Modified Tukey's Control Chart

Tercero-Gómez, Víctor G.; Ramirez-Galindo, Jose; Cordero-Franco, Álvaro E.; Smith, Matt; Beruvides, Mario G.

doi:10.1080/03610918.2011.606951

Cited by 14 publications

(16 citation statements)

References 16 publications

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“…It has an effective charting structure that exhibits robustness for the skewed distributions. Alemi and several other authors have studied its properties such as Khaliq et al , Lee et al , Torng and Lee, Lee and Torng, Tercero‐Gomez et al , and Torng et al , among many others. The control limits for this chart are defined a

L C L = Q_{1} - L ((), I Q R), C L = Q_{2}, U C L = Q_{3} + L ((), I Q R)

where Q 1 and Q 3 are the first and third quartiles, Q 2 is the median, IQR is deviation of Q 1 and Q 3 , i.e., inter‐quartile range (IQR) is defined by Q 3 −Q 1 , and L is the control limits coefficient that may be fixed at a pre‐specified value of average run length (ARL), denoted by ARL 0 for an in‐control process.…”

Section: Design Of Tukey‐cusum Control Chartmentioning

confidence: 88%

“…It has an effective charting structure that exhibits robustness for the skewed distributions. Alemi 1 and several other authors have studied its properties such as Khaliq et al, 10 Lee et al, 12 Torng and Lee, 32 Lee and Torng, 13 Tercero-Gomez et al, 31 and Torng et al, 33 among many others. The control limits for this chart are defined a…”

Section: Tukey's Control Chartmentioning

confidence: 99%

“…Tukey control chart (TCC) was proposed by Alemi 1 and have been studied by several authors. To mention but a few of these: Borckardt et al, 3,4 Torng et al, 33 Lee, 11 Sukparungsee,28,29 Tercero-Gomez et al, 30 Tercero-Gomez et al, 31 Lee and Torng 13 , and Lee et al, 12 Khaliq et al, 10 Mekparyup et al, 14 and Mekparyup et al 15 This study intends to design a new memory structure for TCC under CUSUM setup. The proposed chart will combine the feature of TCC with CUSUM designs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Tukey–CUSUM Control Chart for Process Monitoring

Khaliq

Riaz

2015

Quality & Reliability Eng

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In real world scenarios, we continuously inspect the performing behavior of different features of processes. A change in the process parameters may affect the product quality and therefore needs a timely identification. For this purpose we have designed new memory structure based on Tukey control chart (TCC) under cumulative sum (CUSUM) setup. The proposed chart, namely Tukey‐CUSUM, combines the feature of TCC with CUSUM design for improved detection of shifts for normal and non‐normal data. The performance of the study proposal is evaluated through different measures based on run length including ARL, EQL, PCI and RARL. We have observed that the proposed Tukey‐CUSUM design is quite robust and efficient, especially using asymmetrical decision intervals for different process models. An application example is also provided to illustrate the study proposal. Moreover, sample selection is always of great concern for practitioners so we have included a brief description of the steps for sample selection with reference to process monitoring. Copyright © 2015 John Wiley & Sons, Ltd.

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L C L = Q_{1} - L ((), I Q R), C L = Q_{2}, U C L = Q_{3} + L ((), I Q R)

Section: Design Of Tukey‐cusum Control Chartmentioning

confidence: 88%

Section: Tukey's Control Chartmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Tukey–CUSUM Control Chart for Process Monitoring

Khaliq

Riaz

2015

Quality & Reliability Eng

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“…Tercero-Gomez at el. [11] designed the Modified Tukey control Chart (MTCC), which has smaller ARL 1 values than TCC. Mekparyup at el.…”

Section: Literature Reviewmentioning

confidence: 99%

On the Performance of Median Based Tukey and Tukey-EWMA Charts Under Rational Subgrouping

et al. 2019

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Control chart (CC) is used to monitor the special causes that arise during the process monitoring.These special causes produce continual shifts in the process parameters that last until it is identified and removed. There is a need for such techniques, which present the true representation of the entire process. Rational subgrouping is an essential concept in Statistical Process Control (SPC) which is seldom overlooked by the practitioner. Hence, most of the manufacturing, engineering, and production processes give output products in the form of batches over smaller intervals of time. The aim of this study is to provide a median based design for Tukey and Tukey-EWMA control charts under subgrouping. It will use the idea of boxplot to monitor the process behavior. This study also provides a brief discussion regarding selecting and forming subgroups from the process data. The performance of the median based Tukey and Tukey-EWMA charts are judged using Average, Median and Standard-Deviation run-length as performance measures. We have considered subgroup sizes of m=1,5 &10 at pre-specified ARL0 equal to 370. To real-life applications of the median based tukey designs are also presented to show their implementation in food manufacturing and hard-bake processes.2

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“…TCC was proposed by Alemi and based on Tukey fences for box plots. Borckardt et al ., Borckardt et al ., Torng and Lee, Torng et al ., and Tercero‐Gomez et al . examined the performance of TCC.…”

Section: Introductionmentioning

confidence: 99%

Performance of Tukey's and Individual/Moving Range Control Charts

Khaliq

Riaz

Alemi

2014

Quality & Reliability Eng

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This paper compares two control charts: Tukey (TCC) and individual/moving range (XmR) control charts. Both are designed to examine single observation per time period, but little is known about which one is more efficient and under what conditions. We simulated data from different distributions and examined the performance of the two control charts on these data. Performance was assessed using the of average run length, extra quadratic loss, median run length, standard deviation run length, performance comparison index, and relative average run length. Overall, TCC was more efficient than XmR, when observations had binomial, Rayleigh, logistic, lognormal, Maxwell, normal, Poisson, Weibull (with α = 10, β = 1), and Student's t (30 and 10 degrees of freedom) distributions. XmR was more efficient when observations had Student's t (with 4 degrees of freedom) and gamma (with α = 4, β = 1) distributions. These results suggest that improvement teams could reach faster conclusions if they use TCC in most common situations.

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Modified Tukey's Control Chart

Cited by 14 publications

References 16 publications

Robust Tukey–CUSUM Control Chart for Process Monitoring

Robust Tukey–CUSUM Control Chart for Process Monitoring

On the Performance of Median Based Tukey and Tukey-EWMA Charts Under Rational Subgrouping

Performance of Tukey's and Individual/Moving Range Control Charts

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