Predictive maintenance delivers more impact on the bottom line when it’s integrated into a broader plant asset management strategy.
“Prediction is very difficult, especially about the future,” quipped Niels Bohr, Danish atomic physicist and Nobel Prize winner, early last century.
But when hard times put the future of your manufacturing business in jeopardy, and plant failure and downtime mean your bottom line could suddenly be rubbed out, you need to be as predictive as possible to prevent problems from happening.
New technologies certainly help. Predictive maintenance and condition-monitoring techniques that constantly analyse critical factors in machine performance, such as oil, vibration, acoustics, and heat, now allow companies to pre-empt major failures and production line problems and help management schedule essential servicing and repairs at optimal times with minimal plant downtime, disruption, and cost.
In Tavaux in eastern France, for example, the largest chemical plant in Belgium-based chemical and healthcare group Solvay has used predictive techniques in Emerson Electric’s Asset Management Suite (AMS) to squeeze productivity gains of 10% to 15% from its maintenance department. With more than 15,000 devices to manage across the plant, generating 60,000 inputs and outputs, the AMS system has also supported a 20% increase in complex instruments at the Solvay site, but without any increase in staffing levels.
Other payoffs have been significant, according to Giacomo D'Andrea, service manager for automation, instrumentation, and electricity at Solvay. “It’s of great value when commissioning [equipment], and it is also now our daily tool for identifying, standardizing, configuring instruments, and saving reference values," he says.
But experience is now teaching companies that simply using predictive systems to keep the plant’s machines up and running is only half the story. The real bottom-line benefits emerge when the predictive approach and the data it gathers are part of a much broader suite of asset management processes.
“The biggest danger we avoided was to implement predictive plant maintenance in isolation. We want the whole production more predictable and more plannable,” says André Ertel, project manager at industrial and retail plastics packaging manufacturer Linpac Allibert in Birmingham, U.K., part of the global €1.2 billion packaging company Linpac Group. “We believe that if you put in plant maintenance, you have to put it as part of an integrated system with other modules. It has definitely had an impact on our bottom line because, ultimately, it’s not just about machine reliability alone; it’s about the reliability to the customer.”
The Right Information
Using an overall equipment effectiveness (OEE) package from ERP supplier IFS that includes predictive maintenance, Linpac now knows more about the performance of its 61 injection moulding machines at its main U.K. plant and can schedule machine servicing or parts replacement more accurately. It not only saves costs on direct maintenance and support, but also helps plant management plan important production runs with more confidence.
“Part of it was about having the right information at the right time, and connecting it with the rest of our customer planning systems,” Linpac’s Ertel says. “That means we now know we can get a couple more orders in the line. We can offer shorter lead times to our customers, and that gives us a better response from the customer base. It’s definitely helped us get more cost-efficient in the manufacturing plant.”
