Improving Decision-Making for Equipment Assets

January 7, 2012 at 10:37 am Leave a comment

Efficiency, uptime, profits can be increased with data driven predictive maintenance

In just the last few months, several research reports were released identifying how organizations are managing their equipment assets – “Operational Risk Management Strategies for Asset Intensive Industries” by the Aberdeen Group, the “Asset Performance Management Study” by Texas A&M University, and the “Best Practices in Asset Management and Reliability Study” conducted by Virginia Tech. It is fascinating stuff, ripe with information every organization can use to begin identifying new competitive opportunities and minimize risk to their organizations, as well as offering valuable benchmark data to help companies measure their progress. One of the findings which particularly sparked my interest was how participants were using their asset-related data and how its usage impacted the performance of their organizations—specifically within the area of reactive maintenance.


Survey results from the “Best Practices in Asset Management and Reliability Study” demonstrated data availability and data use had a strong correlation with reactive maintenance levels, and reactive maintenance levels in turn significantly impacted the number one and two ranked organizational concerns of survey participants—cost/margins and safety. While no one on the outside knows your business like you, one thing that many years of experience working with hundreds of organizations in support of the management of their equipment assets provides is a good feel for the challenges organizations face. And I do emphasize challenge—although survey results validated that high-performing companies experienced significantly lower levels of reactive maintenance, there is still far too much of it occurring. One thing is certain, it is not happening because any organization thinks reactive maintenance is a good thing and for the most part, not because they are not actively attempting to reduce it.

Escaping the reliability maelstrom

Many of us have either experienced first-hand or witnessed what can be referred to as the “reliability maelstrom,” ending in statistics like those published in the Wall Street Journal in January 2010 citing 60% of corporate improvement initiatives fail. It is ugly to watch. Upper management has finally been convinced of the potential benefits associated with implementing a reliability initiative (e.g., reliability-centered maintenance) and appropriate funds for its execution. Success seems eminent. A careful and deliberate Reliability Centered Maintenance (RCM) process to identify failure modes is followed, applicable and effective maintenance tasks are developed, and the performance of team members is nothing short of outstanding. But once completed, it is discovered it is difficult, if not impossible, to extract the anticipated value from the initiative because:

  • Existing RCM tools are disconnected from the Enterprise Asset Management (EAM) system.
  • Loading RCM recommendations into the EAM is difficult.
  • RCM task justification is lost.
  • Measuring results over time is difficult.
  • Keeping the RCM process evergreen is nearly impossible.

And the maelstrom begins—management becomes skeptical about making future investments in reliability and maintenance projects, tensions between departments increase and cooperation decreases, and even the individuals in charge of maintenance and reliability efforts become hesitant to attach themselves to another potentially unsuccessful initiative. What this example demonstrates is asset data—even that which is by all measures good data—represents “potential” value. It is only when you are able to put data into motion—aggregate, analyze and act on it—that value can be demonstrated in terms the “Operational Risk Management Strategies for Asset Intensive Industries” reportcites as the top two concerns of upper management—asset life-cycle improvements and maximum Return on Assets.

High-performing companies evolve

The reliability and maintenance practices of high-performing companies have evolved along with the people, processes, and technology that support them. Predictive maintenance was a pivotal first step in helping organizations to increase asset reliability and lower costs. Diagnostic information generated in the field by intelligent instruments and condition monitors are capable of obtaining critical information on plant assets to:

  • Raise alarms when signs of impending failure appear, so immediate corrective action can be taken to avoid unplanned downtime, reduce maintenance costs, and increase equipment reliability.
  • Enable plant personnel to determine with some precision if repairs can be delayed until the most favorable time, such as a scheduled maintenance shutdown.

While this data has reduced machine breakdowns and associated costs, asset availability and uptime often remain below targeted levels. Just as in the RCM example above, valuable data, isolated from other asset-related data, diminishes its ability to contribute to the development of asset strategies, which support what “Asset Performance Management Study” respondents identify as their top two performance metrics—availability and uptime.

While it is sometimes unclear at any given time who is doing the pushing and who is doing the pulling within organizations to adopt asset performance management as a strategic business driver, findings from the “Best Practices in Asset Management and Reliability Study” clearly correlate a lack of information availability and use with lower performance and increased reactive maintenance levels:

“High-performance class respondents were much more likely to monitor the status of production assets using an automated enterprise asset performance management software system capable of integrating the organization’s key methodologies, technologies, and best practices to develop, analyze, and evaluate their asset management plans in terms of meeting key performance indicators. Medium- to low-performance classes were much more likely to maintain asset information in a paper-based record file limiting data availability. High performers utilized data more often and in more places in their business and indicated that they used performance data when making purchasing decisions as well as for safety and risk evaluations.”

It appears more and more organizations are recognizing the link between high performance and asset performance management with 44% of participants in The Aberdeen Group October 2010 survey, “The Role of Software in Asset Performance Management (APM),” indicating they are planning on investing in an asset performance management software solution.

What would a unified approach to asset data management look like?

Let us continue with our most recent example of predictive maintenance data and see what happens when we combine it with other asset reliability information.


An APM homepage can be customized for each user, providing a quick view of the current state of asset performance, availability, and maintenance in a specific plant. Also on display are historical charts showing monthly results for overall equipment effectiveness, availability, and maintenance costs. Users can obtain greater detail on any of these factors simply by clicking on one of the dials.

To learn about critical asset failures and how much they are costing the plant, the user moves the cursor to the “Linked Reports” box in the lower left-hand portion of the homepage and clicks on “Critical Asset Failures and Cost Summary.” The Critical Asset Failure & Cost Summary screen shows where maintenance time and money are being spent, pinpointing 10 pieces of equipment by tag number. Failure information pulled from predictive diagnosis applications and cost information from CMMS records is used to illuminate these “bad actors.”


The bars on the Critical Asset Failure & Cost Summary chart indicate the number of time-consuming failures suffered by each asset over the past year, and the green line shows the total maintenance cost of each piece. By clicking on tag number GC0036-083 (a gas compressor), the user finds out why that particular unit is costing more than $200,000 per year to maintain. Five failure modes for the compressor are highlighted on the subsequent screen along with exactly which root cause is most costly. These reports can be updated as frequently as required by the end user, so decisions can be made in real time.

By clicking on “Risks to Reliability” in the “Linked Reports” box, the user can access a report showing the highest priority assets with degraded health. All plant equipment is prioritized on the basis of failure modes and effects, understanding its operational significance, and the risk of failure. Each piece of equipment is then given a criticality rating. Appearance on this list is a strong indication that the asset is critically important to overall production and performance goals, which would be adversely affected by its failure.

System customization allows specific objectives to be established and tracked. For example, once key performance indicators are identified, a manager can very quickly determine whether the targets are being hit or missed. As indicated earlier, higher level performance, including overall equipment effectiveness (OEE) and availability, is reflected on the home page with details available by drilling down. For example, if OEE is declining, as seen in the “Key Performance Indicator OEE” screen, the specific assets contributing to the decline can be identified with a single click so the manager knows instantly where to focus attention to reverse the trend. The same is true of critical asset availability.


Surveys are a great source of information, and we all owe a debt of gratitude to the organizations willing to participate in them. While it is natural to want to draw comparisons and conclusions from survey data, a more productive outlook might be to view them as a source of information for new beginnings. While it is clear there is much more work to be done, perhaps as never before, we are seeing the emergence of a more unified approach to the management of our organizations—people, processes, and technology—and the significant advantages associated with this new approach.

ABOUT THE AUTHOR

John Renick is the Partner Solutions Manager for Meridium, Inc. He has more than 12 years of experience working with asset intensive industries. In his current role, John oversees Meridium partnerships.

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