Real-world users of asset management software have their priorities. They’ll implement enough of the software to meet their primary objectives and sometimes postpone or sidestep opportunities for broader benefits. Why? It may be a shortage of funding or project personnel, or they might just need time to enjoy the wealth of asset information now at their fingertips.
Process consultants and industry analysts, on the other hand, seek to extend the value of asset management software. They take a holistic view of its influence on equipment availability, plant performance and operating costs, and they strive to exploit its full potential. Although their paths vary, they and the end users ultimately share a common goal: to secure better asset health information and make timely, educated maintenance and management decisions.
Economics drive the asset management approach
The need for effective asset management is greater now than in the past. “It is not business as usual; the paradigm has changed. There is a lot of stress on equipment and budgets, and the workforce is aging.” says S. Rao Palakodeti, executive consulting engineer for asset optimization at Sigma Energy Solutions (www.sigenergy.com), a wholly owned subsidiary of Alstom (www.alstom.com). Companies need more information on how their equipment and systems behave so they can prioritize spending for greater ROI, explains Palakodeti. Without it, maintenance decisions are subjective.
Greenstar Recycling (www.greenstarrecycling.com) chose to implement asset management software for this reason. Recycling is a young industry that is heavily dependent on continuous processing equipment and industrial-strength trucks. The company entered the U.S. market in 2007 through acquisitions and is now recycling 2 million tons of material per year.
Asset management software helped Greenstar Recycling to cut its maintenance and repair costs by 40% over the first three years of its use in the Northampton, Pennsylvania, plant. (Source: Greenstar Recycling)
In 2008, Greenstar’s Northampton, Pennsylvania, material recovery facility (MRF) became the pilot site for its asset management software implementation. The software was subsequently rolled out to 14 MRFs throughout the United States and the Houston headquarters. It is used by operations and maintenance personnel and management alike, says Chris Morgan, plant manager at Greenstar Recycling.
Preventive maintenance schedules are set up for each asset in the software, and work orders are generated automatically. Maintenance and repair histories are used to track costs and troubleshoot problems. “We could see which conveyors and belting were not getting enough life, so we switched from two-ply to three-ply belts for a few hundred dollars,” says Morgan. “The extra cost was worth it; it added to our uptime and required less labor.”
The software helped Greenstar to cut its maintenance and repair costs by 40% over the first three years of its use in Northampton. “This was achieved with the management of preventive maintenance and the development of predictive maintenance through the analysis of the software’s various reporting functions,” says Morgan. “Proper maintenance and repair increased uptime, providing a safer, more efficient workplace.”
Ralph Rio, research director at ARC Advisory Group (www.arcweb.com), encourages reducing maintenance costs with a predictive approach. “Compared to preventive maintenance, which is largely time-based, predictive maintenance assures maintenance occurs when needed and significantly reduces the amount of maintenance performed,” he says.
For instance, while mechanical equipment wears out, most electronic and electromechanical equipment have purely random failure rates. “Research shows that a little more than 90% of these failures are random in nature,” says Rio. “This means scheduling preventive maintenance based on time is not as effective as it once was when most equipment was mechanical in nature.”
Historical view supports predictive decisions
New asset management tools and processes are available to increase availability with limited funds, producing smarter, objective, statistical analyses from which to project future asset health and make better spending decisions.
The most popular predictive maintenance mechanism is currently the historian because it is considerably less expensive than condition monitoring systems, says ARC’s Rio. Companies are monitoring asset and process readings, using the data to decide when maintenance is required, and alerting the planner to create a work order. “Most companies already have a historian with existing information. Any investment in programming and configuration time is small compared to acquiring an independent condition monitoring system.”
Past data from a historian allows you to project into the future. “It is a dynamic look using statistical analysis rather than a static look at failing equipment,” says Sigma’s Palakodeti. “Online performance monitoring allows proactive problem solving when it is directly integrated with a historian. Applying data models to the historian allows you to analyze past history and, through simulations, predict how equipment will perform in the future.”