A new approach to design efficient univariate control charts to monitor the process mean. (1st August 2018) - Nawaz, Tahir; Raza, Muhammad Ali; Han, Dong
Abstract: Designing of efficient control charts for monitoring manufacturing processes and identifying assignable cause of variations is of constant interest in statistical process control. The present study incorporates neoteric ranked set sampling to design more efficient Shewhart, cumulative sum, and exponentially weighted moving average control charts for monitoring the mean of normal process. Using Monte Carlo simulations, the performance of the proposed charts is evaluated and compared with competing control charts on the basis of average run length and standard deviation of the run length for detection of shift of a specific magnitude. The extra quadratic loss is used to assess the overall performance of the control charts to detect shifts of various magnitudes. To further support the findings, a simulation study is carried out which revealed the supremacy of the proposed control charts over the existing charts. A real data set from a combined cycle power plant is used to demonstrate the application of the proposed control charts.