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This research aimed at developing the Quality 4.0 transition framework for Tanzanian manufacturing industries.

The survey method was used in this study to gather practitioners' perspectives. The approach included open-ended and closed-ended structured questionnaires to assess respondents' perceptions of Quality 4.0 awareness and manufacturers' readiness to transit to Quality 4.0. The study's objective was to adopt non-probability and purposive sampling strategies. The study focused on fifteen Tanzanian manufacturing industries. The data were analysed qualitatively and quantitatively using MAXQADA 2020 and Minitab 20 software packages, respectively.

The study demonstrated a high level of awareness of Quality 4.0 among Tanzanian manufacturing industries (i.e. 100% in Quality 4.0 traditional attributes and 53% in Quality 4.0 modern attributes). Individuals acquire knowledge in various ways, including through quality training, work experience, self-reading and Internet surfing. The result also revealed that most manufacturing industries in Tanzania use Quality 3.0 or a lower approach to manage quality. However, Tanzanian manufacturing industries are ready to embrace Quality 4.0 since practitioners are aware of the concepts and could see benefits such as customer satisfaction, product improvement, process and continuous improvement, waste reduction and decision support when using the Quality 4.0 approach. The challenges hindering Quality 4.0 adoption in Tanzania include reliable electricity, high-speed Internet and infrastructure inadequacy to support the adoption, skilled workforces familiar with Quality 4.0-enabled technologies and a financial set-up to support technology investment. Moreover, the study developed a transition framework for an organisation to transition from traditional quality approaches such as quality control, quality assurance and total quality management to Quality 4.0, a modern quality approach aligned with the fourth industrial revolution era.

The current study solely looked at manufacturing industries, leaving other medical, service, mining and construction sectors. Furthermore, no focus was laid on the study's Quality 4.0 implementation frameworks.

This is probably the first Quality 4.0 transition framework for Tanzanian manufacturing industries, perhaps with other developing countries.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

The issue of quality assurance in vocational and technical education (VTE) programs identifies a perilous need to develop a conceptual framework for teaching and learning standards. This study aims to identify standard components for teaching and learning to ensure quality delivery for the achievement of VTE objectives. The quality assurance framework for teaching and learning is envisioned as a closed-loop management process that functions as an effective operational scheme to coordinate teaching processes that support the structure for quality assurance in VTE programs.

Survey data were collected through a questionnaire developed to examine the quality standards for teaching and learning in VTE from 259 respondents from colleges of education. The identified quality components for teaching and learning and their quality indicators were analysed.

The conceptual framework for teaching and learning with the 12 quality components and 62 quality indicators is the key standard for improving quality teaching and learning to meet the desired goals of VTE programs.

The framework proposed is flexible and can be applied to many other programs to ensure that teachers are professional enough to teach effectively to provide adequate learning outcomes.

The research aims to develop an assessment framework that evaluates critical success factors (CSFs) for the Quality 4.0 (Q 4.0) transition among Indian firms.

The authors use the fuzzy-Delphi method to validate the results of a systematic literature review (SLR) that explores critical aspects. Further, the fuzzy decision-making trial and laboratory (DEMATEL) method determines the cause-and-effect link. The findings indicate that developing a Q 4.0 framework is essential for the long-term success of manufacturing companies. Utilizing the power of digital technology, data analytics and automation, manufacturing companies can benefit from the Q 4.0 framework. Product quality, operational effectiveness and overall business performance may all be enhanced by implementing the Q 4.0 transition framework.

The study highlights significant awareness of Q 4.0 in the Indian manufacturing sector that is acquired through various means such as training, experience, learning and research. However, most manufacturing industries in India still follow older quality paradigms. On the other hand, Indian manufacturing industries seem well-equipped to adopt Q 4.0, given practitioners' firm grasp of its concepts and anticipated benefits, including improved customer satisfaction, product refinement, continuous process enhancement, waste reduction and informed decision-making. Adoption hurdles involve challenges including reliable electricity access, high-speed Internet, infrastructure, a skilled workforce and financial support. The study also introduces a transition framework facilitating the shift from conventional methods to Q 4.0, aligned with the principles of the Fourth Industrial Revolution (IR).

This research exclusively examines the manufacturing sector, neglecting other fields such as medical, service, mining and construction. Additionally, there needs to be more emphasis on the Q 4.0 implementation frameworks within the scope of the study.

This may be the inaugural framework for transitioning to Q 4.0 in India's manufacturing sectors and, conceivably, other developing nations.

The research explores the shift to Quality 4.0, examining the move towards a data-focussed transformation within organizational frameworks. This transition is characterized by incorporating Industry 4.0 technological innovations into existing quality management frameworks, signifying a significant evolution in quality control systems. Despite the evident advantages, the practical deployment in the Indian manufacturing sector encounters various obstacles. This research is dedicated to a thorough examination of these impediments. It is structured around a set of pivotal research questions: First, it seeks to identify the key barriers that impede the adoption of Quality 4.0. Second, it aims to elucidate these barriers' interrelations and mutual dependencies. Thirdly, the research prioritizes these barriers in terms of their significance to the adoption process. Finally, it contemplates the ramifications of these priorities for the strategic advancement of manufacturing practices and the development of informed policies. By answering these questions, the research provides a detailed understanding of the challenges faced. It offers actionable insights for practitioners and policymakers implementing Quality 4.0 in the Indian manufacturing sector.

Employing Interpretive Structural Modelling and Matrix Impact of Cross Multiplication Applied to Classification, the authors probe the interdependencies amongst fourteen identified barriers inhibiting Quality 4.0 adoption. These barriers were categorized according to their driving power and dependence, providing a richer understanding of the dynamic obstacles within the Technology–Organization–Environment (TOE) framework.

The study results highlight the lack of Quality 4.0 standards and Big Data Analytics (BDA) tools as fundamental obstacles to integrating Quality 4.0 within the Indian manufacturing sector. Additionally, the study results contravene dominant academic narratives, suggesting that the cumulative impact of organizational barriers is marginal, contrary to theoretical postulations emphasizing their central significance in Quality 4.0 assimilation.

This research provides concrete strategies, such as developing a collaborative platform for sharing best practices in Quality 4.0 standards, which fosters a synergistic relationship between organizations and policymakers, for instance, by creating a joint task force, comprised of industry leaders and regulatory bodies, dedicated to formulating and disseminating comprehensive guidelines for Quality 4.0 adoption. This initiative could lead to establishing industry-wide standards, benefiting from the pooled expertise of diverse stakeholders. Additionally, the study underscores the necessity for robust, standardized Big Data Analytics tools specifically designed to meet the Quality 4.0 criteria, which can be developed through public-private partnerships. These tools would facilitate the seamless integration of Quality 4.0 processes, demonstrating a direct route for overcoming the barriers of inadequate standards.

This research delineates specific obstacles to Quality 4.0 adoption by applying the TOE framework, detailing how these barriers interact with and influence each other, particularly highlighting the previously overlooked environmental factors. The analysis reveals a critical interdependence between “lack of standards for Quality 4.0” and “lack of standardized BDA tools and solutions,” providing nuanced insights into their conjoined effect on stalling progress in this field. Moreover, the study contributes to the theoretical body of knowledge by mapping out these novel impediments, offering a more comprehensive understanding of the challenges faced in adopting Quality 4.0.

Existing conceptual, empirical and case studies evidence suggests that manufacturing industries find the joint implementation of Kaizen philosophy initiatives. However, the existing practices rarely demonstrated in a single framework and implementation procedure in a structure nature. This paper, therefore, aims to develop, validate and practically test a framework and implementation procedure for the implementation of integrated Kaizen in manufacturing industries to attain long-term improvement of operational, innovation, business (financial and marketing) processes, performance and competitiveness.

The study primarily described the problem, extensively reviewed the current state-of-the-art literature and then identified a gap. Based on it, generic and comprehensive integrated framework and implementation procedure is developed. Besides, the study used managers, consultants and academics from various fields to validate a framework and implementation procedure for addressing business concerns. In this case, the primary data was collected through self-administered questionnaire, and 244 valid questionnaires were received and were analyzed. Furthermore, the research verified the practicability of the framework by empirically exploring the current scenario of selected manufacturing companies.

The research discovered innovative framework and six-phase implementation procedure to fill the existing conceptual gap. Furthermore, the survey-based and exploratory empirical analysis of the research demonstrated that the practice of the proposed framework based on structured procedure is valued and companies attain the middling improvements of productivity, delivery time, quality, 5S practice, waste and accident rate by 61.03, 44, 52.53, 95.19, 80.12, and 70.55% respectively. Additionally, the companies saved a total of 14933446 ETH Birr and 5,658 M² free spaces. Even though, the practices and improvements vary from company to company, and even companies unable to practice some of the unique techniques of the identified CI initiatives considered in the proposed framework.

All data collected in the survey came from professionals working for Ethiopian manufacturing companies, universities and government. It is important to highlight that n = 244 is high sample size, which is adequate for a preliminary survey but reinforcing still needs further survey in terms of generalization of the results since there are hundreds of manufacturing companies, consultants and academicians implementing and consulting Kaizen. Therefore, a further study on a wider Ethiopian manufacturing companies, consultants and academic scale would be informative.

This work is very important for Kaizen professionals in the manufacturing industry, academic and government but in particular for senior management and leadership teams. Aside from the main findings on framework development, there is some strong evidence that practice of Kaizen resulted in achieving quantitative (monetary and non-monetary) and qualitative results. Thus, senior management teams should use this research out to practice and analyze the effect of Kaizen on their own organizations. Within the academic community, this study is one of the first focusing on development, validating and practically testing and should aid further study, research and understanding of Kaizen in manufacturing industries.

So far, it is rare to find preceding studies proposed, validated and practically test an integrated Kaizen framework with the context of manufacturing industries. Thus, authors understand that this is the very first research focused on the development of the framework for manufacturing industries continuously to be competitive and could help managers, institutions, practitioners and academicians in Kaizen practice.

Most of the emphasis in the green building literature on the green performance of buildings has been on optimizing energy and resource efficiency. Admittedly, from the perspective of the sustainable construction industry, making optimally energy and resource efficiency, often seen as a technical challenge, has a premise role in green building projects. However, green buildings need to optimally meet the health, well-being and comfort requirements of their occupants and their environmental quality targets. In that context, perceived quality is a crucial determinant of occupant satisfaction and can play a critical role in the user-oriented improvement of the green performance of buildings. While previous research has highlighted issues related to occupant satisfaction, none of them examines green buildings from a perceived quality perspective. Therefore, the study attempts to fill this research gap.

Firstly, to reveal the positive and negative satisfaction of the building occupants according to the green building features, the review of previous research in the related literature is supplemented by an exploratory study of case studies evaluating occupant satisfaction in green buildings. Then, a conceptual framework is proposed to link perceived quality and green building features towards occupant satisfaction.

A review of the case studies in 49 research articles has shown deficiencies in a comprehensive understanding and approach to the perceived quality of green buildings. In response, the development of a framework for conceptual interrelationships may provide a pathway for more detailed quality assessments for future research. In this study, the proposed conceptual framework has the potential to provide a conceptual basis for future models in determining the relationship between quality expectations and quality experiences in green buildings. It can also serve as a constructive approach for assessing occupant satisfaction in the quality-driven improvements of green buildings and further investigation of the importance of various quality cues, quality attributes and their interactions.

This study aims to incorporate green building features and perceived quality concepts into a framework that can form the basis for assessing occupant satisfaction in green buildings. The ultimate goal of the proposed conceptual framework is to generate an insight that can contribute to rethinking the perceived quality of green buildings and developing more occupant-driven solutions for future green buildings.

Industry 4.0-driven digitalisation is said to offer a way to redesign traditional compliance-oriented quality management (QM) models. However, despite a rising academic and practitioner interest, it is still unclear how companies transform their current QM models to meet the real-time needs of the new manufacturing paradigm. The purpose of this study is to explore practices for the digitalisation of QM and to uncover the digitalisation journey.

An exploratory research approach of an embedded case study of a multinational auto-component manufacturer was adopted to achieve the research aim.

A guiding framework called the “Quality 4.0 transition framework” was developed based on literature and expert knowledge. The framework is made up of three building blocks, i.e. the foundation of “as-is” digitalisation maturity assessment; pillars representing horizontally and vertically integrated QM processes, and roof signifying reinforcement of total quality management (TQM) principles at all levels.

The study provides empirical evidence of the case company's digitalisation journey to avert product recall due to field failure issues. The study contributes to theory and practice in many ways. First, the study uses empirical data from a real-world case to understand how digitalisation affects QM processes. Next, the guiding framework for the Quality 4.0 transition adds to the existing literature on the digitalisation of business processes.

In addressing the housing deficits for the less privileged citizens, the South African government began constructing social housing after coming to power in 1994. However, the construction of these houses is bedevilled with many issues; prominent among them are poor quality of the constructed houses. This study seeks to develop a quality management framework for achieving quality and efficiency in public-sector housing construction, a hallmark of the country's procurement goals.

Telephone interviews were conducted with construction professionals involved in constructing government social houses across South Africa, chosen randomly. The data gathered were analysed using the content analysis method.

The study found that the most significant cause of poor quality government-constructed social housing is multifaceted, categorised into project management-related, procurement-related, contractor-related, corruption-related and political-related.

Failure to develop and implement a quality management framework on government-constructed social housing leads to poor quality social housing.

The study has identified quality-related issues and has developed a Quality Management (QM) framework for the stakeholders involved in the construction of the houses to guide them in the project implementation process to ensure project success and quality standards.

This study aims to develop and implement an initial framework for assessing progress in lean implementation within an higher education institution (HEI). It includes developing preliminary findings regarding the impacts of lean implementation in the HEI case organization and comparing outcomes from this assessment to outcomes from other published sources.

Existing literature on lean, particularly in an HEI context, was used to develop a preliminary lean assessment framework for HEIs. Quality and continuous improvement literature were also compared to the proposed assessment framework to further validate the approach. This assessment framework was then utilized to evaluate lean implementation at a large public university (LPU) in the U.S.

The paper presents the framework as well as the major findings from the Large Public University (LPU)’s assessment. The assessment findings are further compared to other HEI quality measures and lean assessments done in other industries. Overall, the findings suggest that the assessment framework provides valuable insight to HEI organizations implementing lean.

The research intends to support lean assessment standardization efforts by proposing a preliminary lean assessment framework for the HEI, grounded in research trends, research findings, identified gaps in the research, and case study outcomes. To the research team's knowledge, this is the first lean assessment framework proposed for HEIs and also contributes to research gaps related to service industry frameworks and those containing both practices and outcomes. The framework can be used by other researchers as a foundation for additional conceptual and empirical developments on the topic and by researchers and practitioners seeking to understand and assess lean implementation progress in the HEI.