April 2011
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).
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)
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.
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.
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:
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:
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.
