Unplanned machine downtime is bad news for any manufacturer. But as Florent Alvez, Technical Director of KeyProd, explains the latest innovations in production monitoring solutions are increasingly providing the eyes and ears to help companies spot issues in advance and take preventative action to avoid the costly impact of stoppages.
If you’re a manufacturer, you don’t need me to tell you that production downtime is bad for business. That’s logical, regardless of the industry sector in which you operate. Every second a machine or production line is down spells very bad news for that manufacturing business. Slowing down overall throughput, delaying orders, annoying customers (possibly even losing some), and potentially damaging brand reputation in the most extreme examples if the issue is a reoccurring one.
The Cost of Downtime
If that doesn’t sound bad enough, the actual financial losses due to machine downtime are the stuff of nightmares. In the manufacturing industry generally, the cost of downtime is reportedly around approximately $260,000 per hour (according to a study by Aberdeen Research). However, for particular sub-sectors, it’s even worse. A recent report by Senseye showed that an hour of unplanned downtime in an automotive plant cost over $2 million.
What’s more, due to increased costs of energy, repairs, and materials, unexpected downtime cost manufacturers at least 50% more in 2022 than it did between 2019 and 2020. Silence is Golden, and so goes the song, but context is everything.
The good news is that manufacturers do not need to wait for a machine issue to arise before springing into action to remedy the problem. And let’s face it, even if that was the plan, if the figures above are anything to go by, you’re simply not going to be fast enough to stem the flow before potentially leaking thousands in lost revenue.
Predictive Maintenance
Thankfully, there are various methods that manufacturers large and small can use to stay ahead of the curve and address issues before they arise to prevent – or at the very least, minimize – downtime. The application of equipment maintenance routines, holding essential spare parts in inventory, and ensuring equipment receives the necessary upgrades as scheduled are among the options available.
However, if you are a manufacturer yourself, it’s worth spending some time researching some of the more innovative breakthrough production monitoring solutions that are now available to support that ever-present quest to reduce machine downtime.
Some of these platforms bring new levels of digitization to production environments by connecting, tracking, and measuring the effectiveness of the whole manufacturing process – including existing software, machinery, and associated applications.
A Crystal Ball for Manufacturers
Some systems even use AI and various IOT devices to provide a consolidated real-time vision of production operations to analyze different quality and performance indicators, such as overall equipment effectiveness.
This type of innovation can, in some cases, issue real-time notifications that allow operators to swiftly intervene to prevent minor machine issues from escalating, ensuring production continuity and avoiding the dreaded sound of silence on the factory floor. Thanks to this targeted insight, companies can optimize OEE, increase productivity, and reduce costs by enabling corrective action and preventative maintenance.
For manufacturers, this is about as close as you can get to having a crystal ball and indicates our own KeyProd solution. Translating any machine’s vibrations, it analyses performance data to help operators identify the various sources of losses, such as machine failures, micro-stoppages, or defects.
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The AI-m of the Game
Such advanced production monitoring solutions are increasingly using AI to further improve their capabilities and offer even more critical insight to manufacturers. In the case of KeyProd, analysing and interpreting machine vibrations is fundamental to the solution and AI is critical to enabling that.
Our engineers are currently exploring how machine learning and AI can help interpret machine vibrations even more precisely by looking at patterns and identifying the vibratory signature of manufacturing cycles. Building that out, this provides visibility on vibrational ‘drift’ – essentially flagging anomalies or issues.
This will further revolutionize preventative maintenance by leveraging predictive analytics to anticipate machine needs. In turn, manufacturers will be better equipped to achieve the wider objective of more efficient and profitable production by preventing unplanned downtime and avoiding the subsequent cost implications. After all, as the saying goes, prevention is better than cure.
