A message from Anton Nrecaj
Founder and Managing Director of Panejo GmbHDear Sir or Madam,
if your company is like many technically demanding industrial companies, you may recognize one or more of these situations:
- You repeatedly carry out tests, but cannot clearly explain why one variant works and another does not.
- Changes to geometries, materials or process parameters sometimes lead to improvements, yet it remains unclear which influence was actually responsible.
- Development decisions rely heavily on the experience of individual employees and are difficult to transfer to new products or operating points.
- Prototypes, tooling changes and machine trials are expensive and tie up valuable development and production capacity.
- Your process works in principle, but does not yet deliver sufficiently stable results, optimal yield, or produces too much scrap.
- You have extensive measurement data, but cannot derive a clear technical basis for decisions from it.
- Different departments or experts have different assumptions about the cause of a problem, without being able to test them conclusively.
You are not alone.
Many companies try to solve complex technical problems through additional test series.
But more tests do not automatically lead to a better understanding of the process.
Why additional tests and prototypes often fail to deliver the result you expect …
Many technical companies believe they only need to test enough variants to gradually optimize their products and processes.
They change geometries, materials, temperatures, speeds, pressures or other process parameters and then compare the results.
If one variant does not work, the next one is tested.
If a result is better, they try to transfer this improvement to other products or operating points.
Yet companies often find that the insights gained apply only to one specific test.
A minor change to the product or operating point can lead to completely different results again.
The reason is simple:
More tests generate more data — but not automatically more understanding.
As long as it is not known which physical relationships determine the result, every technical optimization remains trial and error to a certain extent.
The real problem is not that you test too little.
The real problem is that the decisive physical relationships within your product or process are not sufficiently visible.
Without a reliable technical model, it often remains unclear:
- which parameters actually dominate the result,
- which interactions exist between several influencing variables,
- why a particular variant works,
- where critical process limits are located,
- which changes truly offer potential for improvement,
- and whether a result can be transferred to other operating points.
Another prototype can show that something works.
It does not necessarily explain why it works.
This is precisely why many development projects produce ever more iterations without permanently solving the underlying problem.
The truth is: technical optimization does not begin with the next test, but with a reliable understanding of the system.
A suitable simulation makes technical relationships visible that are difficult or impossible to measure directly in real tests.
For example, it shows:
- how forces, stresses and deformations are distributed in a component,
- how heat is transported within a system,
- which flow regions are critical,
- how different physical effects interact,
- which parameters have a major influence,
- and which changes bring hardly any measurable benefit.
Simulation does not fundamentally replace every real test.
It does, however, ensure that tests are performed specifically where they are truly necessary and useful.
Instead of testing ten variants based on intuition, the most promising variants are identified first.
This creates a sound basis for decisions in development, design and production.
Companies do not lose money because they test too little — but because they do not sufficiently understand the decisive technical relationships within their product or process.
A reliable simulation model therefore does more than reduce testing.
It shows which changes actually lead to:
- more stable processes
- higher yield
- less scrap
- shorter development times
- lower material costs
- more robust products
- better technical decisions