Some Recent Studies in Statistical Process Control

Qiu, Peihua

doi:10.1007/978-3-030-20709-0_1

Cited by 11 publications

(4 citation statements)

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“…Double sampling schemes are mostly based on the assumption that the subgroup samples do not have either autocorrelation (within-sample correlation) or cross-correlation (between-samples correlation). However, for sequential observations, there tend to be some inherent underlying correlation within the observationssee for instance, Qiu (2019). Therefore, it is important to also focus on double sampling with more emphasis on autocorrelated observations as well as on nonparametric or distribution-free monitoring schemes.…”

Section: Concluding Remarks and Future Research Ideasmentioning

confidence: 99%

Double sampling monitoring schemes: a literature review and some future research ideas

Motsepa

Malela‐Majika

Castagliola

et al. 2021

Communications in Statistics - Simulation and Computation

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Adapted from the acceptance sampling field, the double sampling monitoring schemes implement a two-stage strategy to decide whether the process being monitored is in-control or out-of-control. That is, a master sample is split into two separate subgroup samples, with the first subgroup sample used in the first stage and, depending on which type of double sampling method is used, either only the second or the combined first and second, subgroup sample(s) are used in the second stage. This strategy has been proven to effectively decrease the sampling effort and, at the same time, to decrease the time to detect potential out-of-control situations. For these reasons, it has received some attention in the statistical process monitoring (SPM) literature and, in this review paper, all 87 existing publications on the basic double sampling monitoring schemes and other different schemes that are integrated with the basic double sampling schemes are reviewed. The double sampling schemes are categorized and summarized so that any research gaps in the SPM literature can easily be identified. Finally, concluding remarks and some directions for future research ideas are given.

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Section: Concluding Remarks and Future Research Ideasmentioning

confidence: 99%

Double sampling monitoring schemes: a literature review and some future research ideas

Motsepa

Malela‐Majika

Castagliola

et al. 2021

Communications in Statistics - Simulation and Computation

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“…In some recent advances, a general class of nonparametric Shewhart-type control charts based on modified Wilcoxon-type rank sum statistics has been introduced in [19], while [20] generalized the monitoring schemes proposed in [15] by taking into account the ranks from a larger amount of test observations. For a thorough study and some interesting perspectives on Nonparametric Statistical Process Control, the interested reader is referred to [21][22][23][24][25], while some very recent advances on the topic can be found in [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Wilcoxon-Type Control Charts Based on Multiple Scans

Triantafyllou

2024

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In this article, we establish new distribution-free Shewhart-type control charts based on rank sum statistics with signaling multiple scans-type rules. More precisely, two Wilcoxon-type chart statistics are considered in order to formulate the decision rule of the proposed monitoring scheme. In order to enhance the performance of the new nonparametric control charts, multiple scans-type rules are activated, which make the proposed chart more sensitive in detecting possible shifts of the underlying distribution. The appraisal of the proposed monitoring scheme is accomplished with the aid of the corresponding run length distribution under both in- and out-of-control cases. Thereof, exact formulae for the variance of the run length distribution and the average run length (ARL) of the proposed monitoring schemes are derived. A numerical investigation is carried out and depicts that the proposed schemes acquire better performance towards their competitors.

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“…Meaningful interpretation of the control charts is subject to the basic assumption of SPC, observations within samples are independent and identically normally distributed (i.i.n.d.). Nevertheless, in many situations, it is di cult to achieve the independency assumption [5,6].…”

Section: Introductionmentioning

confidence: 99%

Geometric Brownian Motion-Based Time Series Modeling Methodology for Statistical Autocorrelated Process Control: Logarithmic Return Model

Lee

Liew

Chen

et al. 2022

International Journal of Mathematics and Mathematical Sciences

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Fitting a time series model to the process data before applying a control chart to the residuals is essential to fulfill the basic assumptions of statistical process control (SPC). Autoregressive integrated moving average (ARIMA) model has been one of the well-established time series modeling approaches that is extensively used for this purpose and is widely recognized for its accuracy and efficiency. Nevertheless, the research community commented that its iterative stages are laborious and time-consuming. In addressing this gap, a novel time series modeling technique with its conceptual assumptions of attributes that was derived from the geometric Brownian motion (GBM) law was developed in this study. It was termed as the logarithmic return (LR) model. Then, the model was employed and tested on a real-world autocorrelated data, whereby the results were assessed and benchmarked with the ARIMA model. The findings for LR model reported a mean average percentage error that ranged between 1.5851% and 3.3793% (less than 10%), which were as accurate as the ARIMA model. The running time (in second of CPU time) taken by the LR model was at least 96.2% faster than the ARIMA model. Interestingly, the corresponding multivariate control chart constructed from the LR model also portrayed a similar general conclusion as that of its counterpart. The LR model was obviously parsimonious and easier to compute and took a shorter running time than the ARIMA model. Therefore, it possessed the potential as an alternative time series modeling methodology for the ARIMA model in the procedures of SPC.

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Some Recent Studies in Statistical Process Control

Cited by 11 publications

References 50 publications

Double sampling monitoring schemes: a literature review and some future research ideas

Double sampling monitoring schemes: a literature review and some future research ideas

Wilcoxon-Type Control Charts Based on Multiple Scans

Geometric Brownian Motion-Based Time Series Modeling Methodology for Statistical Autocorrelated Process Control: Logarithmic Return Model

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