Search Results (85)

The role of externalities generated by enterprise digital transformation in advancing SDGs 8 and 9 has been largely overlooked in existing research. Taking Lighthouse Factory certification (LFC) as a quasi-natural experiment, this paper uses China’s county-level panel data from 2016 to 2023 and adopts the DID model to investigate the impact of leading enterprises’ digital transformation on regional digital entrepreneurship (RDE). The findings show that LFC promotes RDE by facilitating digital technology transfer, deepening digital technology cooperation, accelerating digital knowledge accumulation, and enhancing local digital industrial competitiveness. Moreover, this effect is more pronounced in regions with stricter environmental regulations and a stronger green transformation climate, yet is less constrained by local digital infrastructure. Interestingly, LFC exerts positive spillover effects on surrounding cities within 50–150 km and those beyond 250 km, whereas it exerts a significant siphon effect on cities within 50 km. Furthermore, LFC generates network spillovers among economically connected cities through regional digital technology transfer and cooperation networks. This paper provides empirical evidence for leveraging the demonstration effect of leading enterprises to promote the coordinated implementation of SDG 8, SDG 9, SDG 10, SDG 12 and SDG 13. Full article

Centered on lean production, this study integrates operational technologies (OT), communication technologies (CT), and information technologies (IT) within an open-system software architecture. Under stochastic customer demand, reliance on static data and experience-based decision-making constrains firms’ responsiveness to market. The integration of lean management with a data-driven database enhances operational flexibility and decision quality, enabling small and medium-sized enterprises (SMEs) in the bicycle industry to develop responsive digital factory environments with real-time monitoring and improved operational transparency. The proposed platform is applicable to both manufacturing processes and operational management, improving overall equipment effectiveness (OEE), production efficiency, process optimization, and reducing quality losses, inventory levels, and workforce misallocation. This study investigates the application of the Analytic Hierarchy Process (AHP) and multi-criteria decision-making (MCDM) within a performance framework integrating ESG indicators and a balanced scorecard to identify key success factors for digital lean improvement in the bicycle industry. A case study of a bicycle manufacturer was conducted using questionnaire surveys and expert interviews with exporters. The results indicate that the five most critical success factors are: enhancing return on invested capital, strengthening digital capabilities, improving product quality, minimizing inventory waste, and reducing lead time. These findings provide practical guidance for decision-makers in designing more effective lean management strategies in highly competitive digital markets. Furthermore, by facilitating the adoption of appropriate digital technologies under a reasonable return on investment, this approach supports the systematic implementation of Industry 4.0 initiatives and transforms traditional lean practices into more efficient and sustainable digital lean operations. Full article

The transition toward Industry 5.0 in sustainable construction demands a radical reconceptualisation of workforce development, moving beyond purely technical training to embrace human-centricity, digitalisation, green competencies, and socio-cognitive resilience. Traditional vocational and higher education systems have largely failed to bridge the gap between emerging construction industry demands and the competencies possessed by current and future professionals. This systematic review investigates how Learning Factories’ 5.0 immersive, experiential, and technology-rich educational environments can address these gaps in sustainable construction contexts. Drawing on a synthesis of 71 peer-reviewed publications spanning 2015–2026 and supplemented by targeted construction-domain literature, this study pursues three objectives: (1) identifying core competencies for Industry 5.0 readiness in sustainable construction, (2) examining how Learning Factories 5.0 support the development of these competencies, and (3) proposing a competency-driven framework for integrating Learning Factories 5.0 into sustainable construction education and training. Seven transdisciplinary competency clusters are identified—Attitude toward Digitalisation, Technical–Green Proficiency, Information and Data Literacy, Digital Security, Collaborative Systems Thinking, Adaptive Problem-Solving, and Reflective Sustainability Practice—and a theoretically derived, eight-phase Construction Learning Factory 5.0 (CLF5.0) Framework is proposed as a conceptual architecture for future empirical development and institutional adaptation. The framework is presented as a generative starting point rather than a prescriptive model, and its effectiveness in diverse construction education contexts requires empirical validation through future implementation studies. Findings reveal that while Learning Factories offer transformative potential, critical barriers remain in terms of economic feasibility, faculty development, industry–academia alignment, and empirical validation. This paper contributes a construction-specific competency architecture and implementation pathway to support the industry’s transition toward a sustainable, human-centric, and Industry 5.0-aligned future. Full article

Cross-border e-commerce fulfillment depends on coordinated inland container movements across factories, inland container depots (ICDs), and port gateways, yet many container trucking operations still follow synchronous one-truck-one-order execution. This study models the fulfillment network as a platform-enabled socio-technical transportation system in which the ICD acts as a digital–physical coordination node for spatiotemporal decoupling. A drop–buffer–pick task architecture is developed to represent direct execution, relay execution, and delayed dispatch, and a mixed-integer linear programming (MILP) model optimizes task assignment and tractor sequencing under loading-time, port cutoff, inventory, and working-time constraints. In the certified-optimal 10-order instance, gross positive cost decreases from CNY 27,540 to CNY 19,915 (−27.7%); after applying the same post hoc coordination-credit accounting rule, net total fulfillment cost decreases to CNY 18,734 (−32.0%). The 10 orders are served with five tractors under the tested platform configuration, compared with 10 tractors under the restricted benchmark. To address sustainability explicitly, the analysis also reports distance-based emissions and energy-use proxies; the proposed schedule lowers cost and fleet deployment but increases total mileage, showing that economic efficiency and emissions performance do not automatically move together. The evidence is a deterministic baseline for later stochastic, mixed import/export, and collaborative-platform extensions. Full article

This qualitative systematic review evaluates the potential of modular prefabricated OSB/plywood housing systems in low-technification, high-seismicity settings. These systems are promoted as low-carbon options for emerging contexts, and we assess how far the evidence supports that promise and under which conditions they can contribute to net-zero housing pathways. An adapted PRISMA 2020 workflow was applied to Scopus (TITLE-ABS, 2000–2025); 153 studies were synthesized in a table-first, coded matrix into axes for structural, digital fabrication, sustainability/circularity, and extrapolatable systems—supplemented by curated housing cases—with other EWPs used only for contrast. To address fragmentation and heterogeneity across domains, we developed a domain-based QA/QC instrument (STRUCTURAL, LCA, and FABRICATION) to judge whether studies provide minimally comparable evidence. Structural performance is relatively mature for certain patterns (calibrated FEM, cyclic tests, some 1:1 trials), whereas digital fabrication and LCA evidence remain partial: file-to-factory workflows rarely report verifiable QA/QC traceability, and most LCAs stop at A1–A3 with uneven treatment of A4, C/D, and biogenic carbon. Full convergence of adequate STRUCTURAL, LCA, and FABRICATION evidence within the same system type is rare, so both transferability to low-technification, seismic-prone settings and alignment with net-zero objectives must be characterized as conditional rather than established. The review identifies minimum multi-domain thresholds—technical robustness, whole-life LCA coverage, and verifiable QA/QC—as prerequisites for positioning modular OSB/plywood housing as a credible low-carbon pathway. These conclusions are limited by Scopus-only, English-language coverage and methodological heterogeneity, especially in the LCA. Full article

The rapid industrialization envisioned in Saudi Arabia’s Vision 2030 requires that new small and medium-sized enterprise (SME) factories be digitally capable from inception. This article proposes a policy framework that establishes a minimum Industry 4.0 maturity threshold as a condition for licensing new SME manufacturing facilities. Building upon international best practices and Saudi Arabia’s specific context, the article specifies minimum technical and organizational requirements across three dimensions: process, technology, and organization. Core elements include basic automation, industrial connectivity, vertical and horizontal integration, data-driven decision-making, workforce digital literacy, and leadership commitment to transformation. The rationale centers on cost efficiency, global competitiveness, and alignment with national goals. Recognizing common barriers such as limited skills, high upfront costs, and cybersecurity risks, the article outlines mitigation strategies including government incentives, public–private partnerships, and regulatory support. The framework is conceptual in nature and intended to inform pilot implementation and subsequent empirical evaluation. Establishing clear digital standards at the factory design stage can support more sustainable and scalable growth for Saudi SMEs while strengthening their readiness for participation in global Industry 4.0 ecosystems. Full article

Challenges caused by climate change increase concern for achieving global sustainability. Although citizen awareness is increasing, ensuring sustainability in key sectors like construction is necessary. Achieving sustainability requires essential actions that, however, could have a negative impact on economic performance. Studies on renewable energy installations tend to prioritize performance or sustainability, rather than facing the strategic challenge to find the balance between both. The present work fits this framework through managing renewable energy operations in a construction materials factory of Grupo Puma, located in Spain. The objective of the proposed methodology is to identify key performance indicators (KPIs) for the FV installation and to simulate energy flows using a validated model within a digital simulation environment. This study proposes a trinomial of KPIs—self-consumption, solar utilization, and avoided CO₂ emissions—as more stable indicators than conventional metrics. The Pareto front analysis shows that self-consumption can be increased by up to 20% with only an approximate 10% reduction in solar utilization. This finding offers a clear strategic recommendation: prioritizing higher self-consumption is a viable industrial strategy to enhance Grupo PUMA’s sustainability performance while maintaining acceptable economic efficiency. Full article

As the Industry 5.0 transition unfolds, engineering education must evolve to integrate Lean manufacturing with advanced digital tools and sustainable, human-centred practices. This study presents the design and implementation of a Lean Learning Factory (LLF) that addresses this challenge by combining traditional Lean methods with technologies such as simulation, robotics, and virtual reality in a modular educational environment. At the University Centre Pitești, six hands-on projects were implemented to guide students through key concepts, including production system layout, digital assistance, sustainability, and human–robot collaboration. Through experiential learning, students engage in iterative design, data analysis, and practical validation using real equipment and software platforms. The results indicate that the LLF effectively supports the development of technical, digital, transversal, and human-centred competencies aligned with EUR-ACE^® standards. Students acquire skills in process optimisation, ergonomics, and sustainable production, while also reflecting on the ethical and social implications of automation. The study concludes that the LLF model provides a scalable and adaptable framework for engineering education. It fosters competence-based learning and prepares students for the demands of Industry 5.0. This paper contributes a replicable educational approach that blends Lean efficiency, digital transformation, and human-centred values into a cohesive learning ecosystem. Full article

The accelerating integration of Artificial Intelligence (AI) in Industrial Automation has established Reinforcement Learning (RL) as a transformative paradigm for adaptive control, intelligent optimization, and autonomous decision-making in smart factories. Despite the growing literature, existing reviews often emphasize algorithmic performance or domain-specific applications, neglecting broader links between methodological evolution, technological maturity, and industrial readiness. To address this gap, this study presents a bibliometric review mapping the development of RL and Deep Reinforcement Learning (DRL) research in Industrial Automation and robotics. Following the PRISMA 2020 protocol to guide the data collection procedures and inclusion criteria, 672 peer-reviewed journal articles published between 2017 and 2026 were retrieved from Scopus, ensuring high-quality, interdisciplinary coverage. Quantitative bibliometric analyses were conducted in R using Bibliometrix and Biblioshiny, including co-authorship, co-citation, keyword co-occurrence, and thematic network analyses, to reveal collaboration patterns, influential works, and emerging research trends. Results indicate that 42% of studies employed DRL, 27% focused on Multi-Agent RL (MARL), and 31% relied on classical RL, with applications concentrated in robotic control (33%), process optimization (28%), and predictive maintenance (19%). However, only 22% of the studies reported real-world or pilot implementations, highlighting persistent challenges in scalability, safety validation, interpretability, and deployment readiness. By integrating a review with bibliometric mapping, this study provides a comprehensive taxonomy and a strategic roadmap linking theoretical RL research with practical industrial applications. This roadmap is structured across four critical dimensions: (1) Algorithmic Development (e.g., safe, explainable, and data-efficient RL), (2) Integration Technologies (e.g., digital twins and IoT), (3) Validation Maturity (from simulation to real-world pilots), and (4) Human-Centricity (addressing trust, collaboration, and workforce transition). These insights can guide researchers, engineers, and policymakers in developing scalable, safe, and human-centric RL solutions, prioritizing research directions, and informing the implementation of Industry 5.0–aligned intelligent automation systems emphasizing transparency, sustainability, and operational resilience. Full article

This article reviews the state of the art, implementation barriers, and emerging trends in industrial digitalization, drawing on studies published between 2020 and July 2025. It analyzes how classical Industry 4.0 technologies, simulation and modeling, and Industry 5.0 priorities are transforming production processes in smart factories, yielding higher productivity, reduced downtime, and improved quality. At the same time, the literature documents persistent obstacles, including system integration and interoperability, security and data-privacy risk, and financial constraints, especially for SMEs. Looking ahead, future directions point to a gradual shift towards sustainable intelligent manufacturing with human–robot collaboration and data-centric operations. In addition, the article proposes and validates a conceptual framework for the digitalization of manufacturing companies and provides practical recommendations for stakeholders seeking to leverage digital technologies for operational excellence and sustainable value creation. Full article

The growing importance of Environmental, Social, and Governance (ESG) performance across sectors has accelerated the adoption of digital technologies for the continuous monitoring, analysis, and optimization of key sustainability indicators. This study investigates the role of data-driven infrastructures—spanning hardware components (such as Smart Meters, IoT sensors, and Power Quality Analyzers) and software solutions (including Energy Management Systems, Business Intelligence, Digital Twins, and Artificial Intelligence)—in enabling more transparent, adaptive, and evidence-based ESG governance. Despite this trend, the literature remains fragmented, often focusing on isolated technologies and lacking a holistic framework. This study employs a critical literature review to address this gap, guided by the research question: How does the integration of advanced digital technologies contribute to the effective monitoring and optimization of ESG Key Performance Indicators (KPIs)? This approach allows for a comprehensive understanding of the strategic, technological, and operational dimensions involved in scaling such solutions. Findings identify two major application domains—Smart Buildings and Smart Factories—where the integration of digital tools enables a critical shift from static reporting to dynamic, real-time governance, demonstrating tangible benefits for sustainability and resilience. The study concludes by presenting an integrated framework and outlining key implications for practice and theory. Full article

With the acceleration of the Fourth Industrial Revolution, the South Korean government has promoted the Smart Factory Construction Support Project as a core strategy for the digital transformation of manufacturing, particularly targeting small and medium-sized enterprises (SMEs). While more than 35,000 smart factories had been established by 2024, systematic evidence on the policy’s economic impact remains limited. This study evaluates the effectiveness of government support for smart factories by analyzing SME financial performance between 2018 and 2021. Specifically, it investigates whether smart factory adoption—supported by government subsidies—led to improvements in sales growth and compound annual growth rate (CAGR). To address potential selection bias, propensity score matching (PSM) was employed to construct a comparable control group of non-recipient firms. The findings show that, after accounting for confounding variables, supported firms demonstrated improvements in sales-based growth metrics compared to their non-supported counterparts, thereby confirming a positive policy effect. These results provide empirical justification for sustained public investment in digital transformation initiatives, while also highlighting the importance of employing rigorous causal inference methods in policy evaluation. Full article

Order-picking is one of the most labor- and cost-intensive operations in warehouses, especially under the pressures of e-commerce growth and supply chain disruptions. Globally, order-picking accounts for 50–75% of total warehouse operating costs and nearly 55% of labor time, making it a dominant factor in logistics performance. Improving picking efficiency is therefore essential not only for reducing operational costs but also for enhancing resilience and sustainability in logistics. This study investigates the combined impact of storage space allocation and picker routing strategies on performance in a real-world edible oil factory warehouse with a three-block U-shaped layout. Three allocation policies (dedicated, turnover-based class storage, and family-based class storage) and three routing methods (S-shape, return, and midpoint) were tested in nine combinations over a five-week period. Results show that storage allocation has a stronger influence on picking efficiency than routing decisions. The family-based (Class 2) allocation with return routing achieved the lowest weekly picking time, reducing retrieval effort by concentrating items in low-level storage locations. Beyond efficiency gains, the findings highlight how simple, low-cost adjustments to storage policies can reduce picker travel, lower energy use, and support sustainable warehouse operations. This case study provides practical guidance for managers of small and medium-sized warehouses and offers baseline insights for the development of digital twin models and smart warehousing solutions in Industry 4.0. Full article

Within the framework of the 2030 Agenda, universities are key institutions in promoting digital competencies aligned with Sustainable Development Goals (SDGs), particularly SDG 4 (Quality Education) and SDG 10 (Reduced Inequalities). This study evaluates the psychometric properties of the Internet Skills Scale (ISS), adapted for Chilean university students, as a tool to assess how effectively higher education fosters digital skills that enable critical participation and social inclusion. Using a sample of 906 students from nine public universities across Chile, the ISS was linguistically and culturally adapted, and its factorial structure, reliability, validity, and measurement invariance were tested. The results support a four-factor model—operational, navigation, social, and creative skills—under a second-order structure, with strong fit indices (CFI = 0.987; RMSEA = 0.055) and high internal consistency (α > 0.83). The ISS also demonstrated gender-based measurement invariance and convergent validity with digital citizenship. These findings underscore the ISS as a valid instrument for monitoring the effectiveness and equity of digital education policies in universities. Its application contributes to diagnosing institutional performance regarding the integration of digital competencies into curricula, thus guiding improvements in educational strategies toward socially just, inclusive, and sustainable digital participation. Full article