Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
2.4
Journals
Mathematics
Special Issues
Statistical Process Control and Inference: Novelty, Controversies and New Direction
share announcement

Statistical Process Control and Inference: Novelty, Controversies and New Direction

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 3877

Special Issue Editors

Dr. Jean Claude Malela-Majika

E-Mail Website
Guest Editor
Department of Statistics, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria 0028, South Africa
Interests: statistics; statistical process control; performance measures; control charts, etc.
Prof. Dr. Kashinath Chatterjee

E-Mail Website
Guest Editor
Division of Biostatistics and Data Science, Department of Population Health Sciences, Augusta University, 1120 15th St., Augusta, GA, USA
Interests: design of experiments; statistical quality control; reliability theory
Dr. Schalk W. Human

E-Mail Website
Guest Editor
Department of Statistics, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria 0002, South Africa
Interests: statistical process control; distribution theory; nonparametric statistics; statistical inference

Special Issue Information

Dear Colleagues,

In the last few months, a number of articles complaining about the quality, unreliability, impracticability and weaknesses of some control charts and concepts newly introduced in statistical process control (SPC) were published in different journals. The complaint was even extended to the results reported in some articles because of the use of no reliable performance measures such as the zero-state average run-length when evaluating the performance of memory-type charts. The charts reported in these articles include:

  • The HWMA (homogeneously weighted moving average) chart;
  • The extended EWMA (exponentially weighted moving average), i.e., the double EWMA, triple EWMA, quadruple EWMA and other variants of the EWMA control chart;
  • The GWMA (generalised weighted moving average) chart;
  • Progressive mean control chart;
  • Auxiliary information-based control charts;
  • Synthetic control charts;
  • Neutrosophic control charts;
  • etc.

However, a few other articles were also published in order to contradict researchers who campaign against the above charts and concepts. The purpose of this Special Issue is to present a platform that will help researchers to freely discuss topics on controversies in SPC and provide clear directions for a new era of quality research work in SPC. Since SPC theory cannot be dissociated from statistical inference, researchers are welcome to submit original articles on inference.

Dr. Jean Claude Malela-Majika
Prof. Dr. Kashinath Chatterjee 
Dr. Schalk W. Human
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • statistical process control
  • HWMA
  • extended memory-type charts
  • progressive mean chart
  • synthetic chart
  • neutrosophic control chart
  • auxiliary information-based chart
  • zero-state
  • steady-state
  • run-length properties

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

30 pages, 2144 KiB  
Article
Efficient Monitoring of a Parameter of Non-Normal Process Using a Robust Efficient Control Chart: A Comparative Study
by Aamir Majeed Chaudhary, Aamir Sanaullah, Muhammad Hanif, Mohammad M. A. Almazah, Nafisa A. Albasheir and Fuad S. Al-Duais
Mathematics 2023, 11(19), 4157; https://doi.org/10.3390/math11194157 - 3 Oct 2023
Viewed by 869
Abstract
The control chart is a fundamental tool in statistical process control (SPC), widely employed in manufacturing and construction industries for process monitoring with the primary objective of maintaining quality standards and improving operational efficiency. Control charts play a crucial role in identifying special [...] Read more.
The control chart is a fundamental tool in statistical process control (SPC), widely employed in manufacturing and construction industries for process monitoring with the primary objective of maintaining quality standards and improving operational efficiency. Control charts play a crucial role in identifying special cause variations and guiding the process back to statistical control. While Shewhart control charts excel at detecting significant shifts, EWMA and CUSUM charts are better suited for detecting smaller to moderate shifts. However, the effectiveness of all these control charts is compromised when the underlying distribution deviates from normality. In response to this challenge, this study introduces a robust mixed EWMA-CUSUM control chart tailored for monitoring processes characterized via symmetric but non-normal distributions. The key innovation of the proposed approach lies in the integration of a robust estimator, based on order statistics, that leverages the generalized least square (GLS) technique developed by Lloyd. This integration enhances the chart’s robustness and minimizes estimator variance, even in the presence of non-normality. To demonstrate the effectiveness of the proposed control chart, a comprehensive comparison is conducted with several well-known control charts. Results of the study clearly show that the proposed chart exhibits superior sensitivity to small and moderate shifts in process parameters when compared to its predecessors. Through a compelling illustrative example, a real-life application of the enhanced performance of the proposed control chart is provided in comparison to existing alternatives. Full article
(This article belongs to the Special Issue Statistical Process Control and Inference: Novelty, Controversies and New Direction)
►▼ Show Figures

Figure 1

21 pages, 1113 KiB  
Article
Combination of Sequential Sampling Technique with GLR Control Charts for Monitoring Linear Profiles Based on the Random Explanatory Variables
by Ali Yeganeh, Mahdi Parvizi Amineh, Alireza Shadman, Sandile Charles Shongwe and Seyed Mojtaba Mohasel
Mathematics 2023, 11(7), 1683; https://doi.org/10.3390/math11071683 - 31 Mar 2023
Cited by 5 | Viewed by 1382
Abstract
Control charts play a beneficial role in the manufacturing process by reduction of non-compatible products and improving the final quality. In line with these aims, several adaptive methods in which samples can be taken with variable sampling rates and intervals have been proposed [...] Read more.
Control charts play a beneficial role in the manufacturing process by reduction of non-compatible products and improving the final quality. In line with these aims, several adaptive methods in which samples can be taken with variable sampling rates and intervals have been proposed in the area of statistical process control (SPC). In some SPC applications, it is important to monitor a relationship between the response and independent variables—this is called profile monitoring. This article proposes adaptive generalized likelihood ratio (GLR) control charts based on variable sampling interval (VSI) and sequential sampling (SS) techniques for monitoring simple linear profiles. Because in some real-life problems, it may be possible that the user cannot control the values of explanatory variables; thus, in this paper, we focus on such a scenario. The performance of the proposed method is compared under three different situations, i.e., the fixed sampling rate (FSR), VSI, and SS, based on average time to signal (ATS) criteria for phase II analysis. Since the SS approach uses a novel sampling procedure based on the statistic magnitude, it has a superior performance over other competing charts. Several simulation studies indicate the superiority as the SS approach yields lower ATS values when there are single-step changes in the intercept, slope, standard deviation of the error term, and explanatory variables. In addition, some other related sensitivity analysis indicates that other aspects of the proposed methods, such as computational time, comparison with other control charts, and consideration of fixed explanatory variables. Furthermore, the results are supported by a real-life illustrative example from the adhesive manufacturing industry. Full article
(This article belongs to the Special Issue Statistical Process Control and Inference: Novelty, Controversies and New Direction)
►▼ Show Figures

Figure 1

Review

Jump to: Research

30 pages, 2360 KiB  
Review
Homogeneously Weighted Moving Average Control Charts: Overview, Controversies, and New Directions
by Jean-Claude Malela-Majika, Schalk William Human and Kashinath Chatterjee
Mathematics 2024, 12(5), 637; https://doi.org/10.3390/math12050637 - 21 Feb 2024
Viewed by 834
Abstract
The homogeneously weighted moving average (HWMA) chart is a recent control chart that has attracted the attention of many researchers in statistical process control (SPC). The HWMA statistic assigns a higher weight to the most recent sample, and the rest is divided equally [...] Read more.
The homogeneously weighted moving average (HWMA) chart is a recent control chart that has attracted the attention of many researchers in statistical process control (SPC). The HWMA statistic assigns a higher weight to the most recent sample, and the rest is divided equally between the previous samples. This weight structure makes the HWMA chart more sensitive to small shifts in the process parameters when running in zero-state mode. Many scholars have reported its superiority over the existing charts when the process runs in zero-state mode. However, several authors have criticized the HWMA chart by pointing out its poor performance in steady-state mode due to its weighting structure, which does not reportedly comply with the SPC standards. This paper reviews and discusses all research works on HWMA-related charts (i.e., 55 publications) and provides future research ideas and new directions. Full article
(This article belongs to the Special Issue Statistical Process Control and Inference: Novelty, Controversies and New Direction)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop