The relationship between Lean Six Sigma, Industry 4.0 and sustainable manufacturing has been evaluated only to a limited extent within this domain of the published literature. A DMAIC-DMADV-based framework along with a phase-by-phase implementation path is proposed in this study to integrate Lean Six Sigma and Industry 4.0 technologies for achieving sustainable manufacturing. The paper also focused on identifying and prioritizing the critical success factors for the implementation of the proposed framework. The critical success factors identified through a literature review are ranked using the multi-decision criteria technique TOPSIS, with input from selected experts across various manufacturing companies. The results highlight that the most important enablers set clear sustainability goals, regularly monitor progress and have a skilled workforce. The findings provide actionable guidance for practitioners, and the study contributes to the existing body of knowledge by offering a comprehensive methodology to integrate Lean Six Sigma and Industry 4.0 for sustainable manufacturing. Further research must focus on the validation of the framework in diverse industrial settings and refining the sustainability assessment model to enhance its adaptability.

This bibliometric analysis elucidates the evolution and impacts of Project-Based Learning (PBL) within vocational education, emphasizing its relevance in translating career goals and addressing educational gaps, particularly from 1993 to 2023. Analyzing 555 Scopus-indexed documents, this study utilized advanced bibliometric tools to uncover PBL's growing appeal and its innovative pedagogical integration into design thinking, challenge-based education, and multidisciplinary approaches. Our findings highlight PBL's significant outcomes, including its role in bolstering STEM curricula and fostering critical skill development, thus bridging the gap between theoretical knowledge and practical application essential for the workforce. Despite the documented benefits, our analysis also identifies substantial gaps in research, particularly the scarcity of longitudinal studies on PBL's long-term effectiveness and its comparative efficacy against traditional pedagogies within diverse educational contexts. The study underscores PBL's utility in vocational education as a transformative approach, aligning with dynamic career expectations and the requirements of the knowledge economy. By offering a detailed thematic evolution and pinpointing influential contributions, this research serves as a strategic guide for educators and policymakers, advocating for informed, future-ready educational strategies that embrace experiential learning to meet the evolving needs of students and the labor market.

Purpose: This study aims to identify and analyze the critical barriers and enablers in integrating Industry 5.0 with Lean Six Sigma (LSS) in the manufacturing sector. Using a Fuzzy DEMATEL approach, this study uncovers the cause-effect relationships among key factors based on the judgments of 52 industry experts. Design/methodology/approach: A fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) method was applied to data collected from 52 industry experts across multiple manufacturing sectors. Experts evaluated the mutual influence among 14 pre-validated factors (seven barriers and seven enablers) using a five-point linguistic scale. The fuzzy evaluations were defuzzified, aggregated and analyzed to derive prominence (D + R) and net causality (D–R) scores, leading to the construction of cause-effect diagrams. Findings: The Fuzzy DEMATEL results revealed that strategic alignment and long-term vision, employee engagement and pilot project success were the most influential enablers driving Industry 5.0-LSS integration, while top management support and investment in digital infrastructure played key dependent roles. Among the barriers, technological incompatibility and high implementation cost emerged as dominant causal challenges, whereas data security concerns and lack of skilled workforce were highly impactful but largely effect-driven issues. Research limitations/implications: This research is limited to perceptual expert-based data and focuses on discrete manufacturing industries. Future studies should apply longitudinal validation, include operational KPIs and extend analysis to other sectors such as services or public administration. Originality/value: This study is among the first to apply Fuzzy DEMATEL to Industry 5.0-LSS integration using a large, diverse expert panel. This study contributes a data-driven, actionable framework for prioritizing strategic interventions and challenges top-down implementation models by identifying bottom-up enablers such as pilot success and workforce engagement. This study uniquely contributes by presenting a dual-perspective Fuzzy DEMATEL framework that simultaneously models the enablers and barriers influencing Industry 5.0-LSS integration, offering a novel causal-effect understanding of digital-human transformation in manufacturing.

Purpose: This study develops and empirically tests a TQM5.0 framework that integrates classical total quality management (TQM) with Industry 5.0 value priorities through the enabling mechanism of quality intelligence. It examines how TQM practices and Industry 5.0 capabilities influence quality intelligence and how quality intelligence drives quality, sustainability, and resilience performance outcomes. Design/methodology/approach: Data were collected via a structured questionnaire from 56 purposively selected domain experts with decision-relevant roles in manufacturing quality and operations, and the model was analyzed using Partial Least Squares Structural Equation Modeling in SmartPLS with bootstrapping. The study assessed measurement reliability/validity, collinearity, structural paths, explanatory power (R2), effect sizes (f2), and mediation via specific indirect effects. Findings: TQM practices significantly predict quality intelligence, while Industry 5.0 capabilities do not show a significant effect on quality intelligence in this sample. Quality intelligence significantly improves quality, sustainability, and resilience performance (with the strongest effect on resilience), whereas the proposed mediation effects through quality intelligence are not statistically supported. Practical implications: Managers should prioritize strengthening TQM governance routines and measurement discipline to build robust quality intelligence capabilities that enable proactive and adaptive performance. Implementation should sequence foundational TQM routines, quality data and analytics infrastructure, and the scaling of Industry 5.0 initiatives through intelligence-enabled governance to improve sustainability and resilience outcomes. Originality/value: The study advances the emerging TQM5.0 concept by positioning quality intelligence as a central mechanism linking quality governance to Industry 5.0-aligned performance outcomes and by validating a parsimonious measurement and structural model. It contributes empirical evidence from expert-informed manufacturing contexts and provides actionable guidance on capability sequencing for intelligence-enabled quality transformation.

Purpose: This study examines how key Industry 4.0 technologies relate to environmental sustainability performance in supply chains. Specifically, it examines Internet of Things, Artificial Intelligence, Big Data Analytics, Blockchain, Digital Twin and Cloud Computing, and explores their relative associations with sustainability outcomes using both statistical and machine learning techniques. Design/methodology/approach: A quantitative cross-sectional survey design was adopted. Data were collected from 268 supply chain and logistics professionals across multiple industries. Descriptive and inferential statistical analyses were conducted using SPSS software version 31.0.2. Machine learning analysis was carried out using Orange Data Mining to perform feature importance ranking and decision tree modeling, with model performance evaluated using 10-fold cross-validation. Findings: Correlation and hierarchical regression analyses reveal no statistically significant association between any individual Industry 4.0 technology and environmental sustainability performance and tested pairwise interaction effects are likewise non-significant. Exploratory machine learning feature rankings order the technologies differently across three methods, and cross-validated models do not outperform a mean-only baseline, indicating no detectable predictive signal in these data. Research limitations/implications: The study is based on cross-sectional survey data, which limits causal interpretation and does not capture changes in technology adoption and sustainability performance over time. The sample, although adequate for the applied analyses, is restricted to 268 respondents and may not fully represent all industries and geographical contexts. Future research should use longitudinal designs, larger international samples and additional Industry 4.0 technologies to extend the understanding of digital transformation and sustainability relationships in supply chains. Practical implications: The findings caution against expecting sustainability improvements from technology adoption alone. No individual technology, and no tested combination, showed a statistically significant association with environmental sustainability performance, and cross-validated models showed no predictive signal. The results redirect managerial attention from technology acquisition toward the mediating organizational practices through which digital technologies may generate sustainability value. Originality/value: This study measures six Industry 4.0 technologies as separate constructs within a single sample, combines conventional statistical testing with exploratory, cross-validated machine learning, and transparently reports a null result for direct technology–performance associations a finding that complements prior mediation-based evidence by suggesting that direct effects of individual technologies may be weak in the absence of mediating capabilities.