Walk any Swiss production floor and you will find more instrumentation than anyone uses. Machines emit data. Some of it is stored. Almost none of it changes a decision that would otherwise have been made differently.
That gap — between collection and consequence — is the whole of Industrie 4.0 in practice. Everything else is procurement.
Why the pilots do not scale
Three reasons, and none is technical.
The pilot was chosen for feasibility, not value. It ran on the newest line because that line already had connectivity. The newest line was also the one with the fewest problems, so the measured improvement was small and the business case for rolling out to the older lines never materialised.
Nobody owned the decision. The dashboard exists. It is open on a screen in the corridor. No one's job description says they act on it, so nobody does.
The data model was per-machine. Each vendor's telemetry lives in its own schema, and comparing across machines requires work nobody scoped. The comparison is where the value was.
What should a Swiss manufacturer instrument first?
The constraint. Not the newest machine, not the most expensive one — the one that sets the pace of the whole line. Improvement anywhere else produces inventory rather than throughput.
If you do not know which station is the constraint, that is the first week of work, and it can be done with a stopwatch and a clipboard before anything is connected.
The sequence that produces output in a quarter
- Weeks 1–2. Identify the constraint and baseline it manually. OEE by shift, measured by hand. Painful, cheap, and the number everything else is judged against.
- Weeks 3–5. Instrument that station only. One machine, one data path, one owner.
- Weeks 6–8. Put the number where the decision is made — the shift handover, not a corridor screen. Name the person who acts on it.
- Weeks 9–12. Measure the change against the manual baseline. If it did not move, stop and find out why before adding a second station.
Twelve weeks, one station, one decision. That is a slower start and a much faster rollout, because the second station reuses everything the first one proved.
What is a realistic OEE improvement?
For Swiss mid-market discrete manufacturing starting from an unmeasured baseline, the first honest measurement usually lands lower than management expects — frequently in the 45–65% range where the assumption was 80%. The gap is almost always unrecorded micro-stoppages and changeover time.
The improvement then comes from the measurement itself before any automation: making stoppages visible changes behaviour on the floor. Ranges are typical observations, not commitments, and the honest answer for any specific line is that it depends on where the losses sit.
Where the Swiss context changes the calculation
| Factor | Effect |
|---|---|
| High labour cost | Automation payback is faster than EU averages suggest |
| Small batch, high mix | Rigid automation underperforms; flexibility beats throughput |
| Long equipment life | Retrofit connectivity, do not wait for replacement cycles |
| Skilled, stable workforce | Operator knowledge is an asset — instrument to support it, not replace it |
| nFADP and works councils | Anything that can measure individual performance needs consultation early |
That last row is regularly underestimated. Telemetry that can be attributed to a named operator is personal data, and introducing it without consultation converts a technical project into an industrial-relations one. Design it as aggregated by station from the start.
What about AI on the shop floor?
Only after the data path is reliable and someone owns the decision. Predictive maintenance on a machine whose telemetry has gaps produces confident predictions from incomplete data, which is worse than no prediction because it is trusted.
The sequence is the same one we describe for getting AI past the pilot stage: name the decision, name the owner, build the data path, then the model.
The ERP question
Sooner or later the shop-floor data needs to meet the order book, and that is an ERP integration rather than an IoT project. If an ERP replacement is already on the roadmap, sequence the two deliberately — instrumenting a process you are about to redesign wastes both efforts. Our view on running that selection quickly is in ERP selection for the Swiss mid-market.
For a broader view of the Swiss industrial base and its productivity data, the Federal Statistical Office publishes the underlying series.
Related reading
- ERP selection for the Swiss mid-market
- Why AI pilots stall in Swiss companies
- Digital transformation consulting
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