It’s 2018. I’m at one of Poland’s largest foundries, mapping production processes and looking for robotisation candidates. We have budget, board buy-in, and high expectations on every side.
The obvious answer? Casting cleaning.
It’s the most physically punishing job on the floor — grinding, heavy lifting, intense heat, noise, dust. Every operator knows it. Every manager wants it automated. From a human welfare standpoint, it’s the absolute priority.
The problem was: that was the wrong question.
Wrong question = wrong answer
„What’s hardest for people?” is not the same as „what’s the best candidate for robotisation?”
The real decision criteria are different:
- Process repeatability — can a robot perform this task identically, every time, without exceptions?
- ROI timeline — when does the investment pay back, and what are the risks?
- Technical maturity — is the technology stable enough for a production environment?
- Line resilience — what happens to production when the robot unexpectedly stops?
Casting cleaning made the list — but not at the top.
Higher score = better automation candidate based on 4 criteria: repeatability, ROI, technical maturity, line resilience
What actually ranked first — and why it surprised everyone
Temperature measurement in the foundry furnace.
Not dramatic. Nobody visibly suffers. But it satisfies all four criteria simultaneously: fully repeatable, high-frequency, zero tolerance for human error, and a clear ROI calculation. A wrong temperature reading means a defective casting — a direct financial loss and a quality risk for the customer.
A robotisation roadmap isn’t a wish list of what’s hardest for people. It’s a ranked argument for where technology creates the most durable value.
Poland vs. the world: the scale of the challenge
International Federation of Robotics data (IFR 2024) shows where we stand:
Source: International Federation of Robotics — World Robotics 2024
Poland has 81 robots per 10,000 workers — nearly 3× below the EU average and 5× below Germany. We’re the largest robotics market in Central & Eastern Europe, but the gap is real. And that’s exactly why the quality of automation decisions matters so much here — there’s no budget for mistakes.
Source: Windward Studios Manufacturing Automation Statistics 2024
A cautionary tale: when the „obvious choice” costs billions
Tesla’s 2017–2018 story is a textbook example of a mistake that plays out across every industry. Tesla installed hundreds of industrial robots to manufacture 5,000 cars per week. The result? They couldn’t produce even 2,500.
Elon Musk publicly admitted: „Excessive automation was a mistake. Humans are underrated.”
Machines are poor at handling unpredictable situations and small deviations. Humans excel at this. The optimal solution is almost always a smart division of labour between human and machine — not full automation.
What actually gets automated
Palletizing, material handling, welding — not casting cleaning. The processes that win are those that simultaneously satisfy all four criteria.
The takeaway: a roadmap is an argument, not a wish list
After this analysis in 2018, we came back to the board with a recommendation that surprised everyone. The reaction was genuine shock — because casting cleaning was what „everyone knew should go first.”
A robotisation roadmap isn’t a political document. It’s a ranking that creates durable value — where technology genuinely wins over humans, not where we assume it should.
Have you ever seen the „obvious” automation choice turn out to be the wrong one?
Sources: International Federation of Robotics — World Robotics 2024 | IMD Business School — Tesla automation case study | Windward Studios Manufacturing Automation Statistics 2024 | Photos: own archive, Krakodlew / GIFA 2019