Design of Cumulative Sum Schemes for Start-Up Processes and Short Runs

Zantek, Paul F.

doi:10.1080/00224065.2006.11918624

Cited by 23 publications

(19 citation statements)

References 20 publications

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“…The value k = 0.25, however, gives a higher likelihood of detection within 30 observations. The effect of the design constants on detection performance is studied in detail by Zantek [11].…”

Section: The Run-length Distributionmentioning

confidence: 99%

A Markov-chain method for computing the run-length distribution of the self-starting cumulative sum scheme

Zantek

2008

Journal of Statistical Computation and Simulation

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To cite this article: Paul F. Zantek (2008) A Markov-chain method for computing the run-length distribution of the self-starting cumulative sum scheme, Journal of Statistical Computation and Simulation, 78:5, 463-473,We propose an analytic method for computing the run-length distribution of the cumulative sum (CUSUM) of Q statistics. The method is based on a model in which the operation of this CUSUM is embedded in a nonstationary, discrete-time Markov chain. The calculations of the method agree closely with those of Monte Carlo simulation, supporting the method's accuracy. Our results facilitate understanding the effectiveness of the CUSUM of Q statistics in detecting process mean shifts.

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Section: The Run-length Distributionmentioning

confidence: 99%

A Markov-chain method for computing the run-length distribution of the self-starting cumulative sum scheme

Zantek

2008

Journal of Statistical Computation and Simulation

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“…Due to the potential complexity present in MC environments, Statistical Process Control (SPC) procedures tend to require a considerable number of control charts to monitor quality characteristics and product specifications (Fan & You, 2009;Zantek, 2006). According to Gu et al (2013), this method is not appropriate, since the successive state of the manufacturing process cannot be reflected and out-of-control situations may not be observed.…”

Section: Introductionmentioning

confidence: 98%

“…The standard approach for monitoring quality control in this environment is to ignore the lag formed by the production of different products, in order not to restart the control chart for the same product (Fan & You, 2009;Zantek, 2006). The warm-up is not considered in this approach as well as common variability that could be estimated through the entire series (Gu, Jia, You, & Zhang, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…If product k is in production, only observations of product k are possible. For all other products, the quality characteristic is not observed at time t. In this kind of environment, the use of more than one control chart to monitor quality control characteristics of different products is frequent -one for each product (Fan & You, 2009;Zantek, 2006). Traditional approaches involve the setup of control charts (Phase I), requiring 400 nÀ1 subgroups of retrospective data under control to estimate the process parameters while for the use of X Charts it is necessary at least 300 data for the control chart to operate as long as if its parameters are known (Quesenberry, 1993).…”

Section: Introductionmentioning

confidence: 99%

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Control charts for flexible and multi-variety production systems

Korzenowski

Vidor

Vaccaro

et al. 2015

Computers & Industrial Engineering

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“…iii) using "self-starting" control charts (Hawkins [5], Quesenberry [17], Quesenberry [19], Sullivan and Jones [24], Zantek [26], Hawkins and Maboudou-Tchao [6]). …”

Section: Introductionmentioning

confidence: 99%

An Enhanced Control Chart for Start-Up Processes and Short Runs

Capizzi¹,

Masarotto²

2012

Quality Technology & Quantitative Management

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Classic charting procedures are usually designed assuming that process parameters are known or may be estimated using large Phase I samples gathered before a production run. However, in some manufacturing settings, such as during the process start-up, historical data cannot be collected to accurately estimate the in-control process parameters. In this article, we suggest a new self-starting control chart which uses consecutive observations to jointly update the parameter estimates and check for out-of-control conditions. In particular, we introduce a charting procedure, ACUSCORE, that updates the reference pattern of a type-CUSCORE chart using an adaptive EWMA. The proposed control chart seems to outperform traditional self-starting control charts which neglect the dynamic pattern of the mean change

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Design of Cumulative Sum Schemes for Start-Up Processes and Short Runs

Cited by 23 publications

References 20 publications

A Markov-chain method for computing the run-length distribution of the self-starting cumulative sum scheme

A Markov-chain method for computing the run-length distribution of the self-starting cumulative sum scheme

Control charts for flexible and multi-variety production systems

An Enhanced Control Chart for Start-Up Processes and Short Runs

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