Search Results (215)

Alkaline water is increasingly marketed for musculoskeletal and recovery benefits, yet its relevance to healthy aging, sarcopenia prevention, and functional capacity in older adults remains largely unexplored. This systematic evidence map and translational appraisal examined whether the available comparative human evidence on alkaline water is applicable to aging populations and longevity research. Following PRISMA guidance, PubMed and Scopus were searched from January 2005 to September 2025. Eligible studies were controlled or comparative observational human studies reporting muscle strength, physical performance, or recovery outcomes. Risk of bias was assessed using RoB 2, ROBINS-I, and JBI criteria; evidence certainty was judged narratively using GRADE-informed principles. Ten studies met the inclusion criteria. Most enrolled young athletic populations; only two had partial relevance to aging cohorts. Crucially, no study included participants aged 65 years or older or assessed primary sarcopenia-relevant endpoints such as appendicular lean mass, gait speed, or chair-rise performance; this total absence of data in the target demographic represents the central limitation of the current literature. Risk of bias ranged from some concerns to serious. The most consistent signals were short-term improvements in lactate clearance and perceived exertion in young male athletes. Evidence for strength, functional performance, and safety in older adults was absent or indirect. Current evidence, rated low to very low certainty for aging-relevant outcomes, does not support alkaline water as an evidence-based strategy for healthy aging or muscle preservation in older adults. Age-appropriate trials using EWGSOP2-aligned outcomes are urgently needed. Full article

With the 2030 deadline for the United Nations Sustainable Development Goals (SDGs) approaching, there is a growing global urgency to identify innovative, scalable, and inclusive AI-based or AI-enabled solutions capable of accelerating progress across sectors. Yet the benefits of AI remain unevenly distributed, particularly in low-resource settings where limited infrastructure, cost barriers, and unequal access to skills constrain adoption. This paper explores how Tiny Machine Learning (TinyML)—a low-power, low-cost edge AI paradigm—offers a concrete technological pathway aligned with the principles of Frugal AI, providing accessible, energy-efficient, and context-adapted tools for sustainable development. We evaluate how participatory citizen science, when combined with TinyML, enables communities to co-create AI applications that address locally defined challenges in environmental monitoring, agriculture, and public health. Drawing on early outcomes from workshops, collaborative projects, and innovation competitions, the paper examines how TinyML-enabled participatory approaches cultivate technical skills, stimulate grassroots entrepreneurship, and generate prototypes suited to low-resource environments. Using a qualitative multiple-case study of 50 participatory TinyML initiatives across 22 countries, we analyse how frugal edge-AI practices support skills formation, prototype development, and early entrepreneurial engagement. The analysis identifies the pedagogical, technical, and institutional frameworks that support successful participatory AI initiatives, emphasizing open educational resources, cross-sector partnerships, and community-driven problem formulation. We introduce the Frugal Edge AI Lean Canvas to help innovators identify novelty, ethical implications, and measurable impact. TinyML-based participatory innovation offers a promising route for accelerating SDG progress by expanding who can create, deploy, and benefit from AI. 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

Outcome-Based Engineering Education (OBEE), a performance-driven approach at the forefront of curriculum design, offers a reliable and scalable framework for reforming engineering education. This research examines the industrial and logistics engineering major at the National School of Applied Sciences of Marrakesh as a case study to develop and implement a new hybrid model that merges the OBEE approach and Lean Management principles and methods through five layers. This paper presents the second and third layers of the Lean-OBEE architecture: the Target layer and Assessment layer, respectively. The target layer employs Hoshin Kanri’s X-Matrix in the OBEE process as a Lean strategic planning tool for visual and efficient management of the educational outcomes. Teachers and academic staff used the X-Matrix to monitor the unfolding of strategic educational objectives and progress throughout the course and curriculum. The assessment layer integrates a set of Lean principles, including PDCA (Plan-Do-Check-Act) cycles, Poka-Yoke, Flow, Muri, Standard Work, Takt Time, and Collective Intelligence, to design and assess the course session. The findings of this study provide preliminary evidence that the proposed Lean-OBEE model supports the development of sustainable engineering education by continuously improving the relevance and efficiency of the curriculum and teaching practices to meet the dynamic needs of industry and all stakeholders. This study serves as a practical reference for achieving the stated outcomes. Full article

The ambition of policymakers to ‘raise aspirations’ among young people from disadvantaged backgrounds as a means for improving social mobility in Britain has been a mainstay of political rhetoric for the last three decades. Reports such as Higher Education in the Learning Society in 1997, Unleashing Aspiration in 2009, and Success as a Knowledge Economy in 2016 are all underpinned by an ideology of neoliberal meritocracy that has transcended political parties and governments since the Thatcher administration. Even those who lean more to the left of the Labour Party within contemporary Britain have perpetuated this narrative by reframing it as ‘working-class ambition’. This paper advances an alternative view which reconceptualises the way in which young people from non-privileged backgrounds experience and perceive the world, and their place within it. Drawing upon our work on Connected Belonging in 2025 and our research on the From the Centre to the Periphery project in 2025, we suggest that ‘hopeful optimism’ offers a more realistic lens through which to understand what is needed to address the ‘personal troubles and public issues’ that young people face. Unlike aspiration, which has an inherently individualistic and future-orientated framing, with value systems directed by dominant hegemonic notions of ‘success’ that are commonly positioned in economic terms, we recognise optimism as being a holistic and relational process that resides in the present as well as looks to the future. Optimism, grounded within principles of hope, allows young people the freedom to be and to dream; by celebrating who they are and their interconnectedness, it protects them from fears of failure; by reimaging what success might mean, it liberates them as creators. 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

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

Experimental Governance (EG) has emerged as a strategic framework for managing complexity in high-uncertainty environments. However, its application in the private sector remains fragmented, often conflated with purely operational tools. This study addresses this gap by performing a conceptual transfer of EG principles into the domain of business processes. Through an expanded Systematic Literature Review (SLR) of 41 peer-reviewed articles (covering the period 2004–2026), we identify what we term the ‘Internalization Paradox’: while firms rapidly adopt experimental methodologies like Agile or Lean, they often fail to embed them into formal governance structures that ensure long-term accountability and institutional learning. This updated review incorporates cutting-edge discussions on Artificial Intelligence (AI) governance, experimentalist metagovernance, and the strategic regulation of uncertainty. Our findings suggest that organizational resilience is not merely a byproduct of technological readiness, but an emergence of ‘Institutionalized Experimentalism’. We propose a Conceptual Framework that operationalizes EG through iterative feedback loops, corporate sandboxes, and adaptive decision rights, providing a robust roadmap for future empirical research in management and organizational theory. Full article

The topology identification (TI) of low-voltage distribution networks (LVDNs) is the foundation for their intelligent operation and lean management. However, the existing identification methods may produce inconsistent results under measurement noise, missing data, and heterogeneous load behaviors. Without principled multiple method fusion and meter-level confidence quantification, the reliability of the identification results is questionable in the absence of ground-truth topology. To address these challenges, a confidence-aware TI (Ca-TI) method for the LVDN based on weakly supervised learning (WSL) and Dempster–Shafer (D-S) evidence theory is proposed, aiming to infer each meter’s latent topology connectivity label and quantify the meter-level confidence without ground truth by fusing different identification methods. Specifically, within the framework of data programming (DP) in WSL, different TI methods were modeled as labeling functions (LFs), and a weakly supervised label model (WSLM) was adopted to learn each method’s error pattern and each meter’s posterior responsibility; within the framework of D-S evidence theory, an uncertainty-aware basic probability assignment (BPA) was constructed from each meter’s posterior responsibility, with posterior uncertainty allocated to ignorance, and was further discounted according to the missing data rate; subsequently, a consensus-calibrated conflict-gated (CCCG)-enhanced D-S fusion rule was proposed to aggregate the TI results of multiple methods, producing the final TI decisions with meter-level confidence. Finally, the test was carried out in both simulated and actual low-voltage distribution transformer areas (LVDTAs), and the robustness of the proposed method under various measurement noise and missing data was tested. The results indicate that the proposed method can effectively integrate the performances of various TI methods, is not adversely affected by extreme bias from any single method, and provides the meter-level confidence for targeted on-site verification. Further, an engineering deployment scheme with cloud–edge collaboration is further discussed to support scalable implementation in utility environments. Full article

Groundwater management increasingly relies on participatory governance, yet most existing participatory frameworks lack mechanisms for iterative learning and continuous improvement and further lack structured operational indicators, systematic monitoring–feedback integration, and institutionalized mechanisms that embed participation within measurable governance cycles rather than treating it as a one-time procedural input. Conversely, Lean thinking, particularly the Plan–Do–Check–Act (PDCA)-based continuous improvement principles, offers systematic methods for feedback and adaptation, but remains underexplored in environmental governance contexts. This paper bridges these traditions by conceptualizing participatory groundwater governance as a continuous improvement system, thus aligning community participation with PDCA logic in order to enhance adaptive management and sustainability outcomes. This study introduces a novel conceptual synthesis that integrates Lean management principles into participatory groundwater governance. In the current research, a methodological framework is proposed for integrating Lean thinking, particularly the Plan–Do–Check–Act cycle, with participatory groundwater governance, thus producing a Lean–participatory groundwater governance (Lean–PGG) framework. To conceptualize the framework, a set of eight rubric-based indicators was developed from a literature matrix of 54 peer-reviewed case studies selected through predefined inclusion criteria and multi-stage screening procedures, in order to evaluate participation, governance readiness, tool application, data use, monitoring, learning, and institutionalization. Each variable indicator was then scored on a three-point scale and categorized into the PDCA maturity levels The findings suggest a consistent heuristic trend across cases, characterized by comparatively stronger performance in the planning and implementation stages. A clear majority of studies scored in the moderate-to-high range (≥2.5/3) for the Plan and Do dimensions, whereas only a limited proportion demonstrated structured Check mechanisms and fewer still exhibited institutionalized Act processes. This asymmetry indicates persistent gaps in the consolidation of evaluation and feedback within participatory groundwater governance systems. This Lean–PGG framework thus demonstrates how continuous improvement mechanisms, i.e., feedback loops, reflection, and adaptive standardization, can strengthen participatory groundwater governance. The proposed framework offers a replicable and practical model for integrating continuous improvement into environmental and groundwater governance, fostering adaptive management, resource efficiency, and sustainability outcomes. 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

Due to the significant challenges faced by healthcare systems, medical establishments strive to set the tone by integrating new concepts to bridge this gap. Here, Lean Healthcare (LH) has been inspired by Lean Management (LM). Utilizing LM to optimize industrial processes and reduce waste presented a real opportunity to enhance the quality of medical services. For more improvement, healthcare systems pushed themselves to keep up with progress by implementing Industry 4.0 (I4.0) tools, such as IoT, Big Data analytics, and AI with LH and sustainability practices. The results promised better quality of care. Although this concept offers significant potential for more efficient workflows and optimizing medical processes, studies examining their combined implementation are still scarce. This research fills the gap via a literature review (LR) of peer-reviewed articles published between 2015 and 2025. The review investigates the impact of integrating smart technologies into LH frameworks and highlights how LH contributes to sustainability across multiple dimensions: economic, social, technological and environmental. Key findings show the impact of combining advanced tools with lean principles by reducing waiting times (25%) and length of stay while also improving satisfaction. Sustainability-centered adaptations of LH incorporate social and environmental comparative parameters such as resource consumption, for instance, reducing operational costs by up to 30–40%. Many challenges were faced with this implementation, such as cultural, technical challenges (e.g., complexity of integration with digital systems), and sustainability barriers. However, to overcome these barriers, this paper proposes a holistic implementation that aligns lean processes with organizational change and sustainability goals. Full article

This study addresses a critical gap in understanding how Lean Manufacturing (LM) practices, particularly people-centered approaches, can enhance operational performance within the unique industrial context of Saudi Arabia’s Vision 2030 economic transformation. The concept of Lean Manufacturing involves a systematic approach and principles aimed at enhancing efficiency, minimizing inefficiencies, and boosting output in manufacturing operations. While LM principles are well-established globally, their application in Gulf Cooperation Council (GCC) economies remains understudied, particularly regarding the central role of workforce engagement in successful implementation. The main objective of the study is to investigate the implications of LM on the productivity of the industry sector. Specifically, this research examines how the integration of people-centered practices with traditional LM constructs (Just-in-Time, Jidoka, Stability and Standardization) influences operational outcomes in Saudi manufacturing firms. A survey was conducted among specific private and public enterprises to collect data, yielding a 55.8% response rate and 67 valuable responses from a pool of 120 contacted companies. The sample encompassed small, medium, and large enterprises across seven manufacturing sectors. SmartPLS 3 and SPSS were used to assess the structural and measurement models. Common method bias was evaluated using Harman’s single-factor test. The findings demonstrate that implementing the recommended LM structural model significantly enhances operational performance. Notably, people integration exhibited the strongest influence on operational performance (β = 0.361), suggesting that human-centered approaches may be particularly salient in the Saudi context. These findings offer practical guidance for manufacturing firms seeking to align lean initiatives with Vision 2030 objectives. Full article

Additive manufacturing (AM) has been increasingly explored in the construction sector for its potential to improve productivity, reduce waste, and enable design flexibility; however, reported outcomes remain inconsistent, and the relationship between AM and Lean Construction (LC) principles is not yet clearly established. This study addresses this gap through an exploratory, theory-building systematic review of 12 peer-reviewed research articles published between 2021 and 2025, examining AM technologies applied in construction, their associated application contexts, Lean principles, performance indicators, and implementation barriers. A mixed quantitative and qualitative analysis was conducted, combining descriptive bibliometric mapping with thematic synthesis to answer three research questions related to AM applications, Lean impacts, and performance measurement. Given the emerging nature of AM–LC integration and the limited number of eligible studies, the review prioritizes conceptual synthesis over empirical generalization. The results suggest that AM contributes primarily to waste reduction, process efficiency, standardization, and built-in quality when integrated with complementary digital and automation technologies. Nevertheless, significant technical, economic, socio-organizational, and regulatory barriers persist, limiting scalability and performance consistency. Based on the synthesized evidence, the study proposes a conceptual framework that interprets AM adoption as a Lean-oriented production system, where barriers act as system-level constraints and enablers function as Lean improvement mechanisms. This study further conceptualizes AM implementation as a Kaikaku-driven transformation that requires Kaizen-based stabilization through established LC tools. These insights contribute to advancing theoretical understanding of AM–LC integration and guide more effective and systematic implementation in construction projects. Full article