In November 1895, Wilhelm Röntgen accidentally discovered something that would change medicine forever. While experimenting with cathode rays in his laboratory in Würzburg, he noticed that a fluorescent screen across the room began to glow – despite being shielded from the direct beam. What followed was one of science’s most celebrated moments of serendipity: the X-ray. For the first time, physicians could see inside the human body without making a single incision. The invisible became visible.
Manufacturing needed its own lightbulb moment – and it’s arrived. After decades of maintaining critical equipment through run-to-failure approaches or scheduled servicing, industry can now see inside its own operations with a clarity that would have seemed like science fiction not long ago. And that visibility enables the ability to act on emerging issues before they escalate, shifting maintenance from reactive to proactive.
The cost of flying blind
For too long, industrial maintenance has operated on fixed schedules – the equivalent of a doctor only seeing patients at their annual check-up, regardless of what might be developing in between.
According to a recent report from ABB, 44 percent of factories experience unplanned downtime at least once a month. The average cost when a line goes down is $170,000 per hour, and for 29 percent of manufacturers, that figure reaches $500,000. Behind every instance of unplanned downtime is an avalanche of operational problems: lost production, panicked maintenance teams, missed customer commitments, and in some cases, safety incidents that could have been avoided entirely.
Strip it back, and the diagnosis is painfully simple: a lack of visibility. Maintenance teams are managing aging assets with shrinking headcounts, and the traditional model – reactive fixes and time-based servicing – simply wasn’t designed for the complexity of modern production environments. Operators can’t manage what they can’t see.
From scheduled to intelligent
Reactive maintenance replaces or services components on a fixed schedule regardless of actual condition, meaning parts are changed when they may not need to be. Meanwhile, failures still occur between intervals because schedules don’t account for real-world variables: load variations, thermal stress, voltage fluctuations, and the cumulative wear patterns unique to each asset in each environment.
Predictive maintenance addresses this directly, using continuous data collection and analytics to identify anomalies before they become failures. But the most sophisticated approaches go further, predicting what’s coming next and translating that foresight into clear recommendations on what to address, and when.
The result is actionable intelligence that guides maintenance teams toward the right intervention at the right time, with the confidence to act before a failure occurs. It turns “what happened?” into “what will happen?” and shifts the window of intervention from after a failure has occurred to well before one becomes inevitable.
The history of diagnostic medicine follows a similar path. The X-ray gave clinical expertise a foundation of objective data on which to act – and diagnosis improved not because doctors became less important or knowledgeable, but because their judgement had something more precise to work with. Conditions that would previously have gone undetected until they became serious could now be caught and treated at the point where intervention was still straightforward. Real-time performance data does the same on the factory floor.
Putting the connected powertrain into practice
The technology to achieve this visibility now exists at scale. Solutions like ABB Ability™ Digital Powertrain connect motors, drives, and driven equipment through continuous sensing and advanced analytics. Manufacturers receive a full picture of asset health alongside clear recommendations on what to address and when. Whether that means an in-house maintenance team managing their own dashboards or leaning on external expertise for more complex, mixed-fleet environments, the principle is the same: data feeds insight, and continuous insight enables earlier, more precise intervention.
The business outcomes go beyond avoiding downtime. Understanding how equipment actually performs – rather than relying on manual checks – allows operators to optimize energy usage. Catching degradation early and addressing it proportionately lengthens asset lifespans. Proactively flagging anomalies mitigates compliance and safety risks before they escalate. And equipment that runs efficiently for longer directly supports circularity targets, increasingly non-negotiable under growing environmental scrutiny.
But the tools are only part of the story. The real shift is cultural and operational, from reacting to failures to anticipating and handling them before it’s too late.
Real stakes, real results
When a power fault caused one of three critical variable speed drives to trip at Xianglu Petrochemicals Zhangzhou – a major petrochemical complex in Fujian Province, China – the stakes were immediately high. Downtime exceeding 24 hours would cause raw materials in the mixer to solidify, with estimated losses of $150,000 in wasted material alone, before factoring in the cost of production loss. The fault occurred during the COVID-19 lockdown in early 2020, making an on-site response impossible.
Because the plant had been operating ABB Ability™ Predictive Intelligence for powertrains since 2018, ABB’s engineering team received the fault notification instantly and assembled a remote support team within hours. Analysis of the performance data identified failed output filter capacitors, later confirmed by on-site measurements. From fault identification to spare part dispatch, the entire process took four hours, and the drive was restored well within the critical 24-hour window. The $150,000 loss was avoided entirely. It’s a masterclass of what becomes possible when continuous data visibility is matched with the capability to act on it decisively and quickly.
Seeing the invisible
Röntgen’s discovery gave medicine a new philosophy: that better outcomes begin with better visibility. Not by replacing clinical expertise, but by giving it something more precise to work with.
Manufacturing is following a similar path. The factories that will lead the next decade aren’t necessarily those with the newest equipment. They’re those that can see clearly into the health of what they already have, and act on that intelligence with speed and confidence. Not reactively, when something has already gone wrong, but predictively – on the basis of data that gets ahead of the curve and sees what’s coming next.
Visibility has always been the precondition for timely, precise decisions. That’s as true on the factory floor as it ever was in the clinic.
