Quality Management In Industry Research Articles

Driving sustainability in industry 5.0 through sociotechnical approach of quality management

Industry 4.0 is primarily driven by technology, while Industry 5.0 (I5.0) places more emphasis on the interaction between human (socio) and technology (technical) aspects in shaping sustainability. Although I5.0 represents a burgeoning area of research, however, there remains a paucity of research on the sociotechnical perspective of quality management (QM) and sustainability in I5.0. This study intends to investigate the direct and indirect effects of socio-technical components of QM on sustainable performance through agile manufacturing, as well as the moderating role of firm size on these relationships. Data is gathered from 298 small and medium enterprises (SMEs) through a survey approach employing proportionate stratified sampling techniques. The findings support the socio-technical view of QM in Industry 5.0. Unexpectedly, employee education and training has no significant role in enhancing technical QM practices. Furthermore, QM and agile manufacturing are crucial to achieving sustainable performance in manufacturing SMEs. Finally, firm size has played a significant role as a moderating variable, particularly in I5.0. Based on the findings, the study offers novel theoretical contributions and practical implications by shedding light on the nuanced role of QM and agile manufacturing on sustainability within Malaysian manufacturing SMEs.

Relationship between quality management and organizational performance in the healthcare industry

ABSTRACT This study investigates how Quality Management (QM) programmes, particularly the Baldrige excellence framework, provide an approach to enhance healthcare organisational performance. Two independent methods were used to examine the short-term and long-term effects of winning the Baldrige award and the specific influence of common QM practices on results. The first method assesses the Baldrige Award’s impact on hospital performance by examining patient survey data and shows that initial quality improvements are not sustained in the long term. The second method uses confirmatory semantic analysis (CSA), a text-mining method, to analyse 22 Baldrige award-winning applications to determine the relationship between QM practices and organisational results in healthcare settings. The results show that only three QM practices – measurement, analysis, and knowledge management (MAKM), leadership, and operations focus – significantly contribute to organisational performance. Furthermore, CSA findings highlight the dominant role of leadership in driving performance outcomes, with other factors in the framework showing nonsignificant mediation effects. By exploring both the overarching effectiveness of QM programmes across different time lengths and specific practices that contribute to performance, this research provides academic insights with practical application in the healthcare industry. Importantly, the findings highlight the nuanced relationship between QM initiatives and organisational excellence.

The Quality 4.0 Roadmap: Designing a capability roadmap toward quality management in Industry 4.0

Literature shows that the path toward Quality 4.0 is unclear and that there is no roadmap to assist organizations in preparing or navigating this transition. This article aims to understand such a path, design, and develop a roadmap to guide organizations. The “Quality 4.0 Roadmap” allows organizations to position themselves and smoothly transition to Quality 4.0. It consolidates fundamental Quality Management practices to promote sustained progress toward stages of advanced technological integration. In each stage, the “Quality 4.0 Roadmap” highlights the necessary capabilities organizations need to develop and alerts them of potential pitfalls. This roadmap offers an original tool to assist organizations in achieving and developing the required capabilities for pursuing the digital transition with an operational and human-centered perspective. It also defines vital stages in the Quality 4.0 transition, identifying the evolving requirements and marking boundaries and touchpoints between Technology and Quality management.

Aquaculture 4.0: hybrid neural network multivariate water quality parameters forecasting model

This study examined the efficiency of hybrid deep neural network and multivariate water quality forecasting model in aquaculture ecosystem. Accurate forecasting of critical water quality parameters can allow for timely identification of possible problem areas and enable decision-makers to take pre-emptive remedial actions that can significantly improve water quality management in aquaculture industry. A novel hybrid deep learning neural network multivariate water quality parameters forecasting model is developed with the aid of ensemble empirical mode decomposition (EEMD) method, deep learning long-short term memory (LSTM) neural network (NN), and multivariate linear regression (MLR) method. The presented water quality forecasting model (shortened as EEMD–MLR–LSTM NN model) is developed using multivariate time-series water quality sensor data collected from Loch Duart company, a Salmon offshore aquaculture farm based around Scourie, northwest Scotland. The performance of the novel hybrid water quality forecasting model is validated by comparing the forecast result with measured water quality parameters data and the real Phytoplankton data count from the aquaculture farm. The forecast accuracy of the results suggests that the novel hybrid water quality forecasting model can be used as a valuable support tool for water quality management in aquaculture industries.

Assessing The Benefit of Adopting TQM Philosophy and Green Supply Chain Management Towards Sustainable Operations: An Evidence from Textile Industries of Pakistan

This research paper delves into the examination of the advantages associated with the implementation of Total Quality Management (TQM) and Green Supply Chain Management (GSCM) practices in Pakistan's textile industry to foster sustainable operations. The study discovers that the adoption of TQM and GSCM practices yields positive outcomes for sustainable operations, encompassing improvements in product quality, operational efficiency, and environmental preservation. Nonetheless, certain barriers impede the implementation process, including a lack of awareness, limited resources, and resistance to change. The study puts forth several recommendations tailored to the textile industry, encompassing leadership commitment, employee training initiatives, supplier collaboration, and the integration of sustainability principles into the organizational culture. By shedding light on the benefits of TQM and GSCM practices, the research contributes to existing knowledge by providing empirical evidence that supports the notion of leveraging these practices in the textile industry. Such adoption leads to competitive advantages, cost savings, and the cultivation of environmental responsibility within the industry.

The benefits of integrating of halal requirements with quality management and food safety systems in the food industry

Quality management and ensuring of food safety systems are crucial in today's world. High levels of quality are demanded by consumers and are essential for a company's success. The Global Food Safety Initiative (GFSI) aims to improve food safety by integrating supported and recognized standards throughout the supply chain. To meet these goals, the MiP Ljubače company, engaged in flour, bakery, pastry, and animal feed production, is enhancing its existing management system. The company already holds ISO 9001, HACCP, and NON GMO certificates and is now integrating HALAL BAS 1049:2010 requirements to meet the demands of key customers, related parties, and target consumer groups. The integration of halal requirements allows the company to validate its list of approved suppliers/producers and final halal products. This integration creates opportunities to expand the consumer base, increase market share in the domestic and EU markets, and enhance the safety system in production processes. By producing Halal certified products, the company contributes to regional self-sufficiency, reduces dependency on other markets, and promotes halal standards in the region. These improvements will elevate the company's position, attract more consumers, and bolster its presence in the market.

THE USAGE OF TOTAL QUALITY MANAGEMENT (TQM) IN INDUSTRY 4.0 CONDITIONS

THE USAGE OF TOTAL QUALITY MANAGEMENT (TQM) IN INDUSTRY 4.0 CONDITIONS

Data-driven multi-objective optimization with neural network-based sensitivity analysis for semiconductor devices

Statistical modeling and optimization are critical for product quality management and technology development in the manufacturing industry. As the emerging device appears and technology advances, it consists of highly-correlated multivariate parameters and has a non-linear relationship. Moreover, technology development requires extensive cost and effort, and often it takes several years to establish a physical model after the advent of new technology. Thus, this paper presents an effective data-driven multi-objective optimization with neural network-based sensitivity analysis for semiconductor devices before explicitly developing a physical or compact model. Sensitivity analysis allows a quick search for influential parameters and selects design parameter candidates for a customized solution. Then, the selected parameters are adjusted to maintain within the set range by the scaled sensitivities and to have robustness against noise. It optimizes design variables to meet performance and variation requirements simultaneously, considering correlation between parameters. Therefore, multi-objective solutions hold physical consistency and are found with ¡ 1.7% error. The proposed method is efficient in computational time, which is significantly reduced by more than 80% compared to Bayesian optimization for process optimization. This approach is a general methodology applicable to various industries besides the semiconductor field. Finally, we expect this study to help shorten development time and improve yield through customized multi-objective solutions.

Blockchain-Enabled Open Quality System for Smart Manufacturing: Applications and Challenges

In this technological development era, new trends and approaches related to quality management are emerging. Although many studies have focused on quality management in Industry 4.0, new technologies face difficulties in resolving problems arising from lack of trust, transparency, integrity, track-and-traceability, connectivity, and responsibility in evaluating quality. In this article, we discuss the potential of blockchain to deliver business value with transparent quality by introducing a new quality concept called “open quality”. A systematic literature review presents the increased visibility of open quality across the manufacturing system, based on which a “blockchain-enabled open quality” (OQB) framework in the fourth industrial revolution is proposed. The research contributes by providing a platform to share manufacturing information regarding quality within the entire system, thus ensuring that all members and processes of the system obtain verified information to enhance trust, transparency, and track-and-traceability. By this, manufacturers are able to meet the expectations of the customer and stakeholder with an improved product and system quality. In addition, the potential threats associated with OQB system implementation and their possible solutions are also discussed. Moreover, research gaps that can be explored in future study and the opportunities for new concepts of quality in Industry 4.0 are presented.

Experiences with the Journal of Chemical & Engineering Data

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTExperiences with the Journal of Chemical & Engineering DataJiangtao WuJiangtao WuMore by Jiangtao Wuhttps://orcid.org/0000-0003-1123-4307Cite this: J. Chem. Eng. Data 2022, 67, 9, 2015–2016Publication Date (Web):September 8, 2022Publication History Received5 August 2022Published online8 September 2022Published inissue 8 September 2022https://doi.org/10.1021/acs.jced.2c00520Copyright © Published 2022 by American Chemical SocietyRIGHTS & PERMISSIONSArticle Views197Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (1 MB) Get e-AlertsSUBJECTS:Chemical engineering and industrial chemistry,Fluids,Quality management,Theoretical and computational chemistry,Thermodynamics Get e-Alerts

The HyDAPI framework: a versatile tool integrating Lean Six Sigma and digitalisation for improved quality management in Industry 4.0

PurposeQuality management practitioners have yet to cease the potential of digitalisation. Furthermore, there is a lack of tools such as frameworks guiding practitioners in the digital transformation of their organisations. The purpose of this study is to provide a framework to guide quality practitioners with the implementation of digitalisation in their existing practices.Design/methodology/approachA review of literature assessed how quality management and digitalisation have been integrated. Findings from the literature review highlighted the success of the integration of Lean manufacturing with digitalisation. A comprehensive list of Lean Six Sigma tools were then reviewed in terms of their effectiveness and relevance for the hybrid digitisation approach to process improvement (HyDAPI) framework.FindingsThe implementation of the proposed HyDAPI framework in an industrial case study led to increased efficiency, reduction of waste, standardised work, mistake proofing and the ability to root cause non-conformance products.Research limitations/implicationsThe activities and tools in the HyDAPI framework are not inclusive of all techniques from Lean Six Sigma.Practical implicationsThe HyDAPI framework is a flexible guide for quality practitioners to digitalise key information from manufacturing processes. The framework allows organisations to select the appropriate tools as needed. This is required because of the varying and complex nature of organisation processes and the challenge of adapting to the continually evolving Industry 4.0.Originality/valueThis research proposes the HyDAPI framework as a flexible and adaptable approach for quality management practitioners to implement digitalisation. This was developed because of the gap in research regarding the lack of procedures guiding organisations in their digital transition to Industry 4.0.

Integrating Lean Six Sigma with blockchain technology for quality management – a scoping review of current trends and future prospects

PurposeThe purpose of this research is to study and analyze the literature that integrates Lean Six Sigma (LSS) approach with blockchain technology in different sectors for improved quality management.Design/methodology/approachThis study presents a scoping review on the application of integrated LSS and blockchain technology in the manufacturing and healthcare sector. Further, the authors examined existing blockchain-based solutions on a variety of dimensions, including application area, technical approach, methodology, application scenario, various blockchain platforms, purpose, and monitoring parameters. The authors study LSS approaches in detail, as well as the key benefits that blockchain technology can enable. Finally, the authors discuss significant research problems to be addressed in order to develop a highly efficient, resilient, and secure quality management framework using blockchain technology.FindingsIt has been observed that the adoption of blockchain technology for quality management and assurance is influenced by several factors such as transaction execution speed, throughput, latency. Also, prior blockchain-based solutions have neglected to leverage the benefits of LSS methodologies for effective quality management.Originality/valueThis is the first study to explores the influence of blockchain technology on quality management and assurance in manufacturing and healthcare industry. Furthermore, prior research has not examined how integrating the LSS methodology with blockchain technology can aid in the control of product quality management.

Automatic reconstruction of irregular shape defects in pulsed thermography using deep learning neural network

Quantitative defect and damage reconstruction play a critical role in industrial quality management. Accurate defect characterisation in Infrared Thermography (IRT), as one of the widely used Non-Destructive Testing (NDT) techniques, always demands adequate pre-knowledge which poses a challenge to automatic decision-making in maintenance. This paper presents an automatic and accurate defect profile reconstruction method, taking advantage of deep learning Neural Networks (NN). Initially, a fast Finite Element Modelling (FEM) simulation of IRT is introduced for defective specimen simulation. Mask Region-based Convolution NN (Mask-RCNN) is proposed to detect and segment the defect using a single thermal frame. A dataset with a single-type-shape defect is tested to validate the feasibility. Then, a dataset with three mixed shapes of defect is inspected to evaluate the method’s capability on the defect profile reconstruction, where an accuracy over 90% on Intersection over Union (IoU) is achieved. The results are compared with several state-of-the-art of post-processing methods in IRT to demonstrate the superiority at detailed defect corners and edges. This research lays solid evidence that AI deep learning algorithms can be utilised to provide accurate defect profile reconstruction in thermography NDT, which will contribute to the research community in material degradation analysis and structural health monitoring.

Quality management in industry 4.0 environment: a morphological analysis and research agenda

PurposeDigitalised and interconnected production systems in the era of fourth industrial revolution or Industry 4.0 (I4.0) have triggered the need of reshaping the traditional Quality Management (QM) models and roles of quality professionals. The purpose of this paper is to review the body of literature on QM in the I4.0 environment (also termed as “Quality 4.0”) and identify future research possibilities.Design/methodology/approachThe morphological analysis (MA) framework is used to identify research gaps in a systematic manner. We present the MA framework based on a literature review of 51 papers on the Australian Business Deans Council (ABDC) list identified using systematic review protocol.FindingsThe MA framework, built on six relevant dimensions, namely, production and operations function, scope of QM activities, enabling technologies, latent capabilities of digitalisation, performance metrics and QM principles, and 41 associated variants, reveals 367 research gaps as an upper bound of opportunities for future research.Originality/valueThis paper is possibly the first to use MA for reviewing the QM literature, presenting important contributions. The MA framework acts as a ready reckoner on the QM in I4.0 literature. It documents research gaps through the construction of a cross-consistency matrix.

Modelling Total Quality Management in the Malaysian Composite Manufacturing Industry

This paper investigates the enabler factors of total quality management (TQM) practices for a composite manufacturing company in Melaka, Malaysia. Leadership (L), training and education (TE), supplier management (SM), customer focus (CF), benchmarking (B), continuous improvement (CI), employee relations (ER), and strategic planning (SP) were considered in this paper. They are the critical TQM elements that influence TQM processes (Y). A questionnaire was used to collect data from 27 employees of a composite manufacturing company in Melaka, Malaysia and analyzed using multiple regression analysis. Five out of the eight hypotheses positively fit with the conceptual model and show a positive effect of TQM practices. The research findings indicate that enabler factors, namely leadership (L), continuous improvement (CI), supplier management (SM), benchmarking (B) and strategic planning (SP), have positive and significant effects on successful TQM practices. This study would benefit managers significantly to increase strategic planning, supply chain management, and benchmarking through TQM practices.

Quality management in a fractional horsepower electrical motor industry: a case study

Quality management in a fractional horsepower electrical motor industry: a case study

Benefits of Total Quality Management in Garments Industry: A Research Review

Benefits of Total Quality Management in Garments Industry: A Research Review

Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review.

The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology (ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed.

Application Analysis of Prefabricated Highrise Building Construction

This paper analyzes the application of prefabricated high-rise building construction. Based on the quality of prefabricated buildings, this paper studies the theory and practice of quality management in manufacturing and construction industry at home and abroad. Based on the background of "integration of two modernizations", this paper combs the relevant research results of intelligent information transmission and collaborative management, and makes an in-depth study on the quality problem and improvement mechanism of prefabricated buildings as the core problem. This paper analyzes the performance and causes of quality problems in the construction process of prefabricated buildings by using arrangement diagram method and 4M1E method. This paper puts forward the quality problem improvement mechanism from four aspects: organization, culture, process control, technical guarantee and information collaborative management.

Quality Management In Construction Industry By Using Building Information Modelling (BIM)

Building Information Modeling has the potential to help the construction sector change its design & construction processes. While BIM is thought to assist improve design designed to remove disputes and minimising rebuilding, little study has been done on its application in projects for construct quality control & data management. The promise of BIM implementation in quality management rests in its capacity to offer multi-dimensional data, combining design data and time sequences, due to the compatibility of project specifications using quality control processes and quality control processes. The advantages using 6D BIM regarding quality framework depends upon on architectural code are examined and discussed in this study.