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Supply chains need to be made viable in this volatile and competitive market, which could be possible through digitalization. This study is an attempt to explore the role of Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business performance from the lens of natural resource-based view.

The study tests the proposed model using a covariance-based structural equation modelling and further investigates the ranking of each construct using the artificial neural networks approach in AMOS and SPSS respectively. A total of 234 respondents selected using purposive sampling aided in capturing the industry practices across supply chains in the UK. The full collinearity test was carried out to study the common method bias and the content validity was carried out using the item content validity index and scale content validity index. The convergent and discriminant validity of the constructs and mediation study was carried out in SPSS and AMOS V.23.

The results are overtly inferring the significant impact of Industry 4.0 practices on creating smart and ultimately sustainable supply chains. A partial relationship is established between Industry 4.0 and supply chain agility through a smart supply chain. This work empirically reinstates the combined significance of green practices, Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business value. The study also uses the ANN approach to determine the relative importance of each significant variable found in SEM analysis. ANN determines the ranking among the significant variables, i.e. supply chain resilience > green practices > Industry 4.0> smart supply chain > supply chain agility presented in descending order.

This study is a novel attempt to establish the role of digitalization in SCs for attaining sustainable business value, providing empirical support to the mediating role of supply chain agility, supply chain resilience and smart supply chain and manifests a significant integrated framework. This work reinforces the integrated model that combines all the constructs dealt with in silos so far in prior literature.

This paper aims to investigate how manufacturers can foster insights and improvements from real-time data among shop-floor workers by developing organisational “learning-to-learn” capabilities based on both the lean- and action learning principle of learning through problem-solving. Second, the purpose is to extrapolate findings on how action learning can enable the complementarity between lean and industry 4.0.

An insider action research approach is adopted to investigate how manufacturers can enable their shop-floor workers to foster insights and improvements from real-time data at VELUX.

The findings report that enabling shop-floor workers to use real-time data consist of developing three consecutive organisational building blocks of learning-to-learn, learning-to-learn using real-time data and learning-to-learn generating real-time data − and helping others to learn (to learn).

First, the study contributes to theory and practice by demonstrating that a learning-to-learn capability is a core construct for manufacturers seeking to enable shop-floor workers to use real-time data-capturing systems to drive improvement. Second, the study outlines how lean and industry 4.0 complementarity can be enabled by action learning. Moreover, the study allows us to deduce six necessary conditions for enabling shop-floor workers to foster insights and improvements from real-time data.

The purpose of this paper is to unravel the success story of a family business, while engaging with debates in business and management research through the application of corporate strategy frameworks. Implement a structured methodology to evaluate a company’s strategic efforts and explore how businesses historically achieved competitive advantages over time.

This company analysis adopts a longitudinal approach on competitive advantages, moving beyond traditional cross-sectional business to transform static models into dynamic ones. It adheres to Michael Porter’s Value Chain model (1985) and his subsequent revisions (1996, 2001) to explore how competitive advantages emerge and are sustained. Data exploration leverages an extensive archival corporate collection comprising approximately 100,000 documents, enabling a thorough examination of value chain activities through primary and secondary sources.

Chocolates Amatller effectively channelled its competitive advantages through strategic operational areas, including purchasing cocoa at prices below market value, using strong marketing tools such as illustrative collectible cards and posters and implementing skilful financial strategies.

Examine why, when and how a Spanish chocolatier achieved a position of prominence among Spain’s foremost industrial figures by constructing Barcelona’s iconic Casa Amatller.

This paper gives the background to the ISO 18404:2015 standard and explains its rationale. It aims to rebut the Oudrhiri et al. (2022) paper. Furthermore, this paper adds further evidence of the misplacement and unfitness for use of the standard, as evidenced in the previous work by Antony et al. (2021, 2022).

A point-counterpoint methods approach with a literature review of studies available on ISO 18404:2015 to respond to the Oudrhiri et al. (2022) study.

The findings indicate that Oudrhiri et al.'s (2022) work is not open minded in relation to ISO18404. Each point raised in the Oudrhiri et al. (2020) study has been answered and counter argued.

Other than Antony et al.'s three studies (2021 and 2022) and Oudrhiri et al.’s (2022) study empirical studies looking into the impact of the ISO 18404 standard in the literature were limited. As the literature has shown, many companies are not utilising the standard given its current format; hence, a lack of information relating to the practical implementation is sparse.

The findings indicate that Oudrhiri et al.'s (2022) work can be answered and counter argued.

This study consolidates and strengthens the findings from the three studies by Antony et al. (2021 and 2022) and acts as a rebuttal to the Oudrhiri et al. (2022) study.

This study aims to investigate the adoption of Quality 4.0 (Q4.0) and assess the critical failure factors (CFFs) for its implementation and how its failure is measured.

A qualitative study based on in-depth interviews with quality managers and executives was conducted to establish the CFFs for Q4.0.

The significant CFFs highlighted were resistance to change and a lack of understanding of the concept of Q4.0. There was also a complete lack of access to or availability of training around Q4.0.

The study enhances the body of literature on Q4.0 and is one of the first research studies to provide insight into the CFFs of Q4.0.

Based on the discussions with experts in the area of quality in various large and small organizations, one can understand the types of Q4.0 initiatives and the CFFs of Q4.0. By identifying the CFFs, one can establish the steps for improvements for organizations worldwide if they want to implement Q4.0 in the future on the competitive global stage.

The concept of Q4.0 is at the very nascent stage, and thus, the CFFs have not been found in the extant literature. As a result, the article aids businesses in understanding possible problems that might derail their Q4.0 activities.

The purpose of this study is to apply bibliometric techniques to critically examine the contributions of Professor (Dr) Jiju Antony in the domain of quality management research over a period close to 30 years (i.e. 1995 to 2023).

The study incorporates a bibliometric analysis approach using VoSviewer software package to critically examine the contributions and trends in publications on Scopus indexed publications of Antony, followed by an industry wide evaluation of contributions made. Thematic analysis of the bibliographic data was imported in comma-separated values (CSV) format by selecting Antony (last name) and Jiju (first name) in the author search tab in Scopus. The search was made on September 09, 2023 and bibliographic records of 429 documents were considered for the analysis. The analysis was carried out in terms of most frequent keywords used, sources with most frequent publications, thematic analysis of most cited works and global collaborations.

Antony has focused his research interest largely in the domain of quality management, publishing his research in top rated journals in the domain with a broad network of international collaborators. Antony has received 18,802 and 40,947 citations in Scopus and Google Scholar, respectively. This signifies the impact that Antony has created through his research publications. His major contributions are on the topics of six sigma, lean six sigma, continuous improvement, critical success factors and quality management practice implementations in various organizations. Diverse methodologies both qualitative and quantitative were utilized to conduct his research. However, his body of work is not without criticism. Such criticism includes the limited scope of work, with narrow focus on ISO 9001 and QMS standards, Total Quality Management (TQM). Critique also highlights the necessity for more depth, following insufficient exploration of distinctions between TQM and operational excellence (OPEX) methodologies like lean, six sigma, kaizen and agile. Antony’s work has yet to consider a diverse range of industry sectors, in terms of implementation of quality management principles, geographical location, the impact of national culture on corporate performance and explore data quality influence on decision-making. Notably, there is clear opportunity to consider the service sector in future research.

To best of the authors’ knowledge, there are few previous studies conducted using bibliometric analysis for analyzing the work of an individual. Therefore, the present study aims to set a trend whereby bibliometric analysis can be used to recognize and critically asses the contributions of other researchers in their respective domains.

The integration of Lean Six Sigma (LSS) and Industry 4.0 (I4.0) is in the nascent stage and promises to achieve new optimums in operational excellence. This study aims to empirically examine the enablers, barriers, benefits and application of I4.0 technologies in LSS and I4.0 integration.

A pilot survey was chosen as an appropriate methodology, as LSS and I4.0 integration is still budding. The survey targeted senior quality management professionals, quality managers, team leaders, LSS Black Belts and operations managers to collect the relevant research data. The questionnaire was sent to 200 respondents and received 53 valid responses.

This study reveals that “top management support” is an essential enabler for LSS and I4.0 integration. The most significant barrier was “poor understanding of data analysis” and “lack of top management support”. The findings further illustrated that LSS and I4.0 integration resulted in greater efficiency, lower operational costs, improved productivity, improved customer satisfaction and improved quality. Regarding I4.0 technology integration at different phases of LSS, the authors noticed that big data analytics and artificial intelligence (AI) are the most prominent technologies used in all phases of LSS implementation.

One of the limitations of this study is the sample size. LSS and I4.0 are emerging concepts; hence, obtaining a larger sample size is difficult. In addition, the study used non-parametric tests to analyse the data. Therefore, future studies should be conducted with large sample sizes across different continents and countries to understand differences in the key findings.

The outcomes of this study can be useful for organisational managers to understand the enablers and barriers before integrating LSS and I4.0 for adoption in their organisations. Secondly, it helps to convince top management and human resource personnel by providing a list of benefits of LSS and I4.0 integration. Finally, it can help decision-makers understand which I4.0 technologies can be used in different stages of LSS methodology.

LSS and I4.0 integration was studied at a conceptual level. This is the first empirical study targeted toward understanding the LSS and I4.0 integration. In addition, this study investigates the application of widely used I4.0 technologies in different phases of LSS.

Despite the global success of Lean Six Sigma (LSS) operations, they are frequently criticised for failing to improve a company’s financial performance. This study investigated and contrasted the financial advantages of LSS, Industry 4.0 (I4.0) and Lean Six Sigma 4.0 (LSS4.0) practices in China and the United States.

We conducted statistical analyses using online databases. We applied regression analysis to quantitatively analyse 32,813 responding organisation data from 3,691 A-share manufacturing companies in China and 5,931 responding organisation data from 570 publicly traded manufacturing firms in the United States.

This study discovered the following findings: (1) American companies are paying less attention to LSS, which is gradually becoming more important in Chinese business strategy. (2) There is a strong positive relationship between LSS implementation and financial performance in China. Nonetheless, the financial impact of LSS is less significant in post-industrial countries such as the United States. (3) There is compelling evidence in the Chinese context for the significant impact of higher LSS maturity levels on the financial performance of publicly traded manufacturing firms. However, the financial benefits of higher LSS maturity levels in the United States are less pronounced. (4) Despite frequent discussions in China and the United States, implementing I4.0 and related technologies has yet to yield the desired results, particularly in the United States. (5) LSS4.0 significantly improves organisational financial performance.

This study explored the impact of LSS, I4.0 and LSS4.0 on financial performance and conducted an international comparative study from a big data perspective.

There is increasing interest in the connection between Industry 4.0 (I4.0) and operational excellence approaches; however, studies on the integration between Six Sigma (SS) and I4.0 have been absent from the literature. Integration with I4.0 technologies can maximize the positive effects of SS. The purpose of this study is to understand what types of relationships exist between SS and I4.0 and with I4.0's technologies, as well as the benefits derived from this integration and future directions for this field of study.

A Systematic Literature Review (SLR) was carried out to analyze studies about connections between I4.0 technologies and SS. SLR analyzed 59 articles from 2013 to 2021 extracted from the Web of Science and Scopus databases, including documents from journals and conferences.

The SLR identified relationships between SS and several I4.0 technologies, the most cited and with the greatest possibilities of relationships being Big Data/Big Data Analytics (BDA) and Internet of Things (IoT). Three main types of relationships were identified: (1) support of I4.0 technologies to SS; (2) assistance from the SS to the introduction of I4.0 technologies, and, to a lesser extent; (3) incompatibilities between SS and I4.0 technologies. The benefits are mainly related to availability of large data sets and real-time information, enabling better decision-making in less time.

In addition, the study can help managers to understand the integration relationships, which may encourage companies to adopt SS/Lean Six Sigma (LSS) in conjunction with I4.0 technologies. The results also drew attention to the incompatibilities between SS and I4.0 to anticipate potential barriers to implementation.

The study focuses on three previously unexplored subjects: the connection between SS and I4.0, the existing relationships with different technologies and the benefits resulting from the relationships. In addition, the study compiled and structured different types of relationships for SS and I4.0 and I4.0's technologies, identifying patterns and presenting evidence on how these relationships occur. Finally, exposes current trends and possible research directions.

This paper aims to conduct an inclusive bibliometric review of the International Journal of Lean Six Sigma (IJLSS) to elucidate the scholarly landscape, growth trends, impact, mappings, couplings, networking and thematic evolution within the field of Lean Six Sigma (LSS) research.

Using advanced bibliometric techniques, including network analysis and clustering, this study examines the publication output of IJLSS since its inception in 2010. The analysis focuses on identifying key contributors, mapping collaborative networks, tracing thematic evolution and exploring emerging research trends. The study is executed as per the proposed easy-to-understand methodology, containing well-structured nine segments hitting various critical-bibliometrics of IJLSS along with their respective implications.

The review reveals substantial growth in the publication output of IJLSS, with India emerging as a prominent contributor. Keywords such as “Lean”, “Six Sigma”, “Quality Management”, “Operational Excellence”, “Supply Chain Management”, “Industry 4.0” and “Sustainability” emerge as central themes, reflecting the journal’s focus on process improvement methodologies along with corresponding case studies. Collaborative networks among authors and countries are robust, indicating the global reach of LSS scholarship. Emerging research trends highlight areas of potential future exploration within the field.

Limitations of this study include the reliance on bibliometric data and the exclusion of nonindexed sources. However, the findings offer valuable insights into the scholarly landscape of IJLSS, providing researchers and practitioners with a comprehensive overview of LSS research inclinations and developments.

This paper contributes to the literature by providing a detailed analysis of LSS research published in IJLSS through a unique approach and future directions. The study adds to existing knowledge by mapping collaborative networks, tracing thematic couplings and identifying emerging research clusters within the ever evolving domain of LSS.