure recognition among a
range of initiatives to be implemented. This is just one of a number of recent safety events where
maintenance has been flagged as a contributing factor.
v
Anecdotal information provided to the author from senior management within a range of companies
in the water industry of the United Kingdom places asset failures as being responsible for
approximately 40 – 60 % of breaches of consent. In this context “consent,” relates to guidelines
designed to protect the environment to an agreed level. In infrastructure this is thought to be even
higher. This particular industry represents one of the world’s first water networks and much of the
infrastructure is ancient.
vi
Mathematical Aspects of Reliability-centered Maintenance, H. L. Resnikov, National Technical
Information Service, US Department of commerce, Springfield
vii
Mathematical Aspects of Reliability-centered Maintenance
viii
GEM stands for generic error modeling and was first developed by Professor Rasmussen of MIT
following his review of the incidents leading up to the three-mile island disaster in the USA. The field
of human error is a fundamental area of modern reliability management and has been advanced greatly
by the works of James Reason, of Manchester Business University in the United Kingdom.
ix
Reasonably likely is a term used within the RCM Standard, SAE JA1011, to determine whether
failure modes should, or should not, be included within an analysis. “Reasonableness” is defined by the
asset owners
x
The strategy options, or policy options, offered within RCM are detailed in the RCM standard SAE
JA1011.
xi
This could theoretically, be suitable for all companies that need to manage physical assets. However
it has particularly importance for financially regulated institutions and companies that need to prove the
case for funding.
xii
The Maintenance Scorecard, ISBN 0831131810, is published by Industrial Press