Asset-intensive organizations are challenged to maximize production, minimize costs, follow regulations and manage risk. To balance the demanding requirements, operators require a more-cohesive system that assesses equipment performance and manages the resulting data. To date, the question of, “How are we doing?” was largely a subjective one, as there was no definitive standard of measurement against which to compare performance. However, in today’s online-centric world, connected plants can benchmark the performance of their sites against those of industry peers around the globe to get a true picture of how well they’re operating. Plant data can now be pulled into an advanced database anonymously and shared among an industry user group comprising various organizations.
As a result, all companies involved understand how their assets are performing as compared with those of their industry peers and can develop strategies to improve their performance. They can assess how their costs, productivity, and downtime measure up, and use this to drive informed decisions for managing production assets. The best part: All of this can be achieved by leveraging the data the organizations already have.
Managing data –Through the use of innovative sensors and condition-monitoring technologies, machines are providing a wide variety of data about health, performance, operational capacity, failures, etc. As data volume continues to grow, data must be managed to provide organizations with strategic insights around risk and potential operational improvements. Asset performance management (APM) software effectively manages data to improve reliability strategies, reduce operational risk, and drive actionable recommendations as well as provide incredible ease of accessibility, use, and sharing. Capturing large amounts of critical data generated by plant sensors and other systems is valuable only when you have the capability to integrate it, analyze it and turn it into actionable, useful information that can improve safety, reliability and operations.
Analyzing data – Once companies have a handle on managing the big data coming from assets, sensors and plant systems, a key component of an APM initiative is the use of analytics to identify areas of risk, predict asset failures, make and manage recommendations, and automate actions and strategies that will improve assets' performance. For organizations to truly reap the benefits of the Industrial Internet of Things (IIoT), they must be able to draw insights from performance data within their plants and across their facilities, and against data from other organizations in their industry. Three specific analyses can provide these insights:
- Corrective cost analysis – This will demonstrate the true costs of planned vs. unplanned work. Unplanned work is extremely disruptive and costly. The ability to schedule work on your terms is the goal every plant manager or operator should be striving to attain.
- Repair event analysis – This analysis highlights failure rates and repairs by equipment or group. This type of information can prove invaluable in predicting the need for and scheduling replacement and repairs of key assets.
- Asset availability analysis – This will demonstrate maintenance effectiveness, with a focus on critical assets. Without a comprehensive analysis, plant managers and operators are leaving to chance whether the maintenance being performed is effective in helping avoid equipment failures and operational inefficiency.
Many industrial projects require choices between equipment vendors offering similar products. In one real-world example, a company found itself needing to explore the financial implications of choosing a centrifugal pump from one vendor vs. a similar pump from another. Simulating a system reliability and maintainability model in an APM system over 10 years produced an estimate of the unreliability cost per year—including the repair expense as well as the value of lost production—for each of the pumps being considered. One pump is $505,292 less expensive to operate over a 10-year service life.
Anonymizing, benchmarking, and securing data – Keeping data secure and companies' identity concealed is critical for wide participation. Subscribers can easily drill down from company to region to site to area to unit, all the way down to the individual asset. This will help users identify specific areas where opportunities for improvement exist. The ability to use data to optimize maintenance and reliability processes, as well as influence vendors, can do nothing but improve an organization’s overall performance over time.
The importance of conducting these types of exercises cannot be understated, as the potential for significant operational improvements and increases in efficiency is at stake. To review another refining example, based on a quick data-mining exercise with a comparative industry database, a major refinery ascertained that its average corrective cost for centrifugal pumps was almost 25% higher than the cost at other company sites. This refinery is known to have a very efficient rotating-equipment machine shop, so these results were unexpected. The refinery was determined to uncover the cause of this unusually high average corrective cost and lower it. Based on the data, the annual opportunity cost was estimated at $400,000. That savings could be realized if the average corrective cost of repairing centrifugal pumps were reduced by 20%. In this case, all that was needed was the discipline to perform planned work on the pumps.
With greater availability of big data and connected assets, there is an end-to-end picture of plant operations waiting to be developed by organizations. From the plant floor to the corporate office, this information will provide valuable insights that will make plants more efficient, provide more accurate forecasts for maintenance and replacements, and of most importance, create a safer environment for everyone. This is the type of knowledge that is necessary for organizations to stay competitive on a global scale in today’s challenging marketplace.