{"id":14382,"date":"2026-09-15T16:50:26","date_gmt":"2026-09-15T14:50:26","guid":{"rendered":"https:\/\/viewpointsystem.com\/?p=14382"},"modified":"2026-09-15T16:52:04","modified_gmt":"2026-09-15T14:52:04","slug":"railway-workforce-shortage","status":"publish","type":"post","link":"https:\/\/viewpointsystem.com\/en\/blog\/railway-workforce-shortage\/","title":{"rendered":"The Rail Industry’s Skills Gap Isn’t Just About Hiring Fast Enough\u00a0\u00a0"},"content":{"rendered":"\n

In safety-critical technical roles, the real constraint isn’t how quickly a position gets filled; it’s how quickly the person filling it becomes genuinely competent, not just certified.<\/p>\n\n\n\n

The Numbers Behind the Crisis<\/h2>\n\n\n\n

The scale becomes clear through the\u202f<\/mark>VDV\u202ffigures for the DACH region<\/a><\/span>: some 80,000 employees in the sector are expected to retire by 2030, against a hiring target of 110,000. This includes roughly 20,000 bus drivers and 3,000 train drivers that are still needed. The average age among operational staff already sits above 50.

This isn’t a DACH-only pattern. In the UK,\u202fNSAR’s 2025 Rail Workforce Survey<\/mark><\/span><\/a>\u202ffound that a third of the rail workforce is now 50 or older, with an average age of 43.5, and that up to 70,000 workers could leave through retirement or attrition by 2030. Across the EU as a whole, the European Commission’s ninth Rail Market Monitoring Report<\/mark><\/span><\/a> found that 42% of the roughly 900,000 people employed in the sector were already over 50 in 2022 \u2014 with staff shortages named as one of the industry’s core challenges.

Different regions, different numbers, same underlying pattern: a large, experienced cohort is heading for the exit at roughly the same time, and the pipeline behind them is nowhere near full.<\/p>\n\n\n\n

What Digitalisation Already Solves and What It Doesn’t<\/h2>\n\n\n\n

Demographic change is making knowledge transfer and rapid qualification increasingly important. In the rail industry especially, valuable practical experience is lost when experienced staff leave, unless it’s passed on in time.

Digitalisation can help with this. Automation handles repetitive diagnostic and inspection tasks faster and more reliably, and AI-based knowledge systems secure explicit expertise such as regulations, specifications, and inspection protocols. These are important advances that relieve staff and make knowledge permanently available.

But qualification involves more than documented knowledge. In safety-critical situations, it also comes down to correctly perceiving and interpreting anomalies under real-world conditions, recognizing a partially obscured crack, correctly interpreting an unusual sound, or assessing a component that looks fine on the outside but is actually faulty. This ability comes from repeated, guided hands-on experience and cannot simply be automated through software.<\/p>\n\n\n\n

The Real Bottleneck: Time to Full Competency<\/h2>\n\n\n\n

Take the inspection of rail vehicles as an example. The checklist itself, what needs to be checked, in what order, and within which tolerances, can be learned within a few weeks. A new hire can be trained, certified, and technically operational relatively quickly. But that alone doesn’t mean they truly have safe command of the task.

Over time, experienced car inspectors develop something no checklist can fully capture: the trained eye for the vehicle. A hairline crack, a worn-out bearing, unusual wear on a wheelset, or something off about a brake or coupling, often just a few seconds are enough for something to catch the eye. Sometimes before it’s even consciously clear why that particular spot doesn’t fit the usual picture.

This sense is usually built through years of experience gained directly on the vehicle, not in a training room. It’s not only critical for inspecting rail vehicles, but in many other areas of rail operations too, such as train driving or shunting.

This pattern isn’t unique to rail. Any industry with safety-critical, experience-built roles is facing the same clock.<\/p>\n\n\n

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\n \n \"Document\n <\/a>\n <\/div>\n
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\n Document knowledge, before it retires<\/a>\n <\/h3>\n \n
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Experienced staff take years of practical know-how with them into retirement. How to document that knowledge in time, before the gap opens up.<\/p>\n <\/div>\n \n

\n \n READ NOW <\/a>\n <\/div>\n <\/div>\n <\/div>\n \n\n\n\n

How Eye Tracking Shortens the Ramp-Up<\/h2>\n\n\n\n

This is where eye tracking changes the equation. Instead of relying on a trainee’s self-report or a pass\/fail assessment at the end of a course, eye-tracking-based training makes the inspector’s gaze itself visible and measurable.

In practice, this means recording where an experienced inspector looks, in what order, and for how long, so the actual visual pattern behind years of built-up judgment, and comparing it against where a trainee’s gaze goes on the same task. Deviations become visible immediately: a trainee scanning a component too quickly, missing a checkpoint an expert always hits, or fixating in the wrong place. And because the data is objective and repeatable, progress can be tracked across multiple sessions, not just judged on a single moment.

The shift this enables is significant: instead of waiting years to find out whether someone has developed the right instincts, gaze data shows, session by session, whether they’re getting there, and where exactly the gap still is.<\/p>\n\n\n\n

What Rail Operators Can Do Now<\/h2>\n\n\n\n

Closing the rail industry’s skills gap takes more than filling vacancies faster. A few concrete steps make all the difference:<\/p>\n\n\n\n