Quality control is an outstanding area of production management. The effectiveness of applied quality control methods strongly depends on the performance of the measurement system. Many researchers aimed to analyze the effect of measurement errors on conformity or process control and proposed solutions to treat measurement uncertainty. Although both risk-based conformity control and process control solutions have been designed, verification and validation of these methods have not been provided through laboratory experiments. This paper proposes a case study from the automotive industry regarding the application of risk-based conformity control and risk-based control charts. Acceptance intervals and control limits are optimized to minimize the loss associated with incorrect decisions. The optimization is conducted assuming two scenarios: first, the process and measurement errors are simulated, and second, all data points are measured in the laboratory. This study verifies the applicability of risk-based approaches to real industrial problems and compares the results obtained by simulations and experiments, providing information about the achievable cost reduction opportunities granted by simulations.