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ENHANCING ACCURACY IN PROCESS SHIFT DETECTION: A MONTE CARLO SIMULATION APPROACH FOR np-EWMA AND np-HEWMA CONTROL CHARTS
Keywords:
control charts, mixed control charts, Monte Carlo simulations, np EWMA, np-HEWMA.DOI:
https://doi.org/10.17654/0972361723071Abstract
Control charts serve the purpose of detecting shifts in a process, with early detection being of paramount importance in the industrial context. Aslam et al. [2] introduced a mixed control chart utilizing exponentially weighted moving average (EWMA) statistics, employing an approximation method to calculate the average run length. Contrarily, Haq and Woodall [6] advocated the application of Monte Carlo simulation for determining the average run length when using EWMA statistics in control charts. This paper focuses on the development of np-EWMA and np-HEWMA control charts via Monte Carlo simulations. The utilization of Monte Carlo simulation results is posited to offer superior accuracy compared to the approximation method.
Received: September 27, 2023
Revised: October 14, 2023
Accepted: November 21, 2023
References
M. Aslam, M. Azam, N. Khan and C.-H. Jun, A mixed control chart to monitor the process, International Journal of Production Research 53(15) (2015), 4684 4693.
M. Aslam, N. Khan, M. S. Aldosari and C.-H. Jun, Mixed control charts using EWMA statistics, IEEE Access 4 (2016), 8286-8293.
S. V. Crowder, Average run lengths of exponentially weighted moving average control charts, Journal of Quality Technology 19(3) (1987), 161-164.
A. Erem and T. Mahmood, A bivariate CUSUM control chart based on exceedance statistics, Quality and Reliability Engineering International 39(4) (2023), 1172-1191.
W. Febrina and W. Fitriana, Exponential weight moving average (EWMA) control chart for quality control of crude palm oil product, International Journal of Management and Business Applied 1(1) (2022), 19-27.
A. Haq and W. H. Woodall, A note on an average run length calculation for the EWMA and other charts, Quality and Reliability Engineering International 38(8) (2022), 4351-4355.
X. Hu, S. Zhang, J. Zhang and A. Saghir, Efficient CUSUM control charts for monitoring the multivariate coefficient of variation, Computers and Industrial Engineering 179 (2023), 109159.
A. Javaid, M. Noor-ul-Amin and M. Hanif, Performance of Max-EWMA control chart for joint monitoring of mean and variance with measurement error, Communications in Statistics-Simulation and Computation 52(1) (2023), 1-26.
N. Khan and M. Aslam, On updating the results of the control chart using a modified EWMA statistic, Quality and Reliability Engineering International 39(6) (2023), 2202-2205.
N. Khan, M. S. Nawaz, R. A. Sherwani and M. Aslam, Moving average EWMA chart for the Weibull distribution, Communications in Statistics-Simulation and Computation 52(5) (2023), 2231-2240.
M. Naveed, M. Azam, M. S. Nawaz, M. Saleem, M. Aslam and M. Saeed, Design of moving average chart and auxiliary information based chart using extended EWMA, Scientific Reports 13(1) (2023), 5562.
A. Saghir, X. Hu, K. P. Tran and Z. Song, Optimal design and evaluation of adaptive EWMA monitoring schemes for inverse Maxwell distribution, Computers & Industrial Engineering (2023), 109290.
W. H. Woodall, The design of CUSUM quality control charts, Journal of Quality Technology 18(2) (1986), 99-102.
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