NDLI: Evaluating the statistical process control performance for monitoring stationary observations using Monte Carlo simulation

Content Provider	IEEE Xplore Digital Library
Author	Kandananond, K.
Copyright Year	2010
Description	Author affiliation: Rajabhat University Valaya-Alongkorn, 1 Moo 20 km 48, Klong-Luang Dist., Prathumthani, Thailand (Kandananond, K.)
Abstract	This research aimed to evaluate the performance of the statistical control charts under the situation that the observations were correlated. The autoregressive moving average model, ARMA (1, 1), was utilized to characterize the disturbances. A process model was simulated to achieve the response, the average run length (ARL). The factorial design of experiment was conducted to define the impacts of critical factors e.g. ARMA coefficients, types of charts and shift sizes on the ARL. The experimental results showed that the exponentially weighted moving average (EWMA) was the most appropriate chart to monitor the ARMA (1, 1) observations. Additionally, both AR and MA parameters along with shift sizes had significant effects on the ARL. Therefore, the available results indicated that conditions at which the minimization of ARL can be achieved. If the performance of the statistical process control under stationary disturbances is correctly characterized, practitioners will have guidelines for achieving the highest possible performance potential when deploying SPC.
Starting Page	182
Ending Page	186
File Size	188592
Page Count	5
File Format	PDF
ISBN	9781424478170
e-ISBN	9781424478187
DOI	10.1109/CISIM.2010.5643668
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-08
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Statistical Process Control (SPC) Exponentially weighted moving average (EWMA) Process control Control charts Autoregressive moving average (ARMA) Mathematical model Monitoring Analysis of variance Monte Carlo simulation Autoregressive processes
Content Type	Text
Resource Type	Article

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