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Validation of risk-based quality control techniques: a case study from the automotive industry
Journal of Applied Statistics ( IF 1.5 ) Pub Date : 2021-06-08 , DOI: 10.1080/02664763.2021.1936466
A I Katona 1
Affiliation  

ABSTRACT

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.



中文翻译:

基于风险的质量控制技术的验证:汽车行业的案例研究

摘要

质量控制是生产管理的一个突出领域。应用质量控制方法的有效性很大程度上取决于测量系统的性能。许多研究人员旨在分析测量误差对一致性或过程控制的影响,并提出了处理测量不确定性的解决方案。尽管已经设计了基于风险的符合性控制和过程控制解决方案,但尚未通过实验室实验提供对这些方法的验证和确认。本文提出了一个汽车行业关于基于风险的合格控制和基于风险的控制图应用的案例研究。接受区间和控制限制经过优化,以最大限度地减少与错误决策相关的损失。假设两个场景进行优化:首先,模拟过程和测量误差,其次,在实验室测量所有数据点。本研究验证了基于风险的方法对实际工业问题的适用性,并比较了模拟和实验获得的结果,提供了有关模拟所授予的可实现的成本降低机会的信息。

更新日期:2021-06-08
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