Many machine manufacturers have been selling in the same way for decades: sell the machine, provide service, and supply spare parts later. This works – until customers start paying closer attention to capacity utilisation, skills shortages and predictable costs. Then it is no longer enough to say “the machine is good”; instead, the questions are: How quickly can production take place? How stable is it? With how little effort?
TRUMPF: Pay-per-Part as a solution to bottlenecks
TRUMPF describes its “Pay per Part” model as an approach in which TRUMPF helps manage the operation, programming and maintenance of the production cell and detects faults early on because the system is monitored remotely.
The logic behind this is crucial: the provider does not earn money from the sale – but from the customer’s results.
This is more than just a pricing model. It is knowledge as a competitive advantage:
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Knowledge of failure patterns.
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Knowledge of actual capacity utilisation.
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Knowledge of which parameters drive component costs.
Why this is so valuable for SMEs
Many SME customers face similar problems:
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Machines are not running continuously.
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Set-up times are too long.
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There is a lack of staff for programming and optimisation.
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Downtime is costly but poorly documented.
If a provider makes these issues visible through data analysis and resolves them, the benefit is very straightforward:
More output per hour, less stress, better costing.
What “in-depth knowledge” actually means here
For such models, you need three layers of knowledge:
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Status knowledge: What is the machine actually doing right now?
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Cause knowledge: Why has it stopped? Material, programme, operation, maintenance?
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Economic knowledge: What does an hour of downtime cost – and what are the benefits of a particular measure?
AI is particularly helpful in Shifts 1 and 2: patterns in sensor data, anomalies, forecasts.
People are crucial in Shift 3: prioritisation, cost-effectiveness, responsibility.
The transfer for SME machine manufacturers
You don’t have to switch to ‘Pay per Part’ straight away. But you can use the principle:
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Link service packages to availability.
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Plan maintenance proactively.
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Provide customers with a clear OEE/downtime logic.
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Define pilots: 1 line, 1 customer, 90 days.
What SMEs can take away from this
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Good products are no longer enough. Good results win.
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Data is not an IT project, but a building block of the business model.
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AI is most effective when it targets real bottlenecks (downtime, set-up time, scrap).
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Start small but measurable: 90 days, clear targets.
