Quality and Process Management in the New Industrial Era: Standards, Theories, Methodologies, Maturity Assessment Frameworks and Excellence Awards

A special issue of Standards (ISSN 2305-6703).

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 2182

Special Issue Editor


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Guest Editor
Department of Financial Engineering & Management, University of the Aegean, 81100 Mytilene, Greece
Interests: quality management; operations management; performance measurement; process mining; fuzzy cognitive maps; knowledge management; corporate social responsibility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim is of this Special Issue is to attract and present outstanding research outcomes and outputs that represent the state of the art in Quality and Process Management (QPM) implementation in the new industrial era. This new industrial era has emerged with the development of initiatives such as Industry 4.0, Quality 4.0, and Human Resources 4.0, etc.

Of special interest are research outcomes related to quality standards, quality methodologies, excellence awards and theories such as total quality management implementation and process mining.

We seek approaches in which QPM implementation is achieved via continuous assessment of current implementation status and evaluation of gaps and discrepancies between current and desired QPM implementation states. Novel proposed approaches or case studies could be applied in a variety of industrial sectors and be related to the implementation of quality standards, maturity assessment frameworks, process models, software tools and technologies.

An indicative, and not limited list of topics include:

  • The role of QPM in Industry 4.0
  • The role of QPM in Quality 4.0
  • The role of QPM in HRM 4.0
  • QPM Implementation Maturity Assessment Frameworks
  • QM Standards Implementation (ISO, BSI, etc.)
  • QM Methodologies Application (Six Sigma, etc.)
  • TQM Implementation
  • Excellence Awards (EFQM, Baldrige, etc.)
  • QPM Process-Organizational Modeling
  • Human Resources Management in QPM
  • QPM and Performance Measurement
  • Organizational Analysis in QPM
  • Strategic QPM
  • Knowledge Management in QPM
  • QPM and Continuous Improvement
  • Leadership and Top Management Commitment in QPM
  • QPM and Change Management,
  • Customer Relationship Management in QPM (SERVQUAL)
  • QPM Managerial Systems (Costing, Budgeting, Information and Business Intelligence, etc.)
  • Enterprise Resource Planning and QPMs
  • Information Systems and QPMs

Prof. Dr. Michael Glykas
Guest Editor

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Keywords

  • implementation maturity assessment
  • quality management standards
  • ISO standards
  • Six Sigma, EFQM
  • excellence awards
  • total quality management
  • process management
  • process mining
  • Industry 4.0
  • Quality 4.0

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Published Papers (1 paper)

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Research

15 pages, 1780 KiB  
Article
Composition of Probabilistic Preferences in Multicriteria Problems with Variables Measured in Likert Scales and Fitted by Empirical Distributions
by Luiz Octávio Gavião, Annibal Parracho Sant’Anna, Gilson Brito Alves Lima and Pauli Adriano de Almada Garcia
Standards 2023, 3(3), 268-282; https://doi.org/10.3390/standards3030020 - 17 Jul 2023
Cited by 1 | Viewed by 1670
Abstract
The aim of this article is to demonstrate the advantages of the Composition of Probabilistic Preferences method in multicriteria problems with data from Likert scales. Multicriteria decision aids require a database as a decision matrix, in which two or more alternatives are evaluated [...] Read more.
The aim of this article is to demonstrate the advantages of the Composition of Probabilistic Preferences method in multicriteria problems with data from Likert scales. Multicriteria decision aids require a database as a decision matrix, in which two or more alternatives are evaluated according to two or more variables selected as decision criteria. Several problems of this nature use measures by Likert scales. Depending on the method, parameters from these data (e.g., means, modes or medians) are required for calculations. This parameterization of data in ordinal scales has fueled controversy for decades between authors who favor mathematical/statistical rigor and argue against the procedure, stating that ordinal scales should not be parameterized, and scientists from other areas who have shown gains from the process that compensates for this relaxation. The Composition of Probabilistic Preferences can allay the protests raised and obtain more accurate results than descriptive statistics or parametric models can bring. The proposed algorithm in R-code involves the use of probabilities with empirical distributions and fitting histograms of data measured by Likert scales. Two case studies with simulated datasets having peculiar characteristics and a real case illustrate the advantages of the Composition of Probabilistic Preferences. Full article
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