Rethinking control chart design and evaluation

Woodall, William H.; Faltin, Frederick W.

doi:10.1080/08982112.2019.1582779

Cited by 51 publications

(29 citation statements)

References 36 publications

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“…We have demonstrated that the NED chart is capable of detecting various size shifts quickly. While we find this to be a great strength, Woodall and Faltin [28] have suggested that it may be undesirable to immediately detect very small shifts that will not impact the quality of a finished product. We agree with Woodall and Faltin [28] that such determinations depend on the specific application and should be based on engineering judgment.…”

Section: F I G U R Ementioning

confidence: 67%

“…While we find this to be a great strength, Woodall and Faltin [28] have suggested that it may be undesirable to immediately detect very small shifts that will not impact the quality of a finished product. We agree with Woodall and Faltin [28] that such determinations depend on the specific application and should be based on engineering judgment. The FSI charts (e.g., the ANE chart) wait for 1 unit of time before sampling the next sample even if the charting statistic is very close to the control limit.…”

Section: F I G U R Ementioning

confidence: 67%

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An adaptive nonparametric exponentially weighted moving average control chart with dynamic sampling intervals

Liu

Peng

Lai

et al. 2020

Statistical Analysis

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Nonparametric statistical control charts have been widely studied over the past decade. The variable sampling intervals (VSIs) methods have been found to exhibit superior performance compared to the traditional fixed sampling policy in this area. In previous VSI studies, the sampling interval function d(⋅) realized only two distinct values. Limiting the selection of the sampling interval to two fixed values may not be reasonable, especially when the monitoring statistic value is at the edge or near the middle of these two intervals so that the advantages of VSI methods cannot be fully reflected. Therefore, we propose a nonparametric exponentially weighed moving average control chart with a dynamic sampling interval in this article, in which the dynamic sampling interval is an extension of two fixed sampling intervals. The dynamic sampling interval can be continuous valued and is determined by an indicator of the magnitude of the unknown shift. This chart can efficiently detect various magnitudes of shifts and performs robustly for different distributions according to simulation studies. The small computational cost indicates that the method is applicable for monitoring large data streams. Data from an aluminum electrolytic capacitor manufacturing process are used to illustrate the implementation of this chart.

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Section: F I G U R Ementioning

confidence: 67%

Section: F I G U R Ementioning

confidence: 67%

An adaptive nonparametric exponentially weighted moving average control chart with dynamic sampling intervals

Liu

Peng

Lai

et al. 2020

Statistical Analysis

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“…For example, Albers and Kallenberg 9,10 also introduced the bias criterion, which aims to provide a specified control chart performance in expectation. Some other recent ideas on control chart design and evaluation can be found in Testik et al 62 , Atalay et al 63 , and Woodall and Faltin, 64 among others.…”

Section: Other Developmentsmentioning

confidence: 99%

“…We have the following thoughts for future research: Woodall and Faltin 64 argued that it is important to ensure the practical significance of control chart signals, not just statistical significance. The importance of specified process changes should be specified by the practitioner, not measured in units of the standard error as is the standard in practice.…”

Section: Summary and Recommendationsmentioning

confidence: 99%

On the design of control charts with guaranteed conditional performance under estimated parameters

Does

Goedhart

Woodall

2020

Quality & Reliability Eng

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When designing control charts the in‐control parameters are unknown, so the control limits have to be estimated using a Phase I reference sample. To evaluate the in‐control performance of control charts in the monitoring phase (Phase II), two performance indicators are most commonly used: the average run length (ARL) or the false alarm rate (FAR). However, these quantities will vary across practitioners due to the use of different reference samples in Phase I. This variation is small only for very large amounts of Phase I data, even when the actual distribution of the data is known. In practice, we do not know the distribution of the data, and it has to be estimated, along with its parameters. This means that we have to deal with model error when parametric models are used and stochastic error because we have to estimate the parameters. With these issues in mind, choices have to be made in order to control the performance of control charts. In this paper, we discuss some results with respect to the in‐control guaranteed conditional performance of control charts with estimated parameters for parametric and nonparametric methods. We focus on Shewhart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) control charts for monitoring the mean when parameters are estimated.

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“…Modified and Acceptance charts are also powerful tools to avoid many false alarms and this is especially important nowadays when there are several systems with many control charts being used simultaneously, as emphasized recently by Woodall and Faltin (2019). Modified (and Acceptance) control charts generate less false alarms (compared with the Shewhart X chart) because, as explained above, they are designed to detected only genuinely important changes in the process mean (changes that generate a rate of non-conforming items larger than what is specified).…”

Section: Introductionmentioning

confidence: 99%

Modified control chart for monitoring the variance

Landim¹,

Jardim²,

Oprime³

2021

BJO&PM

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ABSTRACT. Goal: The main objective of this research paper is to propose a chart, named Modified Control Chart, where the process variance () is allowed to be larger than the in-control variance value () until a maximum value (), as long as the process remains capable, in the sense that it produces a specified (tolerated) small fraction of non-conforming items. Design / Methodology / Approach: The research methodology was quantitative approach with statistical analysis of simulated data, to assess the practical impact of variance increase in process control in terms of quality requirements. Results: The analysis of the simulated data showed that by using the proposed Modified Control Chart the number of unnecessary interventions in the process could be decreased, contributing to improve its efficiency. Limitations of the investigation: The analysis assumed mean and variance known, which is unlikely to occur in real applications. Research considering unknown parameters is also in progress. Practical implications: The S2 Modified Control Chart detects only genuinely increases in the process variance, which significantly increase the rate of non-conforming items being produced, preventing unnecessary process stop and assessment for assignable causes if only a small increase in the process variance occurs, contributing for higher process efficiency and reduce costs. Originality / Value: This paper introduces a new control chart to monitor the variance of quality characteristics being produced by capable processes, preventing process overcontrol.

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Rethinking control chart design and evaluation

Cited by 51 publications

References 36 publications

An adaptive nonparametric exponentially weighted moving average control chart with dynamic sampling intervals

An adaptive nonparametric exponentially weighted moving average control chart with dynamic sampling intervals

On the design of control charts with guaranteed conditional performance under estimated parameters

Modified control chart for monitoring the variance

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