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Axioms
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Communications in Industrial Statistics—Theory and Methods
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Communications in Industrial Statistics—Theory and Methods

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 7784

Special Issue Editors

Prof. Dr. Bi-Min Hsu

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, Cheng Shiu University, Kaohsiung City 83347, Taiwan
Interests: engineering and management
Dr. To-Cheng Wang

E-Mail Website
Guest Editor
Department of Aviation Management, Republic of China Air Force Academy, Kaohsiung City 83347, Taiwan
Interests: quality and reliability engineering

Special Issue Information

Dear Colleagues,

Industrial Statistics is an essential domain in the scientific world, having broad applications in many different industries for enhancing research and development, process design, product design, design verification test, process monitoring, production verification test, performance evaluation, and continuous improvement.

The objective of the Special Issue is to present recent developments in industrial statistics that are relevant to successfully address the new challenges in technological and engineering issues. The focus will be on the interface between statistics and engineering with high-quality, innovative methodologies to improve industrial engineering and management, statistical engineering, or forceful applications of existing methods. Submitted papers should not have been published nor be under consideration for publication elsewhere currently. Potential topics include but are not limited to design of experiments; reliability engineering; accelerated life testing; quality engineering; statistical process control; six sigma; process capability analysis; acceptance sampling plans; management science; quality management; quality evaluation; performance evaluation; and applications in production, manufacturing, and logistics.

Prof. Dr. Bi-Min Hsu
Dr. To-Cheng Wang
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. Axioms is an international peer-reviewed open access monthly 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.

Keywords

  • design of experiments
  • response surface methodology
  • reliability engineering
  • accelerated life testing
  • quality engineering
  • statistical process control
  • six sigma
  • process capability analysis
  • acceptance sampling plans
  • management science
  • quality management
  • quality evaluation
  • performance evaluation

Published Papers (3 papers)

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Research

20 pages, 2366 KiB  
Article
Measuring Profitable Efficiency, Technical Efficiency, Technological Innovation of Waste Management Companies Using Negative Super-SBM–Malmquist Model
by Chia-Nan Wang, Quynh-Ngoc Hoang, Thi-Kim-Lien Nguyen, Hsien-Pin Hsu and Thanh-Tuan Dang
Axioms 2022, 11(7), 315; https://0-doi-org.brum.beds.ac.uk/10.3390/axioms11070315 - 28 Jun 2022
Cited by 1 | Viewed by 2531
Abstract
In recent decades, waste generation has increased gradually because of the development of the quantity and size of businesses, together with the high growth rate of the population. However, in 2019, like other industries, the waste management industry was affected by the COVID-19 [...] Read more.
In recent decades, waste generation has increased gradually because of the development of the quantity and size of businesses, together with the high growth rate of the population. However, in 2019, like other industries, the waste management industry was affected by the COVID-19 pandemic, particularly in relation to aspects concentrated on strategy. Subsequently, appropriate waste management in all aspects of the community, specifically for waste management enterprises, was demanded. This research aims to assess the profitable efficiency, position, technical, and technological innovation and compare the global major waste management corporations by integrating the negative super-slacks-based measure model and the negative Malmquist model in data envelopment analysis. Various inputs and outputs are initially selected from nine waste management companies’ financial statements from 2017 to 2020, including negative values, to attain their performance. The empirical results indicated that waste management companies’ managers could make better investment or strategy decisions for superior performance. At the same time, collaborators from other sectors could find their potential partners in the waste management industry. In general, considering the efficiency, Veolia Environment (DMU3) and Heritage-Crystal Clean Company (DMU8) were the most efficient companies. Meanwhile, Covanta Holding (DMU2) and Republic Services Corporation (DMU5) required additional development to improve their performance. Besides, because of the disparity in technical and technological innovation, most decision-making units could not achieve consistent improvement in terms of technical, technological change, and total production. Full article
(This article belongs to the Special Issue Communications in Industrial Statistics—Theory and Methods)
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14 pages, 1252 KiB  
Article
Evaluation of Digital Marketing Technologies with Fuzzy Linguistic MCDM Methods
by Ngo Quang Trung and Nguyen Van Thanh
Axioms 2022, 11(5), 230; https://0-doi-org.brum.beds.ac.uk/10.3390/axioms11050230 - 13 May 2022
Cited by 9 | Viewed by 2666
Abstract
Technology is becoming the tool that changes how people live every day, and the marketing strategies of businesses are also gradually shifting to the industry 4.0 mindset of constant growth and development. Digital marketing has changed human habits of information accessibility, determined their [...] Read more.
Technology is becoming the tool that changes how people live every day, and the marketing strategies of businesses are also gradually shifting to the industry 4.0 mindset of constant growth and development. Digital marketing has changed human habits of information accessibility, determined their interactions, and witnessed the birth of a variety of new marketing technologies. Marketers are creating digital marketing products and services that enhance the experience for consumers, products, and services that are also delivered through high digital marketing networks. As a result, data sources become more abundant and allow consumers to have more choices. All products, services, technologies, and data are increasingly meeting the needs of consumers, thereby confirming the effectiveness of digital marketing in today’s market. However, the evaluation and selection of digital marketing technology is very complex since it has many conflicting criteria and goals. The multi-criteria decision-making model (MCDM) is a powerful technique widely used for solving this type of problem. Thus, the author proposed a fuzzy linguistic MCDM method for evaluation of digital marketing technologies. After determining the evaluation criteria and alternatives, two MCDM methods, including Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), are used in the evaluation and selection of digital marketing technologies procedure. Finally, an application is present to demonstrate the potential use of the proposed methodology. The main contribution of this study is to propose a Spherical fuzzy MCDM model to support planners and decision makers in the digital marketing technology evaluation and selection processes. A case study is also performed to showcase the feasibility of the proposed approach. Full article
(This article belongs to the Special Issue Communications in Industrial Statistics—Theory and Methods)
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25 pages, 2132 KiB  
Article
On the Generalized Bilal Distribution: Some Properties and Estimation under Ranked Set Sampling
by Zuber Akhter, Ehab M. Almetwally and Christophe Chesneau
Axioms 2022, 11(4), 173; https://0-doi-org.brum.beds.ac.uk/10.3390/axioms11040173 - 13 Apr 2022
Cited by 6 | Viewed by 1719
Abstract
The generalized Bilal (GB) distribution can be defined as the distribution of the median of three independent random variables drawn from the Weibull distribution. Its failure rate function can be monotonic (decreasing or increasing) or upside-down bathtub-shaped. In this study, we aim to [...] Read more.
The generalized Bilal (GB) distribution can be defined as the distribution of the median of three independent random variables drawn from the Weibull distribution. Its failure rate function can be monotonic (decreasing or increasing) or upside-down bathtub-shaped. In this study, we aim to reveal some important properties of the GB distribution that have not been considered before. The findings are both theoretical and practical. From the theoretical viewpoint, we present explicit expressions for both single and product moments of order statistics from the GB distribution. The L-moments are derived as well. From the practical viewpoint, the parameter estimations are accomplished using the maximum likelihood (ML) method, which is based on two different sampling schemes: simple random sampling (SRS) and ranked set sampling (RSS) schemes. Furthermore, the asymptotic confidence intervals for the SRS and RSS estimators are discussed. For the sake of comparison and illustration, a simulation study and a real data example are presented. Concluding remarks are given at the end. Full article
(This article belongs to the Special Issue Communications in Industrial Statistics—Theory and Methods)
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