Validation of risk-based quality control techniques: a case study from the automotive industry

Area of Science:

• Industrial Engineering
• Manufacturing Systems
• Quality Management

Background:

• Effective quality control is crucial for production management.
• Measurement system performance significantly impacts quality control effectiveness.
• Existing risk-based conformity and process control solutions lack experimental validation.

Purpose of the Study:

• To verify and validate risk-based conformity control and control charts using laboratory experiments.
• To optimize acceptance intervals and control limits to minimize losses from incorrect decisions.
• To compare simulation-based results with experimental data in an automotive industry case study.

Main Methods:

• Application of risk-based conformity control and risk-based control charts.
• Optimization of acceptance intervals and control limits.
• Simulation of process and measurement errors.
• Laboratory measurement of all data points.
• Comparative analysis of simulation and experimental results.

Main Results:

• Demonstrated the practical applicability of risk-based approaches in real industrial settings.
• Quantified the cost reduction opportunities offered by simulation-based optimization.
• Validated the effectiveness of optimized risk-based methods in minimizing losses from incorrect decisions.

Conclusions:

• Risk-based quality control methods are applicable and effective in the automotive industry.
• Experimental validation confirms the benefits of risk-based approaches over traditional methods.
• Simulation provides a valuable tool for estimating cost reductions in quality control optimization.