Search Results (791)

Leveraging artificial intelligence (AI)―a cutting-edge technological tool―to drive the new quality transformation of Chinese manufacturing is a crucial foundation for China’s steady advancement of the new real economy, as well as an inevitable requirement for China to align with contemporary economic and technological trends. This study constructs a multi-sectoral equilibrium model to theoretically analyze the focal points of the new quality transformation in Chinese manufacturing and the impact AI has on it, followed by corresponding empirical tests. The results indicate that (1) AI has a positive impact on the qualitative transformation of China’s manufacturing sector; a one-unit increase in a firm’s AI level leads to a 0.171-unit increase in the sector’s qualitative transformation level. (2) This impact exhibits heterogeneity at the firm, industry, and regional levels. At the firm level, the impact varies depending on firm size, digitalization level, operational performance, internal control strength, and governance quality. At the industry level, the impact varies depending on technology intensity, industrial structure, strategic importance, and green development level. At the regional level, heterogeneity is reflected in geographical location, natural resource endowments, and the degree of urban agglomeration. (3) Artificial intelligence promotes the new quality transformation of Chinese manufacturing through the following mechanisms: reducing time lag costs and transaction costs in market penetration mechanisms; enhancing the quality of cutting-edge factor combinations and key core technologies in advanced innovation mechanisms; and improving resource utilization and operational management efficiency in lean production mechanisms. Full article

This study examines how lean manufacturing practices are adopted in Singapore’s SME precision and electronics manufacturing industry. Its main goal is to assess the extent of lean manufacturing method adoption and the challenges involved. The study analyzed 36 responses from 150 surveys distributed online. The results show that about 50% of manufacturers find it difficult to implement lean manufacturing practices. Our research reveals that most SMEs face significant challenges when applying lean manufacturing techniques. The findings identify barriers such as a lack of experience, skills, and knowledge, which significantly slow progress. Additionally, the study emphasizes that management support is vital for successful lean implementation. Key factors include employee training, goal alignment, and the creation of a supportive environment. While tools and external expertise are helpful, internal resources and organizational culture are considered more critical. Full article

Background: Obesity involves an excessive buildup of adipose tissue and is linked to chronic inflammation and oxidative stress, both of which contribute to immunosenescence. Obesity and aging share common features, including immune system impairment and oxidative and inflammatory states, suggesting that obesity may represent a model for accelerated immunosenescence. Objectives/Methods: The aim of this research was to evaluate in Zucker fatty (fa/fa) rats, a well-established genetic model of obesity, multiple immune function parameters (phagocytic activity, natural killer cell function, lymphocyte proliferation in response to mitogens, and cytokine profiles), as well as redox parameters (total antioxidant capacity, glutathione levels, activities of glutathione peroxidase and reductase, and xanthine oxidase activity) in peritoneal leukocytes, spleen, thymus, and liver at adult age (24 weeks). Comparisons were made with Zucker lean controls (fa/+), commonly used as standard controls, and Wistar rats as an independent control group. Results: Zucker fa/fa rats displayed significant physiological disorders, including increased body and organ weights, premature immunosenescence characterized by impaired innate and adaptive immune responses, reduced IL-2 and IL-10 secretion, elevated TNF-α production upon mitogen stimulation, and oxidative stress evidenced by redox imbalance in the spleen, thymus, and liver. Conclusions: These immune dysfunctions and oxidative imbalances are comparable to those observed during the aging process. Given that the immune parameters analyzed are considered indicators of health, aging rate, and longevity, our findings suggest that adult Zucker fa/fa rats could exhibit features of premature aging. Full article

Lean production philosophy and sustainability approach have become a critical framework for efficiency improvement, waste reduction, and promoting sustainable manufacturing practices. In the age of artificial intelligence (AI), there is a synergy, which has now found new dimensions, data-driven decision-making, predictive analytics, and operational agility. AI technologies promise to transform industrial processes by converging lean production and sustainability principles, a synergy explored in this paper. AI APIs enable the use of AI to improve resource utilization, reduce environmental pressure, and maintain economic growth inherent to all business sectors while also fostering social accountability. In this study, a robust regression model is employed to study the role of AI in moderating the lean practices and sustainability outcomes relationship, using a sample of 528 manufacturing firms. The results show that the contribution of AI technologies to economic, ecological, and social sustainability is effectively multiplied by that of lean production. This research offers a framework to help practitioners and policymakers optimize production systems in line with Sustainable Development Goals. Finally, the study delivers actionable recommendations for navigating skill gaps and cybersecurity risks that were identified. In sum, this paper contributes to the rapidly emerging conversation by providing empirical evidence on AI’s moderating role in the lean–sustainability relationship and offering a strategic framework for practitioners. Full article

This study investigates the application of the SMED (Single-Minute Exchange of Die) methodology to improve operational efficiency and support energy- and resource-efficient manufacturing systems. The research is based on a case study conducted in an automotive company producing electrical harnesses, where SMED was implemented to optimize changeover processes and reduce process-related inefficiencies. The methodological approach follows an AS-IS to TO-BE framework, incorporating direct observation, time measurements, and the classification of activities into internal and external operations. In addition to operational indicators, selected energy- and resource-related aspects such as energy consumption during changeovers, material usage, and waste generation were evaluated based on process observation and indirect estimation. The results indicate a significant reduction in changeover time, along with improvements in machine availability and production flow. Furthermore, the study suggests a reduction in process-related energy consumption and material intensity associated with improved organization and reduced downtime, although these effects are partially indirect. The findings demonstrate that SMED can enhance operational efficiency and indicate its potential to improve energy performance in manufacturing systems, primarily through reduced machine downtime and more stable production flows. However, the results are case-specific, and further research based on direct energy measurements and broader industrial applications is required to confirm their generalizability. Full article

Small and medium-sized enterprises (SMEs) are expected to contribute to sustainable manufacturing, yet they often lack the resources and capabilities needed to adopt advanced automation in a structured and scalable manner. While lean robotics have been widely studied, there is still limited empirical evidence on how their integration can be systematically designed to improve sustainability-oriented performance in SME contexts. This paper examines how a scalable lean robotics system can be conceived and implemented to enhance productivity and resource efficiency in an SME packaging process. We develop a lean robotics design approach that jointly considers lean principles, collaborative industrial robotics, and Industrial Internet of Things (IIoT) monitoring. The approach is applied in a real-world case study of a “Fold Station” robotic cell, where stone paper sheets are destacked, glued, and formed into cylindrical plant protectors. Key performance indicators related to cycle time, material utilization, process stability, and manual workload are measured before and after implementation. The results show a three- to four-fold reduction in preparation time per unit, more efficient use of stone paper and adhesive, and a decrease in repetitive manual handling, thereby contributing to both economic and environmental sustainability. TRIZ (Teoriya Resheniya Izobretatelskikh Zadach, Theory of Inventive Problem Solving) is used to structure the resolution of design contradictions that arise when embedding lean principles into the robotic system and to support its scalable adaptation to different production scenarios. This study advances the understanding of lean robotics for sustainable SME production and derives practical guidelines for designing scalable, resource-efficient robotic cells. Full article

Lean Construction has become a key strategy for improving productivity, reducing waste, and increasing efficiency in civil engineering projects. In parallel, advances in digital technologies have transformed the way engineering design and project planning processes are conceived and managed. However, there remains a limited systematic understanding of how emerging technologies support engineering design practices and influence the implementation and performance of Lean Construction in diverse civil engineering scenarios. This study presents a systematic literature review of 70 peer-reviewed articles published between 2019 and 2025, following the PRISMA 2020 guidelines. The selected studies were examined using a structured classification framework consisting of three analytical categories: Technologies and Tools, Construction Methods and Sustainability, and Production Philosophies and Management. From an engineering design perspective, this framework allows the identification of technological trends, design-support tools, and management strategies that influence the planning, modeling, and optimization of construction processes. The results show that digital technologies, such as Building Information Modeling (BIM), automation systems, Artificial Intelligence, and Industry 4.0 tools, play a significant role in supporting engineering design activities by improving project visualization, coordination, and decision-making during the design and planning stages. These technologies contribute to more integrated design processes aligned with Lean Construction principles. At the same time, the analysis reveals that the adoption of Lean Construction technologies varies depending on project characteristics, levels of digital maturity, and regional industry conditions. The main barriers identified in the literature include interoperability limitations, insufficient workforce training, and organizational resistance to technological change. Overall, the review provides a structured synthesis of recent research trends and highlights the technological and managerial factors that influence the successful integration of Lean Construction with engineering design practices in civil engineering. The findings contribute to bridging the gap between technological innovation, design methodologies, and Lean Construction implementation, offering insights for both researchers and practitioners seeking to improve efficiency, sustainability, and design performance in construction projects. Full article

Occupational Health and Safety systems, as well as physical Ergonomics, serve a common goal, which is to eliminate safety-related injuries within production systems. The analysis of potential hazards that could compromise the safety of operations’ employees assists in preventing a high rate of safety-related injuries. Safer processes result in a high output rate and, hence, a profitable business. Focusing on the accuracy of problem solving and failure prediction analysis on new processes could potentially result in zero safety-related injuries, good-quality products, cost reduction, and the elimination of delays within the processes. This research seeks to add more knowledge to the fields of Occupational Health and Safety systems and Total Productive Maintenance by combining lean manufacturing troubleshooting models with Ergonomic analysis, as well as Hazard Identification Risk Analysis, to predict future kaizen projects for businesses. The proposed upgrade to the problem-solving model was developed by evaluating and reviewing the impact of Ergonomic analysis on different production systems. It was found that Ergonomic analysis provides solutions for a more comfortable working environment; hence, the existing troubleshooting model was combined with an Ergonomic exercise. The proposed model is more beneficial to production systems. It could potentially result in zero safety-related injuries, high-quality products, more accurate problem analysis, and more innovation by enabling kaizen projects. The proposed model was applied in the electronics industry, where it resulted in drastic improvements. The old method, which was causing fatigue, was eliminated, and a new machine was designed and prototyped. The new machine assisted the company in this case study in reducing delays, eliminating defects, and reducing costs. Furthermore, the proposed troubleshooting model evaluated an impactful kaizen project, which was the introduction of new technologies that will eliminate the power-up stage. Full article

The socks, hosiery, bodywear (SHB) industry is a critical segment of the textile sector, characterized by high-volume production and rapid delivery requirements, making efficiency and resource optimization essential. A corporate sustainability system is needed to minimize environmental impact, ensure long-term competitiveness, and align operations with global sustainability standards. Thus, this research aims to propose an integrated Corporate Sustainability System (CSS) framework that synergizes Lean Manufacturing (LM), Digital Transformation (DT), and sustainability transition through a methodological triangulation of (1) a narrative review, (2) in-depth expert interviews, and (3) a comprehensive Turkish case study. The proposed framework integrates foundational lean principles such as 5S, TPM, and Value Stream Mapping with Industry 4.0 technologies, including RFID traceability, real-time ERP integration and machine vision systems. Empirical demonstration through the case study reveals that establishing foundational lean maturity is a critical foundation for successful digital adoption. Furthermore, the study demonstrates that transitioning from manual tracking to integrated digital platforms resolves data silos and enhances the transparency of customer revisions and warehouse accuracy. The framework also incorporates human-centric Lean 5.0 improvements, proving that ergonomic interventions such as rail-mounted cable systems are vital for operational sustainability. Ultimately, the CSS provides a scalable model that aligns SHB production with global mandates like the EU Green Deal and CBAM, positioning the sector for long-term competitive advantage in an increasingly eco-conscious global market. Full article

Large-scale composite equipment manufacturing imposes stringent requirements on the lean management of multi-specification fiber prepreg sheet storage, while existing optimization methods suffer from poor process adaptability, insufficient multi-objective collaborative optimization capability, and low space utilization of static layouts. This study constructs a symmetric optimization framework for multi-objective composite sheet storage to address these critical bottlenecks. Specifically, the multi-dimensional process value of fiber sheets is quantified, and the layered storage optimization problem is transformed into a 0–1 knapsack problem with symmetric constraints. An improved Dynamic Programming–Backtracking (DP-BT) material selection algorithm and an adaptive dynamic programming iterative space optimization algorithm are proposed to achieve a symmetric balance of inter-layer space utilization and global optimization. Experimental validation with actual production data of 17 fiber sheet types verifies that the proposed method enables space optimization for specified layer counts to maximize average space utilization, with the rate rising from 79.4% (initial 4-layer layout) to 95.7% (3-layer) and 99.9% (2-layer), and a peak single-layer utilization of 100%. This framework achieves favorable optimization performance in the target production scenario and provides a referenceable symmetric optimization approach for the lean storage management of similar fiber sheet storage scenarios in composite manufacturing. Full article

Live-streaming commerce has become a routine channel for merchants, and streamers’ nonverbal cues are closely associated with consumer responses and conversion. Drawing on real live-streaming settings, this study examined the relationship between streamers’ nonverbal cues and consumer purchase behavior, and further tested whether immersion, as reflected by average watch time, helped explain this relationship. Building on Social Cognitive Theory, we constructed a multimodal dataset of 4600 product-presentation segments from 546 live sessions. Using an automated computer-vision-based framework, we measured segment-level nonverbal behaviors, including nodding frequency, gesture intensity, postural movement intensity, forward lean, and camera proximity. We then examined how these nonverbal cues were associated with consumer purchase behavior and through what mechanisms in live-streaming settings. The results showed that each nonverbal cue was positively and significantly associated with consumer purchase behavior. Mediation tests further indicated that immersion significantly helped explain the relationships between nonverbal cues and consumer purchase behavior. From a process perspective, this study extends the range of constructs examined in live-streaming commerce and clarifies how nonverbal communication is associated with outcomes, offering practical implications for streamer training, camera setup, and content design. Full article

This study proposes a real-time data-based Digital Value Stream Mapping (Digital VSM) framework that integrates Artificial Intelligence (AI) feature selection and discrete-event simulation validation to enhance production system performance. Unlike conventional VSM approaches that rely on static, manually aggregated data, the proposed framework uses real-time operational data to dynamically quantify Value Added (VA), Non-Value Added (NVA), and Necessary Non-Value Added (NNVA) activities. To improve decision accuracy, an Artificial Neural Network (ANN) combined with Genetic Algorithm (GA) feature selection is employed to identify dominant production variables influencing lead time and line imbalance. Furthermore, Ranked Positional Weight (RPW) optimization results are validated through Tecnomatix Plant Simulation to ensure robustness before physical implementation. The proposed framework was applied to a discrete manufacturing line, resulting in a reduction of total lead time from 8755 s to 6400 s and an increase in process ratio from 33.64% to 45.91%, with line efficiency reaching 91.7%. The findings demonstrate that integrating Digital VSM with AI-driven feature selection and simulation validation transforms Lean analysis from a descriptive tool into a predictive and validated decision-support system suitable for Industry 4.0 environments. Full article

Environmental transparency in food manufacturing requires efficient carbon footprint data collection, yet multi-departmental coordination often creates time-consuming, fragmented processes that impede adoption. This study applies lean office methodologies to optimize carbon footprint assessment processes in food manufacturing. Using a case study approach at a Thai food processing facility, we implemented flow process charts, value stream mapping, eight waste analysis, and ECRS methodology to evaluate the data collection process for strawberry jam production. The baseline assessment documented 142 activities across 12 departments, requiring 17,540 min. The lean interventions included establishing a centralized cross-functional team, developing standardized data collection templates, implementing a unified digital repository system, and consolidating redundant verification procedures. The improved process reduced activities from 142 to 63, decreased the required time from 17,540 to 11,190 min (36.2% reduction), and eliminated 95.8% of non-value-added activities while maintaining regulatory compliance. These efficiency gains enable more frequent environmental assessments and facilitate the broader adoption of carbon footprint measurement within resource-constrained manufacturing contexts. The study demonstrates that lean principles effectively optimize environmental assessment processes themselves, providing a replicable framework adaptable across diverse food manufacturing facilities and product lines while addressing critical adoption barriers including resource constraints and administrative complexity. Full article

Data suggest that both pancreatic and intestinally produced glucagon-like peptide-1 (GLP-1) increases in response to inflammation. Here, we set out to determine the tissue-specific function of increased GLP-1 during inflammatory stimuli. Using our innovative mouse model of tissue-specific Gcg (the gene that encodes GLP-1) expression, we explored the function of GLP-1 under severe inflammatory conditions induced by lipopolysaccharide (LPS) administration in lean and obese mice. High-fat diet (HFD) increased the LPS-induced suppression of feeding and increased the plasma levels of pro-inflammatory cytokines and GLP-1. Both pancreatic and intestinal Gcg expression contribute to LPS-induced increases in GLP-1, but Gcg was not necessary for the glucoregulatory or suppressed feeding responses to LPS. While Gcg was not necessary for systemic cytokine increases with LPS in either chow- or HFD-fed mice, whole-body Gcg-null animals had increased macrophage accumulation and an increased expression of genes reflecting pro-inflammatory signaling in the pancreas. We then performed flow cytometry on the pancreas from mice expressing a fluorescent marker on the GLP-1 receptor (GLP-1R). In response to LPS, we found that pancreatic CD64+/CD11b+ macrophages expressed the GLP-1R. We conclude that under severe inflammatory conditions, pancreatic production of GLP-1 functions in an immunological rather than a metabolic role to directly regulate local macrophage accumulation. Full article

In this study, shale samples with diverse lithofacies from the Lower Silurian Longmaxi Formation in the Fuling Field were investigated to evaluate the variations in pore characteristics and methane adsorption capacity (MAC) of different shale lithofacies. A set of experiments were performed, such as total organic carbon (TOC) content, X-ray diffraction (XRD), field emission–scanning electron microscopy (FE-SEM), low-pressure gas (CO₂/N₂) adsorption, and high-pressure methane adsorption. Combined with TOC content and mineral composition, three types of shale lithofacies were identified, including organic-rich (OR) argillaceous-rich siliceous (S-3) shale lithofacies, organic-moderate (OM) argillaceous/siliceous mixed (M-2) shale lithofacies, and organic-lean (OL) siliceous-rich argillaceous (CM-1) shale lithofacies. Through detailed comparative analyses, we found that OR S-3 shales possess the maximum TOC content, the most developed heterogeneous organic micro-mesopores, the largest pore volume (PV), and the highest pore surface area (PSA); consequently, they display the strongest MAC. Conversely, OL CM-1 shales have the lowest TOC content and the highest clay content, and thus the smallest PSA and the poorest methane adsorption performance. In conclusion, considering the excellent gas storage potential, sustained shale gas production, and brittle response to hydraulic fracturing, OR S-3 shales are superior to shale gas exploration and exploitation compared with OM M-2 and OL CM-1 shales. Full article