Blog
Sometimes it can feel like we’re drowning in all the data that’s available from the equipment and systems running on our plant floors.
Many maintenance, reliability, and operations leaders in asset intensive industries have equipment health data available in the form of maintenance records stored in ERPs or CMMS applications, process data flowing into historians, and condition data embedded in the brains of operators. And this information can be powerful. With insights to give us a complete picture of our equipment health across every plant site, we can start predicting when equipment failures are likely to occur and develop asset management strategies to consistently hit production goals while mitigating risk to our shareholders, customers, or our communities. It’s not always about preventing failures; it’s about knowing when those failures may occur and the impact they could have on the organization’s bottom line.
Even in this world of the Industrial Internet of Things (IIoT), virtual reality (VR), artificial intelligence (AI), and machine learning (ML), many industrial organizations still don’t measure the financial impact that effective asset management can drive. Many companies do, however, know that unplanned downtime can cost them millions of dollars in lost production. So, what’s the typical response? Increase the amount of preventative maintenance on the equipment that is thought to be most critical. Depending on the nature of the failure, this approach may or may not reduce unplanned downtime in the short term, but it will likely increase planned downtime, raise spare part inventory costs, and increase maintenance and inspection labor costs.
Business conditions, operating and environmental conditions change, equipment ages, and our products often evolve; an asset that’s critical to operations today, may not be as critical tomorrow. And if an organization doesn’t truly have a pulse on what equipment is critical at that time they mandate more preventative maintenance, then they will still incur all the costs associated with an overly rigorous maintenance strategy and continue to be exposed to equipment failures capable of halting production, causing injury, or leaking chemicals into the atmosphere – which is never good for public relations.
Getting back to IIoT, VR, AI, and ML: while I feel these and the 20 other acronyms I chose not to include are grossly overused, there is practical application for organizations looking to grow margins without introducing risks to the business. For those of us in the world of maintenance and operations, asset performance management (APM) solutions like GE Digital’s Predix APM are helping industrial organizations of all digital and maintenance maturity levels to start aligning their growth initiatives with intelligent asset strategies to gain competitive advantage.
Watch our on-demand webinar Maximizing Value from Your Asset Management Strategy, a one-hour webinar hosted by IndustryWeek. Dan and I will walk through a few customer stories and give our take on how to start and grow a culture that values asset performance. We’ll also highlight how innovative digital twin and prescriptive analytics technologies are being utilized today to improve maintenance data integrity, detect potential failures earlier, and accurately recommend asset management actions.
It will be a valuable opportunity to really dig in.
Listen to Intelligent Assets Practice Lead Paul Casto of GrayMatter and Senior Product Marketing Manager Dan Parker of GE Digital discuss how to craft a successful asset maintenance strategy that balances equipment criticality, predictive and condition-based maintenance and predictive analytics. They highlight common missteps that can hurt long-term success and detail how Eastman Chemical embraced a comprehensive solution that dropped unscheduled downtime by 60 percent, reduced product loss by 40 percent and cut long-term maintenance costs by 10 percent, saving millions of dollars.