Study proves Asset Performance Management increases plant efficiencies

  • April 11, 2011
  • Feature
April 2011
 
By Bill Lydon, Editor
 
Joel Nachlas, Ph.D., and Andrew Henry from Virginia Tech did a survey to learn about asset management behaviors and results. Meridium, a leading provider of asset performance management (APM) solutions, funded the research project. (Editor’s Note: Meridium has worked with both the colleges of Engineering and Computer Science at Virginia Tech for years, including some validation studies by Dr. Nachlas on algorithms and statistical models in Meridium software.) The study defines Asset Performance Management (APM) as the use of quantitative analysis and informed management in the acquisition, use, maintenance and retirement of production equipment. Companies deploy an APM program to maximize physical asset performance, mitigate risk, and optimize return on asset cost across the business enterprise.
 
The asset management study was done through Virginia Tech’s Grado Department of Industrial and Systems Engineering, which was founded in 1920 and is currently ranked 4th by US News and World Report among Industrial Engineering Departments in the country. Faculty and students study a wide range of topics including Operations Research, Manufacturing, Management Systems, and Human Factors engineering. Dr. Nachlas is an associate professor in Virginia Tech’s Grado Department of Industrial and Systems Engineering serving as Operations Research Program Director and is author of two textbooks, Reliability: Probabilistic Models and Maintenance Methods and Probabilistic Models in Operations Research. His research interests include reliability theory, maintenance scheduling, statistical quality control, as well as modeling of system availability. Andrew Henry is a Ph.D. student in Industrial and Systems Engineering (ISE) at Virginia Tech. Andrew's research interests include reliability, decision making under uncertainty, and stochastic processes.
 
All research participants reported their results directly to the research team at the VT College of Engineering. The survey was aimed at companies or facilities that rely on capital intensive production resources. Industries represented in the survey include aerospace & defense, chemical/petrochemical, consumer products, forest products, metals & mining, oil & gas, pharmaceutical, power/utility, and transportation. Survey results indicate companies that elevate the importance of APM through involvement of upper level management or organization-wide involvement tend to out-perform others based on a variety of metrics. Companies that maintained the ability to understand risk at each asset (example: machine) level outperformed those with a more macro overall plant-level view of risk.
 
Other questions were used to gauge the success of the maintenance program by measuring the amount of maintenance required to react to production failures and breakdowns (reactive), versus the use of scheduled maintenance, condition monitoring and predictive modeling based maintenance (proactive).
 
Problem Solving Techniques
 
The survey explored problem solving techniques used by companies. 42% of respondents indicating the “5 Whys” method was used primarily for investigating equipment failure. The “5 Whys” method is based on asking the Why question about a problem and then asking the Why question about each subsequent answer to find a root cause for a failure.
 
An example, my car will not start. (Problem)
 
   1. Why? - The battery is dead.
   2. Why? - The alternator is not functioning.
   3. Why? - The alternator belt has broken.
   4. Why? - The alternator belt was well beyond its useful service life and has never been replaced.
   5. Why? - I have not been maintaining my car according to the recommended service schedule. (Root cause)
 
Maintenance Triggers
 
The survey also explored what triggers maintenance in companies. Surprisingly, both predictive analytics and condition monitoring were significantly lower in use than simply reacting to a breakdown. The study illustrated that higher value assets are more likely to have predictive analytics applied to them for improved maintenance.
 
Asset Replacement
 
High performance respondents utilize data more often to make data driven decisions. Companies with low reactive maintenance levels are more likely to use breakdown reports to make asset purchasing decisions, evaluate, and design current/future maintenance plans. The low performance class was much more likely to maintain asset information in a paper based record file. While data availability corresponded to lower reactive maintenance levels, what companies did with the data on hand was shown to significantly affect class ranking.
 
Total cost of ownership (TCO) has been a mantra for a number of years, and this survey shows that high performance companies take this seriously. High performance companies armed with solid information take into account asset reliability in new and replacement purchase decisions. Low performance companies primarily use asset payback which does not account for plant operating efficiency.
 
Maintenance Commitment
 
Study results indicate that a higher level management commitment for physical asset performance correlated with organizations that leverage analytical and other refined maintenance methods, leading to better business performance. The next highest performance group is committed to using these tools at business unit levels. Interestingly, the high performance class companies are much more likely to include maintenance staff in production scheduling.
 
The complete study is available for download at:
 
Thoughts & Observations
 
In many cases, the challenge is to convince management of the value of investing in the systems to add these capabilities to a plant. This study is a useful tool for automation professionals to help sell management on the idea that better analytics and real-time condition monitoring can improve plant efficiency.
 
Dr. Joel Nachlas made an observation about industries adoption of new ideas that is worth pondering:
 
“The ‘quality revolution’ of the 1970’s and 1980’s was essentially a re-awakening to the existing technical methods that had been developed and applied in the US. The implementation of these established methods produced a sort of renaissance in American industry. By 1995, the general consensus was that the productivity and profitability gains attributable to the implementation of quality technology were between 25% and 40%.”
 
“We are in the same position today relative to asset management that we were to quality in 1970. We have proven scientific results that have been translated into convenient and useful techniques for planning and managing maintenance, reliability and replacement. Unplanned downtime levels suggest that the available productivity and profitability gains that can be realized through the use of asset management technology are 25% to 50%.
 
For companies that believe proper predictive maintenance lowers downtime and lengthens equipment life, the tools are now available to achieve better results and improve productivity.
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