Among the areas where modern enterprise asset management (EAM) systems provide substantial benefits is the driving out of inefficiencies in business processes. Through the capture, storage, manipulation, and display of historical transactional data, companies can take great leaps forward in the efficiency with which they execute maintenance programs. They can do this, for example, through ensuring that delays in executing work are captured, analyzed, and resolved, or by being able to display trends in performance and cost over time.
Part Two of the series Captured by Data.
The effectiveness of a maintenance task comes from how it manages failure modes, not from the level of efficiency that it is executed with. The original reliability-centered maintenance (RCM) studies revealed that many routine tasks could actually contribute to failure, or to lower cost-effectiveness, by having limited or no impact on the performance of the asset (in effect wasting the maintenance budget). Executing these tasks with greater efficiency would have either have no impact at all on effectiveness, or would possibly even magnify the effects of unsuitable tasks.
For example, after an RCM analyst had spent a lot of time working with a utility company in the UK, it became clear that the reported schedule compliance was not an accurate figure. Schedules were regularly coming in with 100 percent compliance, while the reality was that they were actually performing at around 25 percent.
After some investigation it turned out that the crafts people recognized that most of the regimes that were coming out of the system were either counterproductive, or not applicable at all. So they were fortunately omitted. Prior to installing the EAM system, they were working with job cards in separate systems; once the EAM went "live," these were collated and assigned to all similar assets regardless of operational context.
This is where RCM-style methodologies contribute to the modern EAM or computerized maintenance management system (CMMS) system. By providing the content that the system needs to manage, they are ensuring that the right job is being executed in the right way. This is common sense, and practitioners of RCM have been emphasizing this point for many years.
What is often not emphasized, however, is that having an effective maintenance program in place which is integrated with the EAM system ensures that future efforts of data capture are executed in a manner that supports the principles of responsible asset stewardship. The effect of building a data capture program on the back of an effective maintenance program is to reverse, over time, the ratio of hard data to human knowledge that is available for decision making.
Figure 1. Integration of EAM and reliability-centered maintenance
On performing analysis, the structured approach within the decision diagram drives RCM analysts to develop an asset management program that is practical, cost effective, and tailored to a given level of performance and risk. There are two main outputs for any correctly performed analysis. The first is one-off changes to procedures, software, asset configurations, asset types, company policies, and asset designs.
The second area is a group of routine maintenance tasks designed to manage the failure mode under analysis. Aside from combinations of policies, RCM supports five different maintenance policy options, as detailed below. These make up the bulk of the content that the EAM system is installed to manage (the strategy or policy options offered within RCM are detailed in the RCM standard SAE JA1011).
- Predictive maintenance (PTive): a task to predict when a failure mode is about to occur.
- Preventive restoration (PRes): a task to prevent failure through applying a task, at a time or usage based interval, to restore the assets' original resistance to failure.
- Preventive replacement (PRep): a task to prevent failure through replacing an asset or component, at a time or usage based interval.
- Detective maintenance (DTive): a task to detect whether an item has failed or not. This task is only applied to failure modes that RCM classifies as hidden.
- Run-to-fail (RTF): a policy to allow an asset to fail, rather than applying any form of routine maintenance. Failure modes that are allowed to run to failure have low, or negligible, consequences in terms of cost only. These are the non-critical, or acceptable, failures that were referred to earlier in this document.
An RCM-based process selects these tasks based on their applicability and effectiveness, as defined within the decision algorithms. These issues have been commented on many times and will not be dealt with in great detail within this paper.
For modern RCM analysts, the routine maintenance tasks are of interest not only because of the impact they have on asset performance, but also because of the way they can be used to develop the asset information portfolio, contribute to whole-of-life costing, and provide an additional tool for proactive monitoring of asset performance and corporate risk exposure.
As with the logic of the decision diagram, the criteria and characteristics of each of these policy choices have been detailed many times, and it is not necessary to describe them in detail here. However, it is necessary to detail how they affect the collection, management, and use of dynamic asset data.
As detailed in figure 2 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 (PTive) task once warning signs have been detected.
Immediately following the analysis, the information established can be used for creating proactive whole-of-life costing models that are directly tied to performance and risk.
Figure 2. Tasks involved in predictive maintenance
The whole-of-life cost of an asset, or component, subject to predictive maintenance tasks, can be represented by the following equation:
whole-of-life cost = (cost (PTive) x n) + cost (PTive),
where n represents the number of times the PTive task is likely to be executed. This also drives estimates of the time between installation and likely failure. It needs to be recognized that the corrective or PTive task is executed at a time less than end-of life (although small).
As time passes, the amount of data that is collected on these tasks will grow, collected now in a responsible manner, and can also be used in statistical models regarding asset degradation and predictions of capital spend requirements. By the inclusion of these outputs of an RCM analysis, asset managers can use the results with increasing confidence as predictors of whole-of-life cost profiles, and end-of-life points.
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 asset's resistance to failure (PRes), or at 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.
Figure 3. Tasks involved in preventive maintenance
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.
The whole-of-life cost of an asset, or component, subject to preventive maintenance tasks, can be represented by the following equation:
whole-of-life-cost = cost (PRes) or cost (PRep)
This is an additional task, 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. This could theoretically, be suitable for all companies that need to manage physical assets. However, it has particular importance for financially regulated institutions and companies that need to prove the case for funding.
As with predictive maintenance tasks, there are actually two tasks that are being implemented in detective maintenance: first, the detective (DTed) 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 optimization of the frequency of this task.
Figure 4. Tasks involved in detective maintenance
The whole-of-life cost of an asset, or component, subject to detective maintenance tasks, can be represented by the following equation:
whole-of-life cost = (cost (DTed) x n) + cost (DTed),
where n represents the number of times the DTed task is likely to be executed. This also drives estimates of the time between installation and likely failure. It needs to be recognized that the corrective or DTed, task is executed at a time greater than end-of life due to the characteristics of this task. As time passes, the data collected can be used to inform decisions and whole-of-life models with increasing certainty.
This is particularly relevant for hidden failures, or hidden functions as they are sometimes called. When implementing the outcomes of an RCM analysis, some of the tasks are DTed tasks. That is, they are tasks put in place to detect if a failure has occurred. Often, the items being tested have not been tested for a long period of time—sometimes years. And often nobody knows if they are working or not!
So when establishing the initial DTed task frequencies, often the information used is not very certain, and is backed by only the experiences and memories of those involved in the exercise. Fortunately, manufacturers do often have a good level of information regarding failure rates in these sorts of devices. But the result is till quite conservative, and not tuned for the specific operational climate. Performing DTed tasks will immediately help the company to establish some baseline information regarding failure rates of the device.
The last policy option, aside from redesign and combinations of tasks, is that of run-to-failure. This option is for the failures detailed in figure 1 (from Part One of this series), for which the costs are acceptable or low (or negligible).The EAM will allow these failures to be captured for analysis to inform whole-of-life cost models and spending forecasts, and for use in reviewing maintenance policies when relevant.
Figure 5. 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 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, this information is not mentioned in previous work on RCM.
In particular, it fuels the shift by the company away from static methods of lifecycle 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.
Measurement of Maintenance Performance
Measurement is worth mentioning within this paper because the data that will be generated from applying an effective maintenance program will allow for companies to look at how their program is functioning further than they previously could. It is another example of the fundamental importance of effective maintenance policies.
When applying measurement programs to asset management or maintenance, companies generally look directly to direct performance measures. These are things such as failure rates, mean time to repair, availability, quality and a whole list of other measures of how a machine is operating at any given time (often trended to give a view of improvement or deterioration).
These are perfectly reasonable measures, and they give a company a snapshot of how a machine is performing with respect to the standards that have been set for it. Regardless of how these measures are selected or generated, they are almost always lagging indicators. That is, they are indicators that tell you how your machine is performing after the fact.
Figure 6. Areas covered within the RCM scorecard
Yet the data collected through establishing an effective maintenance program allows the company to generate a range of leading indicators (these are measures that lead performance, or that tell you that something is likely to begin to perform badly before it actually does).
The diagram in figure 6 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 (Mather, Daryl. 2005. The Maintenance Scorecard: Creating Strategic Advantage. New York: Industrial Press), and addressed in a separate article.
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 respond to several questions:
- Is it more cost-effective to manage the asset over its whole-of-life profile, or not? (This leads to incorrect whole-of-life management, not just costs.)
- Was the task really more cost effective than the estimates of failure? (This leads to incorrect whole-of-life costs.)
- Was the cost of failure really more cost effective than the estimated costs of the maintenance policy? (This leads 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 DTed tasks? (This leads to higher-than-acceptable levels of risk exposure.)
- What is the increase in risk due to late performance of PRes or PRep tasks? (This leads to higherthan-acceptable levels of risk exposure.)
- What is the increase in risk due to late performance of PTive tasks? (This leads to higher-than-acceptable levels of risk exposure.)
The actual measures contained within the RCM scorecard are detailed fully within the book mentioned above. 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.
The Foremost Consideration of Maintenance Managers
We began this paper discussing the three key drivers of maintenance that EAM systems often target. Without considering the different operating environments of different companies, these do cover the basic drivers of most maintenance departments:
- to develop a maintenance policy designed to minimize the total cost of managing and operating the assets throughout its entire life cycle for a given level of performance and risk.
- to obtain maximum efficiency out of the resources used to carry out the maintenance policy, driving unit costs further towards the optimum level.
- to steadily build the asset data portfolio to allow future decisions regarding the asset base with increasing levels of confidence.
It could be argued that methods based in RCM-style thinking alone could satisfy all three of these primary drivers of maintenance management. But the business processes that would be required to do so would be onerous, and would restrict the ability of the company to manipulate and analyze data effectively (as well as being a burden to those trying to manage the maintenance workload).
It could also be argued that implementing EAM or CMMS, without implementing a parallel or leading initiative to create an effective maintenance policies will produce limited results, possibly exacerbating the current situation by allowing the company to perform incorrect work efficiently. And it might also potentially create an environment where the assets are being managed in a way that is contrary to principles of responsible asset stewardship.
This line of thinking can lead to only one conclusion. The development of effective maintenance policies is the foremost consideration for modern asset managers. When done correctly, it provides the base for business processes, inventory management techniques and methods, software configurations and selection, and the numbers and skill requirements of labor.
Aside from these tactical advantages, it also offers the strategic advantages of improving the whole-of-life management and understanding of the physical asset base, and the way that it is monitored and managed through performance measures. However, once the program is created, attempting to manage the asset base without leveraging the advances in modern maintenance software deprives the organization of a tremendous opportunity for improvement.
This viewpoint is not new, nor is it particularly complex. It is a commonsense approach, and is an extension of the basic way that maintenance managers acted prior to discovering technology and being drawn down the path of increasing functionality, graphing, mobile devices, and other gadgetry. It just seems to have been lost in the maze of tools that we are faced with today!
Of particular importance in this paper is the growing role of the RCM analyst. Once a separated facilitator or a sole analyst, the RCM analyst is a role that is by necessity becoming a lot broader, covering a range of additional areas of expertise. A twentieth century facilitator was generally driven to apply a team-based method and to complete the analyses. Twenty-first century analysts are generally owners of the program for their area or region. They are responsible for its upkeep, implementation, for ensuring that it is effective, for establishing the links to whole-of-life costing, and for capturing the knowledge of the organization through the application of the method in a flexible fashion. A new role for a new set of challenges!
This concludes the series Captured by Data.
About the Author
Daryl Mather has assisted companies in increasing the profitability of their physical asset base in over twenty-three countries and regions, including the US, Europe, Asia, and Latin America. He is the author of The Maintenance Scorecard: Creating Strategic Advantage. He currently works with Knowledge Based Management, based in London (UK), and can be reached at firstname.lastname@example.org.