Integration of continuous improvement strategies with Industry 4.0: a systematic review and agenda for further research

How The TQM Journal has addressed “quality”: a literature review using bibliometric analysis

Purpose

This study investigates the overall publications of The TQM Journal since its inception with an aim to identify the trending topics and emerging trends.

Design/methodology/approach

The quantitative bibliometric and social network analysis techniques composed of keywords, co-occurrence network and keyword cluster detection are employed to conduct the investigation. A total of 968 papers published in The TQM Journal till August 2022 were sourced from the SCOPUS database to conduct the analysis.

Findings

The research identifies five themes from the published articles namely, customer service experience and satisfaction; quality management and organizational performance; quality measurement tools and models; quality and sustainable development; and quality and competitive advantage. The study also identifies the most significant articles, authors and countries published in the journal and shows that Industry 4.0 is the trending topic and quality 4.0 the new emerging trend in the journal.

Research limitations/implications

The analysis is carried out only for papers published in The TQM Journal till August 2022; those after this month are not included in the analysis. The outcome of this study is dynamic in nature and subject to change over time as more papers, citations and collaborations are added to the list.

Originality/value

This is the first article of its kind to explore The TQM Journal publications with an aim to identify trending and emerging topics and also the most valuable authors based on the number of publications and citations through the bibliometric analysis.

Production Planning Using a Shared Resource Register Organized According to the Assumptions of Blockchain Technology

This article presents the architecture of integration of blockchain technology (BCT) and the Internet of Things with the planning of production processes. The authors proposed a shared concept of a distributed machine database based on BCT. As part of the work, a network of connections for the exchange of production resources was created using nodes communicating in a decentralized system, which at the same time serves as an integration of the virtual and real environment. Particular attention was focused on developing an algorithm for the efficient division of production tasks between all interested network users. BCT is used to conclude smart contracts and transactions and ensure the security of exchanged production data within shared ledgers. The proposed concept is a solution enabling a modern approach to the interdisciplinary management of production resources while maintaining the highest cybersecurity standards.

Modeling and Analysis of Industry 4.0 Adoption Challenges in the Manufacturing Industry

The manufacturing sector is a fast-growing sector demanded by the increasing population. The adoption of information technology is a boon in the manufacturing industry. The industrial transformation from the third generation to the fourth generation has significantly impacted sustainable development. On account of this, different sectors are adopting industry 4.0 technologies to smooth their process flows. The industry 4.0 technologies implementation in the manufacturing sector will not only enhance its productivity, but also lead to sustainable growth. In this regard, this study intended to examine the challenges associated with adopting industry 4.0 technologies in the manufacturing sector. A thorough literature review was carried out from the Scopus database, and a list of ten important challenges was shortlisted for analysis. The article uses interpretive structural modeling to analyse the challenges of industry 4.0 and make a structural model between identified challenges. “Lack of employee skills” and “lack of technological infrastructure” were identified as the topmost challenges in adopting industry 4.0 technologies in the manufacturing sector. This study will enable decision makers, policymakers, and industrial practitioners to effectively analyse the challenges of I4.0 for its smooth adoption in the manufacturing sector. Practical implications of the study and future research directions were also highlighted in the article.

Analysis of barriers for implementation of integrated Lean Six Sigma and Industry 4.0 using interpretive ranking process

Purpose

Lean Six Sigma (LSS) is a continuous improvement procedure that helps in improving the performance, decreasing variations, reducing lead time and increasing profit. Industry 4.0 (I4.0) portrays a new phase in the enterprise and aims at digitalization of the enterprise. The basis of I4.0 is a cyber-physical system, leading to product networking and complete coordination of process, value enhancement and further optimization. Integrating LSS methodology with I4.0 will give an edge in competitiveness to an enterprise. The aim of this study is to identify and analyze barriers to have an obstacle free implementation.

Design/methodology/approach

LSS and I4.0 barriers are identified through literature review. In this paper 18 barriers of LSS and I4.0 that hinder the implementation of integrated LSS and I4.0 are collected. Analysis of barriers was done using interpretive ranking process (IRP).

Findings

From the results, it is observed that lack of top management attitude, commitment and involvement are the most dominant barriers.

Practical implications

The study is done by gathering inputs from industry practitioners and derived inferences have practical relevance.

Originality/value

The study of barriers for integrated LSS and I4.0 is the original contribution of the authors.

Implementation of Industry 4.0 Principles and Tools: Simulation and Case Study in a Manufacturing SME

Small and medium enterprises (SME) face various challenges in order to remain competitive in a global market. Industry 4.0 (I4.0) is increasingly presented as the new paradigm for improving productivity, ensuring economic growth, and guaranteeing the sustainability of manufacturing companies. However, SMEs are ill equipped and lack resources to undertake this digital shift. This paper presents the digital shift process of an SME in a personalized mass production context. Our work provides a better understanding of the interaction between Lean and I4.0. It contributes to the development of Lean 4.0 implementation strategies that are better adapted to manufacturing SMEs in a personalized mass production context. We also demonstrate the usefulness of simulation as a decision-making assistance tool when implementing I4.0. A practical case is documented to fill a gap in the scientific literature identified by several researchers.

Development Trends of Production Systems through the Integration of Lean Management and Industry 4.0

The integration of efficient production and management systems with the new Industry 4.0 concept represents a challenge for any company wishing to adapt its processes in order to increase performance, both at the operational and the organizational levels, through digitization and connectivity. This research proposes an analysis of Lean tools and Industry 4.0 technologies for compatibility in order to provide a framework model for development and integration in industrial applications. Through a systematic review of the literature, this paper contributes to the development of the current vision regarding the implementation and integration of the two “paradigms” of production. An analysis of the current key production strategies was performed through a new conceptual approach from the perspective of correlating the Lean management system, a system that has been stable in recent decades, with the innovative technologies of Industry 4.0. The applicative character of the research consisted of the optimization by simulation of a flexible production system in which the two concepts were integrated. The results showed that the implementation of Lean in the field of flexible manufacturing, correlated with the integration of Industry 4.0 techniques, such as digital twin and simulation, led to improved production processes by fast and flexible reconfiguration, with the two concepts being interdependent.

PROPERTY MANAGEMENT ENABLED BY ARTIFICIAL INTELLIGENCE POST COVID-19: AN EXPLORATORY REVIEW AND FUTURE PROPOSITIONS

The Covid-19 pandemic outbreak across the globe has disrupted human life and industry. The pandemic has affected every sector, with the real estate sector facing particular challenges. During the pandemic, property management became a crucial task and property managers were challenged to control risks and disruptions faced by their organizations. Recent innovative technologies, including artificial intelligence (AI), have supported many sectors through sudden disruptions; this study was performed to examine the role of AI in the real estate and property management (PM) sectors. For this purpose, a systematic literature review was conducted using structural topic modeling and bibliometric analysis. Using appropriate keywords, the researchers found 175 articles on AI and PM research from 1980 to 2021 in the SCOPUS database. A bibliometric analysis was performed to identify research trends. Structural topic modelling (STM) identified ten emerging thematic topics in AI and PM. A comprehensive framework is proposed, and future research directions discussed.

Benchmarking Industry 4.0 readiness evaluation using fuzzy approaches

Purpose

The purpose is to assess Industry 4.0 (I4.0) readiness index using fuzzy logic and multi-grade fuzzy approaches in an automotive component manufacturing organization.

Design/methodology/approach

I4.0 implies fourth industrial revolution that necessitates vital challenges to be dealt with. In this viewpoint, this article presents the evaluation of I4.0 Readiness Index. The evaluation includes two levels with appropriate criteria and factors. Fuzzy logic approach is used for assessment. Furthermore, the results obtained from fuzzy logic have been benchmarked with multi-grade fuzzy approach.

Findings

The proposed assessment model has successfully utilized fuzzy logic approach for assessment of I4.0 readiness index of automotive component manufacturing organization. Based on fuzzy logic approach, readiness index of I4.0 has been found to be (4.74, 6.26, 7.80) which is further benchmarked using multi-grade fuzzy approach. Industry 4.0 readiness index obtained from multi-grade fuzzy approach is 6.258 and thus, validated. Furthermore, 20 weaker areas have been identified and improvement suggestions are provided.

Research limitations/implications

The assessment module include two levels (Six Criteria and 50 Factors). The assessment model could be expanded based on advancements in industrial developments. Therefore, future researchers could utilize findings of the readiness model to further develop multi-level assessment module for Industry 4.0 readiness in organization. The developed readiness model helped researchers in understanding the methodology to assess I4.0 readiness of organization.

Practical implications

The model has been tested with reference to automotive component manufacturing organization and hence the inferences derived have practical relevance. Furthermore, the benchmarking strategy adopted in the present study is simple to understand that makes the model unique and could be applied to other organizations. The results obtained from the study reveal that fuzzy logic-based readiness model is efficient to assess I4.0 readiness of industry.

Originality/value

The development of model for I4.0 readiness assessment and further analysis is the original contribution of the authors. The developed fuzzy logic based I4.0 readiness model indicated the readiness level of an organization using I4RI. Also, the model provided weaker areas based on FPII values which is essential to improve the readiness of organization that already began with the adoption of I4.0 concepts. Further modification in the readiness model would help in enhancing I4.0 readiness of organization. Moreover, the benchmarking strategy adopted in the study i.e. MGF would help to validate the computed I4.0 readiness.

Lean in industry 4.0 is accelerating manufacturing excellence – A DEMATEL analysis

Purpose

The purpose of this research paper is to study the digital accelerators in conjunction with lean manufacturing enablers in the technology driven Industry 4.0 (I4.0) and understand their interrelationship dynamics with a goal to accelerate the pace of manufacturing excellence.

Design/methodology/approach

Literature review coupled with the focus group approach facilitated to cull the key accelerating enablers to lean in I4.0. Thereafter, application of the multi criteria decision making methodology–DEMATEL (Decision Making Trial and Evaluation Laboratory) was carried out for analysis.

Findings

A total of 18 factors from the integration of lean in I4.0 were identified from the focus group approach. The analysis from DEMATEL approach reflected that big data analytics and technology driven talent were the two most important factors in the manufacturing excellence journey. Leadership standard work and continuous improvement culture were the two key cause category factors, while, just in time the critical effect category factor.

Practical implications

Analysis from DEMATEL approach has provided useful insights to industry leaders with the details of the degree of importance and type of influencing factors. It has given them direction in areas of investment to face the challenges of smart factories of tomorrow for sustainability.

Originality/value

Application of DEMATEL approach for analyzing the dynamics of the 18 factors in the integrated lean systems in I4.0 for manufacturing excellence.

Continuous improvement behaviors and impacts on innovation

Purpose

This paper investigates whether adopting continuous improvement (CI) behaviors is related to better radical and incremental product and process innovation performance and identifies whether CI behaviors positively affect respondent perceptions on CI contributions to innovation performance.

Design/methodology/approach

Data were collected from 139 firms in Brazil, and cluster analysis was used to identify groups with different CI adoption levels. The nonparametric Wilcoxon–Mann–Whitney test was used to verify differences in innovation performance at companies via manager perceptions on CI and innovation.

Findings

Data show that more CI behavior implementation was associated with better product and process innovation performance. Furthermore, companies with more mature CI behavior levels innovate more, which may reinforce CI investments.

Practical implications

Organizational managers focused on innovation should dedicate time to evaluating and developing CI behaviors, and maturing CI philosophies to improve performance. This study can contribute to the literature by offering insights for developing public policies, especially for emerging economies, or for companies seeking to become more competitive, since CI can foster and promote a culture of long-term innovation.

Originality/value

Despite the fact that a relationship between CI and operational performance has already been established, there is still a lack of research that identifies the impacts of CI behavior on innovation. Focusing on CI behavior is important because it can be fostered by various programs and improvement initiatives, highlighting paths for managerial practices and academia. This study was conducted for an emerging economy.

Analyzing the Implementation of Lean Methodologies and Practices in the Portuguese Industry: A Survey

The mass production paradigm on which much of the industry was based has changed. The market is increasingly demanding, requesting diversity and products that are more and more adapted to personal wishes and requirements. This implies producing a greater diversity of products in smaller quantities. Competitiveness is enormous, which forces most companies to be truly effective and efficient, taking care of product quality, delivery time, and final cost. Lean methodologies have been a valuable aid in this field. The diversity of Lean tools has been shown to have answers to the most diverse challenges, and companies are aware of this, increasingly adopting methodologies and processes that aim to progressively reduce waste and adapting their production paradigm to what the market requires. This work intends to provide a vision, as global as possible, of the pathway of Lean implementation in the Portuguese industry. For this purpose, a survey was carried out with a significant sample of Portuguese industrial companies from a wide range of activity sectors. The data collected through the survey were treated statistically, and then a SWOT analysis of the results was performed, which provided a collection of precious information on the evolution of industrial companies in Portugal.

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.

Quality 4.0 – a review of and framework for quality management in the digital era

Purpose

Industry 4.0 has brought about a paradigm shift in value delivery with the introduction of disruptive technologies. This has resulted in efforts by organizations to re-invent their business processes and reskill their workforce while attempting to realize digital transformation. Quality management in the context of Industry 4.0 is still in its nascent stage with researchers trying to identify key and relevant components of quality management with respect to Industry 4.0. The current study attempts to address the knowledge gap through a literature review and subsequently provide a conceptual framework for quality in the digital transformation context.

Design/methodology/approach

An integrative literature review was conducted to analyze and abstract knowledge from the literature on Quality 4.0 and a conceptual framework was developed based on the review.

Findings

The review revealed the motivators, building blocks and challenges for Quality 4.0. The conceptual framework discusses the salient points relevant to Quality 4.0 with respect to the people, process and technology dimensions and their sub-dimensions that can be used to build 4.0 capabilities. The proposed framework is represented to depict the conceptualization and the relationships among its components.

Originality/value

This study aims to contribute to the model building efforts of researchers towards Quality 4.0. The points discussed here provide an actionable direction to augment the efforts of practitioners and organizations in quality management in the context of Industry 4.0, especially digital transformation.

Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research

The challenging situations and disruptions that occurred due to the outbreak of the COVID-19 pandemic have created a severe need for supply chain resiliency (SCR). There has been a growing interest among researchers to investigate the resiliency in supply chain operations to overcome risks and disruptions and to achieve successful project management. The supply chain of every business requires innovative projects to accomplish competitive advantage in the market. This study was conducted to identify the significance of artificial intelligence (AI) for creating a sustainable and resilient supply chain, and also to provide optimum solutions for supply chain risk mitigation. A systematic literature review has been conducted to examine the potential research contribution or directions in the field of AI and SCR. In total, 162 articles were shortlisted from the SCOPUS database in the chosen field of research. Structural Topic Modeling (STM), a big data-based approach, was employed to generate several thematic topics of AI in SCR based on the shortlisted articles, and all topics were discussed. Furthermore, the bibliometric analysis was conducted using R-package to investigate the research trends in the area of AI in SCR. Based on the conducted review of literature, a research framework was proposed for AI in SCR that will facilitate researchers and practitioners to improve technological development in supply chain firms. The purpose is to combat sudden risks and disruptions so that project management will perform well Post COVID-19. The study will be also helpful for future researchers and practitioners to identify research directions based on existing literature covered in this paper in the field of SCR. Future research directions are proposed for AI-enabled resilient supply chain management. This study will also provide several implications for supply chain managers to achieve the required resilience in their supply chains post COVID-19 by focusing on the elements of the proposed research framework.

PDCA 4.0: A New Conceptual Approach for Continuous Improvement in the Industry 4.0 Paradigm

Continuous improvement (CI) is a key component of lean manufacturing (LM), which is fundamental for organizations to remain competitive in an ever more challenging market. At present, the new industrial revolution, Industry 4.0 (I4.0), is taking place in the manufacturing and service markets, allowing more intelligent and automated processes to become a reality through innovative technologies. Not much research was found regarding a holistic application of I4.0′s technological concepts towards CI, which clarifies the potential for improving its effectiveness. This clearly indicates that research is needed regarding this subject. The present publication intends to close this research gap by studying the main I4.0 technological concepts and their possible application towards a typical CI process, establishing the requirements for such an approach. Based on that study, a conceptual approach is proposed (PDCA 4.0), depicting how I4.0 technological concepts should be used for CI enhancement, while aiming to satisfy the identified requirements. By outlining the PDCA 4.0 approach, this paper contributes to increasing the knowledge available regarding the CI realm on how to support the CI shift towards a I4.0 industrial paradigm.

A systematic and network-based analysis of data-driven quality management in supply chains and proposed future research directions

Purpose

This work aims to review past and present articles about data-driven quality management (DDQM) in supply chains (SCs). The motive behind the review is to identify associated literature gaps and to provide a future research direction in the field of DDQM in SCs.

Design/methodology/approach

A systematic literature review was done in the field of DDQM in SCs. SCOPUS database was chosen to collect articles in the selected field and then an SLR methodology has been followed to review the selected articles. The bibliometric and network analysis has also been conducted to analyze the contributions of various authors, countries and institutions in the field of DDQM in SCs. Network analysis was done by using VOS viewer package to analyze collaboration among researchers.

Findings

The findings of the study reveal that the adoption of data-driven technologies and quality management tools can help in strategic decision making. The usage of data-driven technologies such as artificial intelligence and machine learning can significantly enhance the performance of SC operations and network.

Originality/value

The paper discusses the importance of data-driven techniques enabling quality in SC management systems. The linkage between the data-driven techniques and quality management for improving the SC performance was also elaborated in the presented study.

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

Purpose

The proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.

Design/methodology/approach

Fora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.

Findings

BD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.

Research limitations/implications

The proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.

Originality/value

There are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

Nexus of circular economy and sustainable business performance in the era of digitalization

Purpose

This study aims to conduct a comprehensive review and network-based analysis by exploring future research directions in the nexus of circular economy (CE) and sustainable business performance (SBP) in the context of digitalization.

Design/methodology/approach

A systematic literature review methodology was adopted to present the review in the field of CE and SBP in the era of digitalization. WOS and SCOPUS databases were considered in the study to identify and select the articles. The bibliometric study was carried out to analyze the significant contributions made by authors, various journal sources, countries and different universities in the field of CE and SBP in the era of digitalization. Further, network analysis is carried out to analyze the collaboration among authors from different countries.

Findings

The study revealed that digitalization could be a great help in developing sustainable circular products. Moreover, the customers' involvement is necessary for creating innovative sustainable circular products using digitalization. A move toward the product-service system was suggested to accelerate the transformation toward CE and digitalization.

Originality/value

The paper discusses digitalization and CE practices' adoption to enhance the SP of the firms. This work's unique contribution is the systematic literature analysis and bibliometric study to explore future research directions in the nexus of CE and SP in the context of digitalization. The present study has been one of the first efforts to examine the literature of CE and SBP integration from a digitalization perspective along with bibliometric analysis.

Applying Industry 4.0 technologies in the COVID–19 sustainable chains

Purpose

The purpose of this paper is 1) to investigate the effects on the crucial Industry 4.0 technological innovations that interact between the real and virtual worlds and that are applied in the sustainable supply chain process; 2) to contribute to the identification of the opportunities, the challenges and the gaps that will support the new research study developments and 3) to analyze the impact of the Industry 4.0 technologies as facilitators of the sustainable supply chain performance in the midst of the Coronavirus (COVID-19).

Design/methodology/approach

This research is performed through a bibliographic review in the electronic databases of the Emerald Insight, the Scopus and the Web of Science, considering the main scientific publications on the subject.

Findings

The bibliographic search results in 526 articles, followed by two sequential filters for deleting the duplicate articles (resulting in 487 articles) and for selecting the most relevant articles (resulting in 150 articles).

Practical implications

This article identifies the opportunities and the challenges focused on the emerging Industry 4.0 theme. The opportunities can contribute to the sustainable performance of the supply chains and their territories. The Industry 4.0 can also generate challenges like the social inequalities related to the position of the man in the labor market by replacing the human workforce with the machines. Therefore, the man-machine relationship in the Industry 4.0 era is analyzed as a gap in the literature. Therefore, as a way to fill this gap, the authors of this article suggest the exploration of the research focused on the Society 5.0. Also known as “super-smart society,” this recent theme appeared in Japan in April 2016. According to Fukuda (2020), in addition to the focus on the technological development, the Society 5.0 also aims at the quality of life and the social challenge resolutions.

Originality/value

This article contributes to the analysis of the Industry 4.0 technologies as facilitators in the sustainable supply chain performance. It addresses the impacts of the Industry 4.0 technologies applied to the supply chains in the midst of the COVID-19 pandemic, and it analyzes the research gaps and limitations found in the literature. The result of this study can add value and stimulate new research studies related to the application of the Industry 4.0 technologies as facilitators in the supply chain sustainable performance. It can encourage the studies related to the COVID-19 impacts on the sustainable supply chains, and it can promote the research development on the relationship among the man, the machine and the labor in the Fourth Industrial Revolution.