2018. I’m at one of the largest Polish foundries. Rather than looking for an „obvious” robotisation candidate, I start by building a digital twin of the production hall — a model mapping individual process sections that allows evaluating automation potential without stopping the line.
Each section is described through a set of parameters: operation frequency, OHS hazard level, process suitability for automation, estimated ROI. Data is collected at every workstation. Only then do I form a recommendation.
Conceptual diagram — digital twin of the hall: section analysis by hazard level
Conceptual model — the actual digital twin covered over a dozen production sections with full process parametrisation.
The question from management: where do we invest first?
The right question changes everything
I don’t ask „what’s cheapest to automate”. I ask: „where is the human most at risk?”
This isn’t sentimentality — it’s rational economics. Processes with the highest hazard levels also exhibit other characteristics: high staff turnover, sick leave, medical costs, accident risk with legal consequences. OHS and economics point in the same direction here.
Analysis criteria, in order of importance:
- Hazards to life and health (OHS) — does automation eliminate harmful factors: temperature, noise, dust, vibration, accident risk? This is the overriding criterion.
- Process repeatability — can a robot perform the task identically, every time, without exceptions?
- Return on investment (ROI) — when does the investment pay off 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 stops unexpectedly?
Higher score = higher robotisation priority. Primary criterion: OHS hazard level and impact on operator health.
Recommendation #1: cleaning of large-format castings
The most physically destructive work on the floor — grinding, heavy lifting, extreme heat, noise, dust, vibration. The operator is simultaneously exposed to all major harmful factors. No other analysed process showed such a cumulation of hazards.
Harmful factors — large-format casting cleaning
This isn’t just ethics. Large-format casting cleaning also has characteristics that favour automation — high repeatability of large-casting geometry, no complex decision-making, measurable outcome (surface quality). Protecting people and economic logic point in the same direction here.
Recommendation #2: temperature measurement in the furnace
The second priority — equally non-arbitrary. Working at temperature measurement near the furnace involves direct exposure to thermal radiation and the risk of extreme thermal events. Additional argument: an incorrect measurement means a defective casting — a direct financial loss and quality risk for the client. High repeatability, zero error tolerance, clear ROI.
Robotisation potential analysis is not a wishlist of what’s cheapest to implement. It’s a ranked argument for where technology creates the greatest value — for people and the process simultaneously.
Poland vs the world: the scale of the challenge
Data from the International Federation of Robotics (IFR 2024) shows where we stand:
Source: International Federation of Robotics — World Robotics 2024
Poland has 81 robots per 10,000 workers — almost 3× below the EU average and 5× below Germany (429/10,000). We’re the largest robotics market in Central and Eastern Europe, but the gap is enormous. That’s precisely why the quality of automation decisions matters so much here — there’s no room for expensive mistakes.
A warning: when the „obvious choice” costs billions
Tesla’s 2017–2018 story is a textbook example of a mistake that happens across every industry. Tesla installed hundreds of industrial robots to produce 5,000 cars per week. The result? It couldn’t produce even 2,500.
Elon Musk publicly acknowledged: „Excessive automation was a mistake. Humans are underrated.”
Robots perform repetitive tasks in stable environments — and they excel at it. Humans remain irreplaceable where flexibility and judgment in exceptional situations are required. Hybrid solutions are optimal — not full automation.
What actually gets automated
Global statistics show the dominance of palletising and material handling — processes with clear ROI and low hazard. In a foundry environment, where harmful factor accumulation is exceptionally high, the analysis starts from a different place.
Conclusion: potential analysis, not a wishlist
After this analysis in 2018, I returned to management with a recommendation that surprised many. They expected me to point to a cheaper, faster-to-implement target. The argument was simple: if we can’t justify prioritising robotisation where the stakes are an operator’s health and life, we can’t justify it anywhere.
Robotisation potential analysis is not a political document. It’s a ranking in which protecting people and creating lasting process value go hand in hand — because only such projects make sense in the long run.
Have you ever seen a process at your facility that „everyone knew should be automated” — but nobody calculated the true cost of not automating it for the people doing it?
Sources: IFR — World Robotics 2024 (ifr.org) | B. Büchel, D. Floreano, IMD Case Study: Tesla 2018 | S. Gibbs, The Guardian, 16.04.2018 | AutomatykaOnline.pl — ROI of automation | pro-assem.pl — industrial robot applications | CentrumMaszynCNC.pl — process selection criteria | Photos: personal archive, Krakodlew / GIFA 2019
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