Design of Risk-Based Univariate Control Charts With Measurement Uncertainty

Control charts are widely used tools of statistical process control (SPC). They are effective tools for indicating process shifts, helping avoid an increased number of defective products in the manufacturing environment. However, their effectiveness is strongly dependent on the performance of the measurement system. In the scientific literature, numerous studies have examined the effect of measurement errors in statistical process control, but only a few of them have focused on developing risk-based control charts that consider the cost of decisions during process control; furthermore, they were developed only for the case of multivariate and adaptive control charts such as T2 and VSSI (Variable sample Size and Sampling Interval) X-bar charts. This paper fills this gap by using the proposed risk-based concept for commonly used control charts such as X-bar, Moving Average (MA), Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts. The effectiveness of the developed risk-based control charts is demonstrated by simulations and real-life datasets.