Unplanned downtime caused by machine failures hits manufacturers hard: Both corporate and frontline workers listed it as one of the top challenges facing the industry. But with today’s technological capabilities, it doesn’t have to be.
Along with procedural advancements, technology advancements have allowed best-in-class manufacturing facilities to move away from reactive and preventive strategies and more toward predictive maintenance that uses machine learning. The goal is to make maintenance so data-driven that the notion of a machine failing unexpectedly becomes a thing of the past.
Preventive maintenance has a number of inefficiencies. Because it operates on a routine schedule, workers may be spending time preemptively upkeeping assets that don’t yet need a tune-up, rather than focusing on higher-value work. Meanwhile, machines close to failure may be overlooked because they’re not scheduled to be serviced yet, which can lead to unplanned downtime when the failure finally occurs. By using technology and continuously monitoring critical machines with AI-driven diagnostics, we can anticipate failures before they occur. This allows you to strategically plan more prescriptive maintenance, reduce the amount of maintenance needed, and eliminate unexpected downtime events.
How Machine Health Leads to Optimized Downtime
Understanding Machine Health provides manufacturers with a multitude of benefits. Using technology, manufacturers can collect data insights on what to fix before machines fail, allowing for more optimized maintenance work. It does this by providing:
1) Improved Machine Visibility
Adding sensors to equipment helps companies continuously monitor Machine Health. These sensors collect real-time data, and AI analyzes the data to help workers understand a machine’s performance status. Enabling predictive maintenance means technicians can see and repair machine issues before they fail.
Improving machine visibility drastically accelerates the reduction of unplanned downtime. How can manufacturers be aware of potential problems if they can’t see them? Rather than time-consuming preventive maintenance, AI insights allow workers to apply predictive maintenance based on where failures are most likely to occur.
2) Save Costs Caused by Downtime
For every hour a system is down, whether it is planned or unplanned downtime, a Fortune 1000 company could lose up to $1 million. And with production targets increasing, that cost may be growing.
While preventive maintenance can let machines slip through the cracks, Machine Health provides real-time insights, alerting manufacturers to which machines that need maintenance. This lets production move away from a calendar system that increases the chances of machine failure. Predictive maintenance alerts teams to potential failures before they occur, providing both a safer work environment for maintenance and less downtime for production lines.
3) Optimize Operations
Machine Health data can be used to optimize operational methodologies. For instance, if the Machine Health status drops off when the production team at a manufacturing facility operates a line at a certain speed, a high-level decision might be made to halt operating the line at that speed. Insights can be continuously harvested and utilized to inform and optimize operational decisions.
Machine Health can play a factor in operational decisions in all sorts of industries. It doesn’t matter what your specific operations are — from machines pulling rollercoaster carts to oil harvesting to vaccine manufacturing — everyone benefits from optimizing operations. From avoiding unplanned downtime to upping safety concerns, machines can help you do all of it — if you listen to them carefully.
Making predictive decisions to perform condition-based maintenance can reduce downtime in production. To learn more about utilizing this approach in your own business, get in touch today.