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Statistical Process Control in Sustainable Industries
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Statistical Process Control in Sustainable Industries

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Products and Services".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 7017

Special Issue Editors

Prof. Dr. Su-Fen Yang

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Guest Editor
Department of Statistics, National Chengchi University, Taipei City 116, Taiwan
Interests: statistical process control; industrial statistics; probability models
Prof. Dr. Barry C. Arnold

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Guest Editor
Statistics Department, University of California Riverside, Riverside, CA 92521, USA
Interests: statistical process control; probability; statistical theory
Prof. Dr. Muhammad Aslam

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Guest Editor
Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia
Interests: statistical quality control; acceptance sampling plans; statistics; neutrosophic statistical quality control
Prof. Dr. Shin-Li Lu

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Guest Editor
Department of Industrial Management and Enterprise Information, Aletheia University, New Taipei City 251, Taiwan
Interests: industrial statistics; quality engineering; production management; business intelligence
Dr. Ming-Che Lu

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Guest Editor
Department of Accounting, Chaoyang University of Technology, Taichung 413310, Taiwan
Interests: statistical process control and fatigue life models
Dr. Wei-Heng Huang

E-Mail Website
Guest Editor
Department of Statistics, Feng Chia University, Taichung 40724, Taiwan
Interests: statistical process control; statistical computing; reliability analysis

Special Issue Information

Dear Colleagues,

The industrial sector is undeniably one of the world's largest emitters of greenhouse gases. This also has an impact on current climate change, which has resulted in numerous changes in social and economic structures around the world. As a result, the encouragement of sustainable industries is one of the current efforts employed to mitigate the worsening effects of climate change.

The Special Issue aims to present the most recent interdisciplinary research on quality control systems, reliability, and sustainable issues for improving environmental quality and ensuring a better future for the planet. Improved quality control in industries would assist organizations in evaluating business operations and increasing efficiency levels in order to reduce waste and pollution in the industry. In the long-term, this effort is likely to have a positive impact on the environment. The integration of quality control and sustainability is discussed not only in terms of technical processes, but also in terms of personnel integration in business organizations. Furthermore, within the macroeconomic framework, such an integration could help harmonize economic growth with the environment, resulting in future green growth.

This Special Issue invites submission topics including, but not limited to, the following: statistical process control in sustainable industries; statistical process control methods; integration of quality control systems and the concept of sustainability; reliability analysis; green ICT (information communication and technology); energy consumption and green economy; sustainable industry effect on the economy; environmental quality and health issues.

Prof. Dr. Su-Fen Yang
Prof. Dr. Barry C. Arnold
Prof. Dr. Muhammad Aslam
Prof. Dr. Shin-Li Lu
Dr. Ming-Che Lu
Dr. Wei-Heng Huang
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. Sustainability 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 2400 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.

Published Papers (5 papers)

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Research

19 pages, 890 KiB  
Article
A New EWMA Control Chart for Monitoring Multinomial Proportions
by Shengjin Gan, Su-Fen Yang and Li-Pang Chen
Sustainability 2023, 15(15), 11797; https://doi.org/10.3390/su151511797 - 31 Jul 2023
Cited by 1 | Viewed by 766
Abstract
Control charts have been widely used for monitoring process quality in manufacturing and have played an important role in triggering a signal in time when detecting a change in process quality. Many control charts in literature assume that the in-control distribution of the [...] Read more.
Control charts have been widely used for monitoring process quality in manufacturing and have played an important role in triggering a signal in time when detecting a change in process quality. Many control charts in literature assume that the in-control distribution of the univariate or multivariate process data is continuous. This research develops two exponentially weighted moving average (EWMA) proportion control charts to monitor a process with multinomial proportions under large and small sample sizes, respectively. For a large sample size, the charting statistic depends on the well-known Pearson’s chi-square statistic, and the control limit of the EWMA proportion chart is determined by an asymptotical chi-square distribution. For a small sample size, we derive the exact mean and variance of the Pearson’s chi-square statistic. Hence, the exact EWMA proportion chart is determined. The proportion chart can also be applied to monitor the distribution-free continuous multivariate process as long as each categorical proportion associated with specification limits of each quality variable is known or estimated. Lastly, we examine simulation studies and real data analysis to conduct the detection performance of the proposed EWMA proportion chart. Full article
(This article belongs to the Special Issue Statistical Process Control in Sustainable Industries)
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20 pages, 2683 KiB  
Article
The Non-Linear Relationship between Air Pollution, Labor Insurance and Productivity: Multivariate Adaptive Regression Splines Approach
by Syamsiyatul Muzayyanah, Cheng-Yih Hong, Rishan Adha and Su-Fen Yang
Sustainability 2023, 15(12), 9404; https://doi.org/10.3390/su15129404 - 12 Jun 2023
Cited by 2 | Viewed by 1167
Abstract
This study explores the non-linear relationship between air pollution, socio-economic factors, labor insurance, and labor productivity in the industrial sector in Taiwan. Using machine learning, specifically multivariate adaptive regression splines (MARS), provides an alternative approach to examining the impact of air pollution on [...] Read more.
This study explores the non-linear relationship between air pollution, socio-economic factors, labor insurance, and labor productivity in the industrial sector in Taiwan. Using machine learning, specifically multivariate adaptive regression splines (MARS), provides an alternative approach to examining the impact of air pollution on labor productivity, apart from the traditional linear relationships and parametric methods employed in previous studies. Examining this topic is imperative for advancing the knowledge on the effects of air pollution on labor productivity and its association with labor insurance, employing a machine learning framework. The results reveal that air pollution, particularly PM10, has a negative impact on labor productivity. Lowering the PM10 level below 36.2 μg/m3 leads to an increase in marginal labor productivity. Additionally, the study identifies labor insurance as a significant factor in improving productivity, with a 9% increase in the total number of labor insurance holders resulting in a substantial 42.9% increase in productivity. Notably, a link between air pollution and insurance is observed, indicating that lower air pollution levels tend to be associated with higher labor insurance coverage. This research holds valuable implications for policymakers, businesses, and industries as it offers insights into improving labor productivity and promoting sustainable economic development. Full article
(This article belongs to the Special Issue Statistical Process Control in Sustainable Industries)
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19 pages, 939 KiB  
Article
Adjustment of Measurement Error Effects on Dispersion Control Chart with Distribution-Free Quality Variable
by Su-Fen Yang, Li-Pang Chen and Cheng-Kuan Lin
Sustainability 2023, 15(5), 4337; https://doi.org/10.3390/su15054337 - 28 Feb 2023
Cited by 2 | Viewed by 1179
Abstract
In industrial processes, control charts are useful tools to monitor the quality of products and detect possibly out-of-control processes. While many types of control charts have been available for data analysts, they were developed by assuming that the variables are precisely measured. In [...] Read more.
In industrial processes, control charts are useful tools to monitor the quality of products and detect possibly out-of-control processes. While many types of control charts have been available for data analysts, they were developed by assuming that the variables are precisely measured. In applications, however, measurement error is ubiquitous when data are falsely recorded by investigators or imprecisely collected by unadjusted machines. Even though the impacts of measurement error for different types of control charts have been explored, error-corrected control charts are still unavailable. In this study, we propose a new dispersion control chart with error correction to fill out this research gap. Our key idea is to convert the observed distribution-free process variables into a flexible sign statistic, and then adopt a function to adjust the measurement error effects on the sign statistic. Finally, we develop an exponentially weight-moving average dispersion control chart with measurement error correction based on the corrected sign statistic. The proposed error-corrected dispersion control chart not only eliminates measurement error effects, but also provides more reliable control limits for monitoring process dispersion. Throughout the numerical examination, we find that the proposed error-corrected dispersion control chart is effective in handling moderate and large levels of measurement error and shows good out-of-control detection performance. Finally, the proposed error-corrected dispersion control chart is implemented in the semiconductor data. Full article
(This article belongs to the Special Issue Statistical Process Control in Sustainable Industries)
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17 pages, 3488 KiB  
Article
Mixed Exponentially Weighted Moving Average—Moving Average Control Chart with Application to Combined Cycle Power Plant
by Muhammad Ali Raza, Komal Iqbal, Muhammad Aslam, Tahir Nawaz, Sajjad Haider Bhatti and Gideon Mensah Engmann
Sustainability 2023, 15(4), 3239; https://doi.org/10.3390/su15043239 - 10 Feb 2023
Cited by 3 | Viewed by 1624
Abstract
Statistical process control (SPC) consists of various tools for effective monitoring of the production processes and services to ensure their stable and satisfactory performance. A control chart is an important tool of SPC for detecting the process shifts that may undermine the quality [...] Read more.
Statistical process control (SPC) consists of various tools for effective monitoring of the production processes and services to ensure their stable and satisfactory performance. A control chart is an important tool of SPC for detecting the process shifts that may undermine the quality of the products or services. In the literature, a mixed exponentially weighted moving average–moving average (EWMA–MA) control chart for monitoring the process location is proposed to enhance the overall shift detection ability of the EWMA control chart. It is noted that the moving averages terms were considered as independent irrespective of their order. Consequently, the covariance terms are ignored while deriving the variance expression of the monitoring statistic. However, the successive moving averages of span w might not be independent since each term includes w − 1 preceding samples’ information. In this study, the variance expression of the mixed EWMA-MA charting statistic is derived by considering the dependency among the sequential moving averages. The control limits of the mixed EWMA-MA control chart are revised and the run-length profile is studied by using Monte Carlo simulations. The performance of the mixed EWMA-MA chart is compared with the existing counterparts and its robustness under various process distributions is studied. In the end, a real-life example is provided to demonstrate its application by using the data from a combined cycle power plant. Full article
(This article belongs to the Special Issue Statistical Process Control in Sustainable Industries)
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16 pages, 297 KiB  
Article
The Performance of S Control Charts for the Lognormal Distribution with Estimated Parameters
by Wei-Heng Huang
Sustainability 2022, 14(24), 16582; https://doi.org/10.3390/su142416582 - 10 Dec 2022
Viewed by 1060
Abstract
Control charts, one of the powerful tools in statistical process control (SPC), are widely used to monitor and detect out-of-control processes in the manufacturing industry. Many researchers have pointed out the effects of using estimated parameters on the average run length (ARL) performance [...] Read more.
Control charts, one of the powerful tools in statistical process control (SPC), are widely used to monitor and detect out-of-control processes in the manufacturing industry. Many researchers have pointed out the effects of using estimated parameters on the average run length (ARL) performance metric. Most of the previous literature has studied the expected value of the average run length (AARL) and the standard deviation of the average run length (SDARL) to evaluate the performance of control charts. In this article, we study the performance of three S control charts, the Shewhart S-chart, the median absolute deviation (MAD) control chart, and the lognormal S control chart, for a lognormal distribution in terms of the AARL and SDARL. Simulation results indicate the sample size to reach the specified in-control ARL value is very large. The lognormal S control chart has a smaller SDARL value than the other two S-charts. Full article
(This article belongs to the Special Issue Statistical Process Control in Sustainable Industries)
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