Search Results (487)

Green manufacturing has emerged as a critical objective in the evolution of advanced production systems. Although digital twin technology is widely recognized for enhancing efficiency and promoting sustainability, the majority of existing research focuses exclusively on physical systems. They neglect the impact of soft systems, including human behavior, decision-making, and operational strategies. To address this limitation, the present study introduces an innovative hybrid digital twin model that integrates both physical and soft systems to support green manufacturing initiatives comprehensively. The primary contributions of this work are threefold. First, a novel hybrid architecture is developed by coupling real-time physical data with virtual soft system components that simulate factory operations. Second, lean production principles are systematically incorporated into the soft system, thereby facilitating reduced energy consumption and minimizing environmental impact. Third, a parameter-driven programming model is formulated to correlate critical variables with green performance metrics, and a genetic algorithm is utilized to optimize these variables, ultimately enhancing sustainability outcomes. This integrated approach not only expands the applicability of digital twin technology but also offers a data-driven decision-support tool for the advancement of green manufacturing practices. Full article

In manufacturing environments, the proper use of Personal Protective Equipment (PPE) is essential to prevent workplace accidents. Despite this need, existing PPE monitoring methods remain largely manual and suffer from limited coverage, significant errors, and inefficiencies. This article focuses on addressing this deficiency by designing a computer vision desktop application for automated monitoring of PPE use. This system uses lightweight YOLOv8 models, developed to run on the local system and operate even in industrial locations with limited network connectivity. Using a Lean UX approach, the development of the system involved creating empathy maps, assumptions, product backlog, followed by high-fidelity prototype interface components. C4 and physical diagrams helped define the system architecture to facilitate modifiability, scalability, and maintainability. Usability was verified using the System Usability Scale (SUS), with a score of 87.6/100 indicating “excellent” usability. The findings demonstrate that a user-centered design approach, considering user experience and technical flexibility, can significantly advance the utility and adoption of AI-based safety tools, especially in small- and medium-sized manufacturing operations. This article delivers a validated and user-centered design solution for implementing machine vision systems into manufacturing safety processes, simplifying the complexities of utilizing advanced AI technologies and their practical application in resource-limited environments. Full article

The main thrust of the study is the need to cut down the time taken for mold changes in plastic injection molding which is fundamental to the productivity and efficiency of the process. The research encompasses Lean Manufacturing, DMAIC, and SMED which are improved using fuzzy logic and AI for rapid changeover optimization on the NEGRI BOSSI 650 machine. A decrease in downtime by 65% and an improvement in the Process Cycle Efficiency by 46.8% followed the identification of bottlenecks, externalizing tasks, and streamlining workflows. AI-driven analysis could make on-the-fly adjustments, which would ensure that resources are better allocated, and thus sustainable performance is maintained. The findings highlight how integrating Lean methods with advanced technologies enhances operational agility and competitiveness, offering a scalable model for continuous improvement in industrial settings. Full article

Operations management personnel emphasize the maintenance of workforce empowerment on the shop floor. This is made possible by implementing effective operations and human resource management practices. However, organizations are adept at controlling the workforce empowerment domain within operational scenarios. In the current industry revolution scenario, industry personnel often face failure due to a laggard mindset in the face of industry revolutions. There are higher possibilities of failure because of standardized operations controlling the shop floor. Organizations utilize well-established human resource concepts, including McClelland’s acquired needs theory, Herzberg’s two-factor theory, and Maslow’s hierarchy of needs, in order to enhance the workforce’s performance on the shop floor. Current SME individuals require fast-paced approaches for tracking the performance and idleness of a workforce in order to control them more efficiently in both flexible and transformational stages. The present study focuses on investigating the parameters and factors that contribute to workforce empowerment in an industrial revolution scenario. The present research is used to develop a framework utilizing operations and human resource management approaches in order to identify and address the issues responsible for deteriorating workforce contributions. The framework includes HRM and operations management practices, including Herzberg’s two-factor theory, Maslow’s theory, and lean and smart approaches. The developed framework contains four phases for achieving desired outcomes on the shop floor. The developed framework is validated by implementing it in a real-life electric vehicle manufacturing organization, where the human resources and operations team were exhausted and looking to resolve employee-related issues instantly and establish a sustainable work environment. The current industry is transforming from Industry 3.0 to Industry 4.0, and seeks future-ready innovations in operations, control, and monitoring of shop floor setups. The operations management and human resource management practices teams reviewed the results over the next three months after the implementation of the developed framework. The results revealed an improvement in workforce empowerment within the existing work environment, as evidenced by reductions in the number of absentees, resignations, transfer requests, and medical issues, by 30.35%, 94.44%, 95.65%, and 93.33%, respectively. A few studies have been conducted on workforce empowerment by controlling shop floor scenarios through modifications in operations and human resource management strategies. The results of this study can be used to fulfil manufacturers’ needs within confined constraints and provide guidelines for efficiently controlling workforce performance on the shop floor. Constraints refer to barriers that have been decided, including production time, working time, asset availability, resource availability, and organizational policy. The study proposes a decision-making plan for enhancing shop floor performance by providing suitable guidelines and an action plan, taking into account both workforce and operational performance. Full article

Predicting the blow-off (BO) is critical for characterising the operability limits of gas turbine engines. In this study, the applicability of a low-order extinction prediction modelling, which is based on a stochastic variant of the Imperfectly Stirred Reactor (ISR) approach, to predict the lean blow-off (LBO) curve and the extinction conditions in a hydrogen Rich-Quench-Lean (RQL)-like swirl combustor is investigated. The model predicts the blow-off scalar dissipation rate (SDR), which is then extrapolated using Reynolds-Averaged Navier–Stokes (RANS) cold-flow simulations and simple scaling laws, to determine the critical blow-off conditions. It has been found that the sISR modelling framework can predict the BO flow split ratio at different global equivalence ratios, showing a reasonable agreement with the experimental data. This further validates sISR as an efficient low-order modelling flame extinction tool, which can significantly contribute to the development of robust hydrogen RQL combustors by enabling the rapid exploration of combustor operability during the preliminary design phases. Full article

This paper analyzed the importance of applying modern concepts and tools for monitoring production processes in order to improve effectiveness, efficiency, and sustainable manufacturing. The aim of the study was to develop and apply a stochastic model based on a modified real-time observation method to optimize production cycles in the metalworking industry. The research was conducted over several years in real industrial conditions using instantaneous observations, and the collected data were statistically analyzed using control charts and flow coefficient functions. The results showed a significant reduction in production cycle times and improved efficiency, particularly when the batch size was optimized to 10 units. The analyzed working time elements and flow coefficients enabled a comprehensive analysis and influenced trends in subsequent years, thereby improving production management. A comparative analysis of the results reveals a downward trend in average PC time per unit over the years—56.2, 37.7, 31.5, and 44.8 min from phases I to IV—until the introduction of a new operation. The corresponding flow coefficient functions are y₁ = 297.54/x + 2; y₂ = 239/x − 7.36; y₃ = 192/x + 0.65; and y₄ = 438.2/x − 11.3. These findings suggest that the optimal batch size for the enterprise under consideration is 10 units. The findings confirmed that the integration of Lean principles and Industry 4.0 methods contributes to the reduction of non-productive time and better process control. The study provided a simple and effective model for cycle time optimization that can be implemented even in small and medium-sized enterprises. Full article

The construction sector significantly contributes to global environmental degradation through intensive resource extraction, high energy consumption, and substantial waste generation. Addressing this unsustainable trajectory requires integrated approaches that simultaneously improve operational efficiency and material circularity. Lean Construction (LC) and Circular Economy (CE) offer complementary frameworks for enhancing process performance and reducing environmental impacts. However, their combined implementation remains underdeveloped and fragmented. This study conducts a systematic literature review (SLR) of 18 peer-reviewed articles published between 2010 and 2025, selected using PRISMA 2020 guidelines and sourced from Scopus and Web of Science databases. A mixed-method approach combines bibliometric mapping and qualitative content analysis to investigate how LC and CE are jointly operationalized in construction contexts. The findings reveal that LC improves cost, time, and workflow reliability, while CE enables reuse, modularity, and lifecycle extension. Integration is further supported by digital tools—such as Building Information Modelling (BIM), Design for Manufacture and Assembly (DfMA), and digital twins—which enhance traceability and flow optimization. Nonetheless, persistent barriers—including supply chain fragmentation, lack of standards, and regulatory gaps—continue to constrain widespread adoption. This review identifies six strategic enablers for LC-CE integration: crossdisciplinary competencies, collaborative governance, interoperable digital systems, standardized indicators, incentive-based regulation, and pilot demonstrator projects. By consolidating fragmented evidence, the study provides a structured research agenda and practical insights to guide the transition toward more circular, efficient, and sustainable construction practices. Full article

The acoustic environment of dental clinics plays a critical role in shaping patient experience, staff performance, and overall clinical effectiveness. This comprehensive review, supported by systematic search procedures, investigates how soundscapes in dental settings influence psychological, physiological, and operational outcomes. A total of 60 peer-reviewed studies were analyzed across dental, healthcare, architectural, and environmental psychology disciplines. Findings indicate that mechanical noise from dental instruments, ambient reverberation, and inadequate acoustic zoning contribute significantly to patient anxiety and professional fatigue. The review identifies emerging strategies for acoustic optimization, including biophilic and sustainable design principles, sound-masking systems, and adaptive sound environments informed by artificial intelligence. Special attention is given to the integration of lean management and circular economy practices for sustainable dental architecture. A design checklist and practical framework are proposed for use by dental professionals, architects, and healthcare planners. Although limited by the predominance of observational studies and geographic bias in the existing literature, this review offers a comprehensive, interdisciplinary synthesis. It highlights the need for future clinical trials, real-time acoustic assessments, and participatory co-design methods to enhance acoustic quality in dental settings. Overall, the study positions sound design as a foundational element in creating patient-centered, ecologically responsible dental environments. Full article

This study proposes a fuzzy logic-based approach to better manage supply chain uncertainty and improve decision-making flexibility. The developed framework categorizes supply chain activities into procurement, production, distribution and reverse logistics and integrates Lean, Agile, Resilient, and Green (LARG) KPIs within a hierarchical structure. The tool was implemented using Microsoft Excel^TM to enhance usability for practitioners. To test its applicability, the model was applied to a real case study. The results show that lean and resilient practices are consistently well-established across all supply chain phases, while agility and green practices vary significantly depending on the operational area—particularly between internal function (i.e., production and reverse logistics) and external ones (i.e., procurement and distribution). These findings help to better understand how the LARG capabilities are distributed across the different operational areas of the supply chain and offer practical guidance for managers seeking targeted performance improvement. Although the numerical results are context-specific, the framework’s adaptability makes it suitable for diverse supply chain environments. Full article

Small-for-gestational-age (SGA) infants who experience a marked postnatal catch-up, mainly in weight, are at risk for developing metabolic disorders; however, the underlying mechanisms are imprecise. Exosomes and their cargo (including miRNAs) mediate intercellular communication and may contribute to altered crosstalk among tissues. We assessed the miRNA profile in cord blood-derived exosomes from 10 appropriate-for-gestational-age (AGA) and 10 SGA infants by small RNA sequencing; differentially expressed miRNAs with a fold change ≥2.4 were validated by RT-qPCR in 40 AGA and 35 SGA infants and correlated with anthropometric, body composition (DXA) and endocrine–metabolic parameters at 4 and 12 mo. miR-1-3p, miR-133a-3p and miR-206 were down-regulated, whereas miR-372-3p, miR-519d-3p and miR-1299 were up-regulated in SGA infants. The target genes of these miRNAs related to insulin, RAP1, TGF beta and neurotrophin signaling. Receiver operating characteristic analysis disclosed that these miRNAs predicted with accuracy the 0–12 mo changes in body mass index and in total and abdominal fat and lean mass. In conclusion, the exosomal miRNA profile at birth differs between AGA and SGA infants and associates with measures of catch-up growth, insulin resistance and body composition through late infancy. Further follow-up of this population will disclose whether these associations persist into childhood, puberty and adolescence. Full article

The study aims to increase productivity in the cold room door manufacturing industry by addressing non-value-adding operations, identifying bottlenecks, and reducing processing time through digital twin (DT)-based simulation. The goal is to eliminate the need for supply chain outsourcing and increase overall efficiency. The research involves developing a DT of the existing production process for five distinct categories of cold room doors: flush door, single door, double door, face-mounted door, and sliding door. Simulation was used to uncover problems at multiple stations, encompassing curing, welding, and packing. Lean principles were used to identify the causes of inefficiency, and the process was improved using TRIZ principles. These changes produced a 42.90% improvement in productivity, a 20% dependence reduction on outsourcing and an increase of 10.5% added inventory to the shortage demand level. The approach presented is provided for a particular manufacturer of cold room doors, but the methods and techniques used are generally applicable to other manufacturing companies to support systematic innovation. Combining DT simulation, lean techniques and TRIZ principles, this study presents a strong approach to addressing the productivity challenges in manufacturing. The incorporation of these methods has brought considerable operational efficiency and has minimised dependency on external outsourcing. Full article

This paper aims to enhance operational efficiency in the labor-intensive production of composite wind turbine blades, which are critical components of renewable energy systems. The study was conducted at a wind energy facility in Türkiye, integrating the Single-Minute Exchange of Die (SMED) methodology with a Multi-Mode Resource-Constrained Project Scheduling Problem (MRCPSP) model to reduce production cycle time and optimize labor utilization. An operational time analysis was used to identify and classify non-value-adding activities. SMED principles were then adapted to the fixed-position manufacturing environment, enabling the conversion of internal setup activities into external ones and facilitating task parallelization. These improvements significantly increased productivity and labor efficiency. Subsequently, a scheduling model was developed to optimize the sequence of operations while accounting for activity precedence and resource constraints. As a result, the proposed approach reduced cycle time by 28.6% and increased average labor utilization from 68% to 87%. Scenario analyses confirmed the robustness of the model under varying levels of workforce availability. The findings demonstrate that integrating lean manufacturing techniques with optimization-based scheduling can yield substantial efficiency gains without requiring major capital investment. Moreover, the proposed approach offers practical insights into workforce planning and production scheduling in renewable energy manufacturing environments. Full article

Global supply chains have faced unprecedented disruptions in recent years, ranging from the COVID-19 pandemic to geopolitical tensions and climate-induced shocks. These events have exposed structural vulnerabilities in operational models overly optimized for efficiency at the expense of resilience and sustainability. This conceptual paper proposes an integrated framework linking resilience enablers, post-pandemic operational strategies, and sustainability outcomes. Through a synthesis of the interdisciplinary literature across operations management, sustainability science, institutional theory, and organizational behavior, we develop typologies of operational responses—including agile, lean–green, circular, and decentralized models—and connect them to broader Sustainable Development Goals (SDGs). Drawing on systems thinking and the Triple Bottom Line framework, we present a conceptual model that outlines causal relationships between resilience drivers, adaptive operational strategies, and long-term sustainable performance. The paper further discusses policy implications for public and private sectors, offering insights for global sustainability governance. We conclude by outlining a research agenda to empirically test and refine the model through multi-method approaches. This study contributes to theory by reconceptualizing sustainable operations in the context of compound global disruptions and offers a normative direction for future scholarship and practice. Full article

The integration of Industry 4.0 (I4.0) and lean manufacturing (LM) has become a crucial approach for industries aiming to enhance accuracy, customization, competitiveness, and environmental sustainability. Manufacturers want to make their factories smarter and their operations more efficient by adopting advanced intelligent solutions. The objectives of this article are to illustrate the impact of I4.0 tools on LM organizations and to clarify the importance of balancing technological progress with human-centered practices. Drawing on academic research, we propose a framework for human resources (HR) integration that fosters adaptability, continuous improvement, and employee engagement in the digital age. Full article

This conceptual paper introduces the Green Lean Operational Excellence (GLOE) framework to address the limitations of conventional lean systems in responding to sustainability and resilience challenges. Rooted in sustainability science and operations management, the model reconceptualizes operational excellence by integrating green imperatives—such as environmental accountability, adaptability, and systemic feedback—into lean methodologies. Rather than focusing solely on cost-efficiency, lean practices have also been recognized for enhancing quality, process stability, and organizational flexibility (e.g., Womack & Jones, 1996), which makes them valuable foundations for sustainability integration. The framework was developed through an interdisciplinary synthesis of the literature on lean operations, green supply chains, and adaptive organizational systems. It proposes a structured flow from strategic preconditions to hybrid mechanisms and sustainability-linked outcomes, supported by continuous refinement loops. Key propositions are offered for empirical testing. GLOE redefines excellence beyond traditional cost-driven paradigms, extending lean theory toward strategic sustainability, and bridging gaps between operational practice and sustainability science. It also provides a roadmap for future research, emphasizing empirical validation, indicator development, and digital integration. The model offers practical guidance for managers to move beyond siloed CSR programs and embed sustainability into the core of operational strategy. Ultimately, GLOE positions operations as active contributors to organizational resilience and long-term value in an era of climate disruption and socio-ecological complexity. Full article