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Organisations are looking for a concept that can solve traditional as well upgraded production problems with current resources and technology, and this can be addressed by integration of lean six sigma (LSS) with Industry 4.0 (I4.0) technologies. This reduces complexity in the manufacturing process through digital technologies. Cyber physical system (CPS) is considered as primary I4.0 technology with which all other technologies are associated to extend. CPS can integrate with other prevailing manufacturing approaches like lean, LSS and so on. LSS, on the other hand, is a team-focussed performance improvement strategy which is widely used by the industries to identify problems, eliminate waste to meet customer requirements. The study aims at analysis of challenges for LSS and CPS integration.

Integrating LSS and CPS will solve both traditional and modern manufacturing problems. To integrate these technologies, organisational requirements need to be assessed. These requirements are posed as challenges in this study. Their priority weights are analysed, and challenges are prioritised using fuzzy Combinative Distance-based Assessment (CODAS) method. Sensitivity analysis is employed to assess the robustness of the results.

The result of this study enables top management to integrate LSS and CPS. In this study, 20 challenges were identified, and they are assessed to compute their relative assessment score. Requirement of new tools and methods with 0.6 score ranks first followed by interplay with big data and requirement of new communication protocol. The result highlighted the need for integration of LSS and CPS, proper utilisation of information and communication technologies, and cyber security management as the main impediments that need to be addressed to implement CPS in an LSS environment.

The analysis of challenges of LSS and CPS integration using MCDM tool is the original contribution of the authors.

This study aims to provide an analysis of cyber-physical system (CPS)-based lean tools. This study focuses on the identification of lean tools for integration with CPS and analyzes those tools using MCDM (multi-criteria decision-making) approaches.

There exists a need to integrate lean manufacturing with Industry 4.0 technologies. According to literature analysis, CPS is the first stage to implement Industry 4.0 technologies. Based on the extensive study, six CPS-based lean tools, i.e. CPS-based Jidoka system, CPS-based Kanban, CPS-based Andon support system, CPS-based Just-in-time delivery system, CPS-based poka-yoke cell and CPS-based value stream mapping have been considered; then Grey TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) MCDM technique has been applied to rank those tools. These CPS-based lean tools are ranked based on seven performance measures as recognized by academic and industry experts.

The top three CPS-based lean tools are CPS-based Kanban 4.0, CPS-based value stream mapping and CPS-based Just-in-time delivery system have been selected based on the above ranking. The study results have been validated using grey-based approach.

Appropriate criteria to evaluate significant lean tools for integration with CPS are identified, which facilitates managers to assess their current tools and technologies that could be integrated with I4.0, and the implementation of CPS-based lean tools would improve organizational performance.

In the emerging Industry 4.0, integration with advanced technologies provides high degrees of optimization. But there exist challenges for industries to integrate CPS with lean tools; hence, this study attempts to identify and analyze CPS-based lean tools. The lean tools are ranked for integration with CPS, the problem is modeled as MCDM problem, and the obtained results are again validated using grey approach. Prioritizing lean tools for integration with CPS is the original contribution of this study.

The use of technology in 4th industrial revolution is at its peak. Industries are trying to reduce the consumption of resources by effectively utilizing information and technology to connect different functioning agents of the manufacturing industry. Without digitization “Industry 4.0” will be a virtual reality. The present survey-based study explores the factual status of digital manufacturing in the Northern India.

After an extensive literature review, a questionnaire was designed to gather different viewpoints of Indian industrial practitioners. The first half contains questions related to north Indian demographic factors which may affect digitalization of India. The latter half includes the queries concerned with various operational factors (or drivers) driving the digital revolution without ignoring Indian constraints.

The focus of this survey was to understand the current level of digital revolution under the ongoing push by the Indian government focused upon digital movement. The analysis included non-parametric testing of the various demographic and functional factors impacting the digital echoes, specifically in Northern India. Findings such as technological upgradations were independent of type of industry, the turnover or the location. About 10 key operational factors were thoughtfully grouped into three major categories—internal Research and Development (R&D), the capability of the supply chain and the capacity to adapt to the market. These factors were then examined to understand how they contribute to digital manufacturing, utilizing an appropriate ordinal logistic regression. The resulting predictive analysis provides seldom-seen insights and valuable suggestions for the most effective deployment of digitalization in Indian industries.

The country-specific Industry 4.0 literature is quite limited. The survey mainly focuses on the National Capital Region. The number of demographic and functional factors can further be incorporated. Moreover, an addition of factors related to ecology, environment and society can make the study more insightful.

The present work provides valuable insights about the current status of digitization and expects to facilitate public or private policymakers to implement digital technologies in India with less efforts and the least resistance. It empowers India towards Industry 4.0 based tools and techniques and creates new socio-economic dimensions for the sustainable development.

The quantitative nature of the study and its statistical predictions (data-based) are novel. The clubbing of similar success factors to avoid inter-collinearity and complexity is seldom seen. The predictive analytics provided in this study is quite elusive as it provides directions with logic. It will help the Indian Government and industrial strategists to plan and perform their interventions accordingly.

Freeways in Australia play a significant role in connecting distant communities, shifting freight and strengthening the country’s economy. To meet the growing needs of present and future generations, delivering a socially sustainable road infrastructure that creates generational benefits is essential. However, the existing literature reveals the lack of comprehensive indicators to assess the social sustainability performance of freeway projects. Therefore, this paper aims to identify a critical set of system-specific indicators to evaluate the life cycle social footprint of Australian freeways.

This study conducted 31 interview questionnaire surveys with actively engaged stakeholders involved in various freeway projects around Australia. The data collected was analysed using fuzzy set theory and other statistical approaches.

The study identified 42 critical indicators for assessing the social sustainability performance throughout the life cycle of freeways in the Australian context. For example, stakeholder involvement, reduction of casualty rate due to road accidents, fair remuneration to project workforce and improved accessibility to required services.

The context-specific opinions extracted from the industry experts and the comprehensive set of critical indicators identified would ensure that all the vital aspects of social sustainability are considered throughout the life cycle of Australian freeways in the future, assisting the decision-makers in enhancing the project’s social sustainability performance.

The linguistic explanations associated with the ratings given by the industry experts provide greater insight into the context of the life cycle social sustainability assessment of Australian freeways exclusively.

India’s manufacturing sector employs about 12% of the labour force and contributes to about 17% of the nation’s GDP. The Indian government intends to implement several initiatives under the “Make in India” and Atma Nirbhar Bharat banners to increase the manufacturing sector’s share of the nation’s GDP to 25% by 2025. Applying lean manufacturing, green manufacturing and Six Sigma is crucial to ensure that India’s manufacturing sectors grow sustainably in international markets. This study aims to identify sustainability indicators and ascertain their respective weights to evaluate the sustainability performance of the Indian manufacturing industry.

This research identifies 25 sustainability indicators and classifies them into the triple bottom line of sustainability based on an evaluative literature review and expert opinion. The Best Worst Method was utilised to determine the weights of the sustainability indicators. The sustainability index was developed to evaluate economic, social and environmental sustainability.

The sustainability performance of a foundry in a significant Western Indian State city was assessed by applying the developed sustainability index. After the adoption of integrated lean, green and Six Sigma (LG&SS) strategies and related practices in the foundry, there has been a notable improvement of 68.03% in the economic index, 61.62% in the social index and 13.24% in the environmental index.

The proposed sustainability index is applied and evaluated specifically for assessing the sustainability performance of Indian manufacturing SMEs. It can be used to substantiate firm’s sustainability performance and also to assess the improvement in firm’s performance in economic, environmental and social dimensions after implementing various operational excellence practices. However, it cannot serve as a benchmark tool across similar companies or organisations.

The developed sustainable index can be used to analyse the company or organisation’s sustainability performance and see how various strategies have improved things. Practitioners can use this index to assess social, economic and environmental performance and focus on areas that need improvement.

The proposed sustainability index serves as a vital tool for monitoring a firm’s progress in triple bottom line (TBL) dimensions of sustainability, tracking a diverse range of indicators and encouraging sustainable organisational practices.

This study attempts to assess the economic, social and environmental performance of Indian Manufacturing SMEs by proposing a sustainability index.

This paper develops an instrument of organizational agility. The instrument is utilized to assess the extent to which Ras Al Khaimah government agencies have adopted agility and to examine its impact on the achievement of strategic outcomes and employee satisfaction.

The dimensions of agility are determined using factor analysis. The reliability of the dimensions is tested based on the Cronbach alpha coefficient, while the predictive validity of the instrument is assessed using correlation and multiple linear regression analysis. The extent to which Ras Al Khaimah government agencies adopted the dimensions of agility is assessed using one-sided T-test, and the difference between the levels of adoption of the dimensions is determined using one-way ANOVA. The relationships between agility the dependent variables of achieving strategic outcomes and employee satisfaction are assessed using multiple linear regression.

The paper determined two valid and reliable dimensions of organizational agility, namely leadership and strategic sensitivity and resource fluidity. Culture, a third reliable dimension found through factor analysis was found to influence agility indirectly. Government agencies have adopted the two dimensions that are found to increase the achievement of strategic outcomes and employee satisfaction.

This paper provides a valid and reliable measure for assessing organizational agility. This measure includes both enablers and capabilities. It adds to the limited empirical research on agility, particularly in the Arab world. The paper focused on local government agencies and its findings may not be applicable in other sectors.

The measure can serve as an effective agility self-assessment tool for organizations, enabling them to identify areas for improvement and specific practices they need to adopt to enhance their agility. This, in turn, allows them to become more responsive to changes, achieve strategic outcomes and improve employee satisfaction.

This paper has important research and practical implications. It provides a valid and reliable measure of organizational agility with both enablers and capabilities. This measure can help organizations become agile and achieve higher strategic outcomes and employee satisfaction.

To attain a competitive edge, it is essential to realize the significant critical success factors (CSFs) that contribute to the adoption of Quality 4.0 (Q4.0) in manufacturing organizations. Therefore, the study aimed to analyze CSFs for Q4.0 implementation in manufacturing small and medium-sized enterprises (SMEs) using multi-criteria decision-making (MCDM) tool.

The present study begins with a systematic literature review of past studies about Q4.0 implementation in manufacturing, followed by the identification of CSFs. Further, a case study was conducted wherein 42 CSFs identified were grouped into five dimensions. Best–worst method is a MCDM tool applied as a solution methodology for the analysis of CSFs based on expert opinion and priority order of CSFs attained.

The priority order of CSFs is obtained. Based on the findings, significant CSFs are “Data prediction and Analytics,” “Organizational culture towards Quality 4.0” and “Machine to Machine communication.”

The shifting market dynamics incorporate Q4.0 inclusion for realizing zero defects and high traceability in automotive SMEs. The present study offers implications for industry managers and practitioners by delivering insights on how Q4.0 could be serving automotive systems and CSFs that industry authorities need to pay attention to effectively adopt Q4.0 in the current quality systems. The study will facilitate industry practitioners to meticulously examine CSFs for Q4.0 toward the improvement of SME performance.

The identification of CSFs for Q4.0 adoption in manufacturing SMEs, along with the prioritization of CFSs using the MCDM tool, is the original contribution by the authors.

Recent years have witnessed a spike in Industry 4.0 initiatives among manufacturing organizations, particularly in the automotive sector. This acceleration aims to enhance competitiveness by addressing various aspects, from efficiency and workforce productivity to safety and insightful decision-making. However, merely adopting technological solutions in isolation may not suffice. Automotive companies need a holistic approach that integrates the antecedents of Industry 4.0 into their overall strategy. This study aims to identify and analyse key antecedents for Industry 4.0 adoption in the Indian automotive sector.

The study follows a structured six-stage methodology, which includes a systematic literature review, expert consultations and best–worst method (BWM) analysis. The research identifies, validates and systematically ranks 16 antecedents that are pivotal for Industry 4.0 adoption.

The study categorizes 16 antecedents into four dimensions: regulatory framework (RF), technology infrastructure (TI), operational optimization (OO) and performance dynamics (PD). The findings emphasize the significance of “Government policies to support smart factories”, “Support from top management”, “Financial performance” and “Technology readiness” as crucial antecedents for Industry 4.0 implementation in the Indian automotive sector.

These findings provide valuable guidance for industry practitioners and policymakers in strategically planning the Industry 4.0 deployment in the automotive sector.

This study contributes to the limited body of research on the identification and analysis of key antecedents for Industry 4.0 adoption in the automotive sector, particularly in emerging economies such as India. By using the BWM, it offers a structured and efficient approach to determining the priority order of these antecedents.

In the present-day highly customer-conscious service environment, supply chain management has become a critical component of health-care industry, helping in fulfilling patient expectation, optimizing inventory and automating departmental activities. Supplier selection is one of the crucial elements of health-care supplier chain, establishing mutually beneficial relationships with the reliable suppliers that provide the most value of money. Health-care supplier selection with feasible sets of alternatives and conflicting criteria can be treated as a multi-criteria decision making (MCDM) problem. Among the MCDM methods, grey relational analysis (GRA) appears as a potent tool due to its simple computational steps and ability to deal with imprecise data. The purpose of this paper is to explore the applicability of a newly developed MCDM tool for solving a health-care supplier selection problem.

In GRA, the distinguishing coefficient (ξ) plays a contributive role in final ranking of the alternative suppliers and almost all the past researchers have considered its value as 0.5. In this paper, a newly developed MCDM tool, i.e. dynamic GRA (DGRA), is adopted to evaluate the relative performance of 25 leading pharmaceutical suppliers for a health-care unit based on nine important financial metrics. Instead of static value of ξ, DGRA treats it as a dynamic variable dependent on grey relational variator and ranks the health-care suppliers using their computed rank product scores.

Based on rank product scores and developed exponential curve, DGRA classifies all the suppliers into reliable, moderately reliable and unreliable clusters, helping the health-care unit in identifying the best performing suppliers for subsequent order allocation. Among the reliable suppliers, alternatives A₂ and A₁₁ occupy the top two positions having almost the same performance with respect to the considered financial metrics.

Application of DGRA along with determination of the most reliable suppliers would help in effectively adopting multi-sourcing strategy to increase resilience while diversifying the supply portfolio, thereby enabling the health-care unit to minimize chances of sudden disruption in the supply chain. It can act as an intelligent decision-making framework aiding in solving health-care supplier selection problems considering perceived risks and dynamic input data.

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.