The analysis of test result stability is an essential methodology to ensure that a test system continues to deliver reliable, repetitive, and consistent measurements over time. This practice is fundamental to maintaining the reliability of the production process and avoiding false failure diagnoses.
🧪 Why is result stability so important?
Even an automated test system can suffer deviations over time caused by:
- Drift of measurement instruments
- Environmental variations (temperature, humidity, EMI)
- Changes in fixture, cables, or connectors
- Software or firmware updates
- Operator changes without requalification
If these factors are not controlled, the system can fail good products (false positives) or release defective products (false negatives), directly affecting quality, rework, and brand image.
🔍 How does stability analysis work?
The analysis generally involves:
- Selection of a reference unit (Golden or Silver Unit)
- Execution of repetitive tests over a period (e.g., 30 cycles, 1x per shift, etc.)
- Collection and statistical analysis of data (mean, standard deviation, trends)
- Calculation of indicators such as CPK, PPK, R&R or control charts
- Generation of periodic validation reports
🛠️ How AJOLLY Testing implements this practice
At AJOLLY Testing, stability is considered a critical pillar in the design of test solutions:
- Benches with self-test and automatic validation with Golden Unit
- Continuous data logging in a database for statistical analysis
- Creation of dashboards with stability indicators (such as FPY, Top Failures, repeatability)
- Technical support for interpreting variations and preventive or corrective action plans
🎯 Benefits of stability analysis
| Benefit | Real impact on operations |
|---|---|
| Reduction of intermittent failures | Less rework and less uncertainty |
| Increased confidence in testing | More assertive decisions in NPI and production |
| Basis for certifications | Technical support for ISO audits |
| Continuous improvement | Data that feeds Kaizen actions |
Measurement without stability is just variation. With stability analysis, you transform your test into a reliable basis for decision-making.