Title: A double-sampling approach for multivariate control charting using multiple measurement strategy and simulation
Authors: Saeid Sharafi; Parnaz Khanbeygi; Mohammad Reza Maleki; Ali Salmasnia
Addresses: Department of Industrial Engineering, Faculty of Engineering, University of Qom, Iran ' Department of Industrial Engineering, University of Eyvanekey, Eyvanekey, Iran ' Industrial Engineering Group, Golpayegan College of Engineering, Isfahan University of Technology, Golpayegan, 87717-67498, Iran ' Department of Industrial Engineering, Faculty of Engineering, University of Qom, Iran
Abstract: To reduce the quality loss cost imposed on the manufacturer, it is essential to develop multivariate control charting methods to be more sensitive to process disturbances than conventional ones. Another important aspect in developing multivariate charts is taking the extra variability caused by measurement instruments into account. Since employing a double sampling (DS) strategy leads to enhancing the detectability of statistical process monitoring techniques, this paper develops two DS-based multivariate control charting methods in the presence of gauge measurement error. To accomplish that, an additive covariate model is used to evaluate how gauge inaccuracy affects the run length properties of the developed charts. Moreover, the negative impact of measurement error is reduced by extending the developed chart statistics through employing multiple measurement strategy. The results of simulation studies confirm that the measurement errors affect the sensitivity of both charts while taking several inspections per item significantly reduces the error's effect.
Keywords: additive covariate model; multivariate control chart; multiple measurements strategy; double sampling; run length.
DOI: 10.1504/IJMDM.2024.137004
International Journal of Management and Decision Making, 2024 Vol.23 No.2, pp.137 - 153
Received: 24 Jul 2022
Accepted: 03 Sep 2022
Published online: 01 Mar 2024 *