RelTest combines experimental design, statistical evaluation and technical product understanding. This turns test and field data into robust conclusions rather than isolated metrics.
From experimental design to prediction, the technical question remains the starting point. Statistics is applied where it strengthens the conclusion and supports the decision.
What we work on
We define responses, factors and levels, select an appropriate design and account for interactions, randomisation and required repetitions.
What you receive
An efficient experimental plan that enables robust cause-and-effect conclusions with reasonable effort.
What we work on
Load profiles, acceleration models, sample size, censoring and evaluation are aligned with the required lifetime statement.
What you receive
A testing and evaluation strategy with defined validity, test duration and statistical assurance.
What we work on
Usage data, population, mileage, complaints and failure times are cleaned, classified and placed within a robust reference framework.
What you receive
A transparent view of failure behaviour, affected populations and relevant influencing factors in the field.
What we work on
Suitable statistical or physics-informed models describe future behaviour. Model limits and prediction uncertainty are assessed explicitly.
What you receive
A robust prediction of lifetime, failure probability or future need for action.
Data is most valuable when the decision it needs to support is clear before collection begins.
The potential test scope exceeds time and budget, and a suitable DoE is needed to identify relevant factors efficiently.
Test results exist, but sample size, variation or acceleration assumptions still prevent a robust conclusion.
Different reference quantities and usage populations need to be harmonised before a prediction is possible.
Learn more about experimental design, testing and robust predictions in our knowledge section.
We review the question, available data and test framework and define a robust approach.
