A Program for ARL Calculation for Multivariate EWMA Charts

Molnau, Wade E.; Runger, George C.; Montgomery, Douglas C.; Skinner, Katina R.; Loredo, Elvira N.; Prabhu, Sharad S.

doi:10.1080/00224065.2001.11980109

Cited by 41 publications

(18 citation statements)

References 11 publications

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“…In addition, Prabhu and Runger 101 provide recommendations for the selection of parameters for a MEWMA chart. Molnau et al 102 presented a program that enables the calculation of the ARL for the MEWMA when the values of the shift in the mean vector, the control limit and the smoothing parameter are known.…”

Section: Mewma Chartsmentioning

confidence: 99%

Multivariate statistical process control charts: an overview

Bersimis

Psarakis

Panaretos

2006

Quality & Reliability Eng

512

313

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In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewharttype control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial least squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.

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Section: Mewma Chartsmentioning

confidence: 99%

Multivariate statistical process control charts: an overview

Bersimis

Psarakis

Panaretos

2006

Quality & Reliability Eng

512

313

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“…The MEWMA UCL is calculated as a function of the Average Run Length (ARL), a weight parameter, and the noncentrality parameter. Montgomery [2009: 191] defines ARL as “the average number of points that must be plotted before a point indicates an out of control condition.” ARL values are generally determined through simulation, integral equation approximation, and Markov chain approximation [Molnau, ]. The weight parameter is essentially a tuning variable whose value supports the robustness of the MEWMA to nonnormal data distribution.…”

Section: Overview Of Control Chartsmentioning

confidence: 99%

Quality Control—An Approach Applying Multivariate Control Charts during the Operation of Systems Involving Human Processes

Mihalcin

Mazzuchi

Sarkani

et al. 2013

Systems Engineering

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Quality assurance during the operation of a system is critical for maintaining performance within desired specifications. Without proper monitoring and control, a system is capable of erratic behavior resulting in degraded performance, unplanned downtime for maintenance, or system failure. Statistical analysis has proven useful when appropriately applied for quality control activities, as is evidenced within the manufacturing industry. The collection of statistical techniques commonly applied to evaluate the performance of systems and/or processes is known as statistical process control. This paper presents an approach concerning the statistical process control technique of control charting, demonstrating its applicability to controlling and monitoring operational systems involving human processes with multiple quality characteristics. Typical control chart application assumes a normal distribution for analyzed data and sufficient data points necessary to collect an appropriately sized sample. The novelty of the proposed approach is in the intent of monitoring the overall operation of a process, focusing on initial inputs and final outputs, while utilizing 100% of process‐generated data from systems where the operational pace does not lend to the collection of large amounts of data points. In this paper, the applicability of the proposed approach is demonstrated on a corporate information technology help desk. An approach designed for systems within the scope of this paper would be beneficial to multiple industries and organizations for evaluation of systems consisting of human‐involved processes.

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“…However, note that ARL 0 and ARL 1 values should be computed each time the objective function is evaluated. In LL 16 , a simulation approach was used whereas in MMR, the Markov chain approach of Runger and Prabhu 6 was implemented using the computer code of Molnau et al 17 . As noted in LL, the economic-statistical design approach would contain sufficient variability.…”

Section: Economic-statistical Design Of the Mewma Chartmentioning

confidence: 99%

Design Strategies for the Multivariate Exponentially Weighted Moving Average Control Chart

Testik

Borror

2004

Quality & Reliability Eng

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The multivariate exponentially weighted moving average (MEWMA) control chart has received significant attention from researchers and practitioners because of its desirable properties. There are several different approaches to the design of MEWMA control charts: statistical design; economic-statistical design; and robust design. In this paper a review and comparison of these design strategies is provided.

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A Program for ARL Calculation for Multivariate EWMA Charts

Cited by 41 publications

References 11 publications

Multivariate statistical process control charts: an overview

Multivariate statistical process control charts: an overview

Quality Control—An Approach Applying Multivariate Control Charts during the Operation of Systems Involving Human Processes

Design Strategies for the Multivariate Exponentially Weighted Moving Average Control Chart

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