Search Results (619)

Engineer-to-Order (ETO) manufacturing environments are characterized by high product variety, low repetitiveness, and unstable demand, which pose significant challenges to the application of Lean Manufacturing (LM). This study investigates the application and adaptation of LM principles and tools in an ETO production line using an action research approach integrated with Value Stream Mapping (VSM). The research was conducted at a manufacturer of highly customized electrical equipment. An adapted method for calculating representative cycle times based on weighted production volumes was developed to support line sizing and workload balancing. The proposed future-state design incorporates multifunctional operators, FIFO lanes, daily scheduling, and pitch-based control. The results show a 9.5% reduction in labor requirements, a 61.7% decrease in manufacturing lead time, and a 75.0% reduction in overtime hours. Statistical validation using daily PPC records confirmed significant improvements in actual output, schedule adherence, overtime, and lead time after implementation. In addition to operational improvements, this study offers methodological contributions by proposing practical adaptations of LM tools suitable for high-variability ETO environments, thereby contributing to both theory and industrial practice. Full article

Lean manufacturing has historically focused on eliminating waste from physical production processes; however, increasing digitalization has shifted a substantial portion of operational effort toward information processing and decision making. Existing Lean frameworks lack formal mechanisms to model and quantify inefficiencies arising within these cognitive processes. This paper introduces Cognitive Waste Theory, a mathematical extension of Lean manufacturing that defines cognitive inefficiency as a distinct form of operational waste. Cognitive waste is conceptualized as non-value-adding mental effort generated by misaligned information flow, task structure, and organizational learning dynamics. The framework decomposes cognitive waste into five analytically separable categories: Information Overload, Context Switching, Knowledge Fragmentation, Cognitive Load, and Learning Lag, each expressed through formal mathematical representations grounded in cognitive and operations theory. To enable quantitative assessment, the study proposes normalized waste functions and develops two composite indices: the Cognitive Efficiency Index (CEI), capturing the ratio of effective decision output to cognitive load, and Information Flow Efficiency (IFE), structured analogously to Overall Equipment Effectiveness. Furthermore, classical Lean instruments are reformulated for analytical application in the cognitive domain through Information Value Stream Mapping and Cognitive 5S. By embedding cognitive constructs within a measurable Lean framework, this work provides an attempt to establish a rigorous foundation for analyzing, comparing, and improving cognitive performance in digitally intensive manufacturing systems. Full article

Digital transformation has become a critical pathway for manufacturing firms seeking to improve responsiveness, resource efficiency, and long-term sustainability. However, existing studies have paid limited attention to how digital transformation strategies generate organizational agility across different stages of sustainable manufacturing transformation. Drawing on dynamic capability theory, this study develops a stage-contingent Strategy–Ambidexterity–Agility framework and conducts a longitudinal single-case study of Contemporary Amperex Technology Co., Limited (CATL) from 2011 to 2023. The findings show that organizational agility develops cumulatively through three transformation stages. In the initial stage, a lean-oriented strategy supports balanced ambidexterity and cultivates customer agility through production optimization. In the development stage, an enhancement-oriented strategy enables exploitation-dominant combined ambidexterity and builds market agility through cross-functional integration and closed-loop business logic. In the industry-leading stage, a leap-oriented strategy supports exploration-dominant combined ambidexterity and fosters value chain agility through ecosystem orchestration, intelligent operations, and circular value creation. This study contributes to the literature on digital transformation and sustainable manufacturing by showing how stage-contingent digital strategies shape ambidexterity configurations, generate layered agility capabilities, and support sustainability-oriented manufacturing outcomes. Full article

The decarbonization of energy systems has intensified interest in hydrogen-enriched natural gas (H₂NG) as a transitional fuel for combined heat and power (CHP) units and micro-CHP systems. This review consolidates experimental and numerical studies that explore the energy, environmental, and techno-economic implications of H₂NG blends in CHP applications. Research conducted over the last decade highlights that enriching natural gas with hydrogen extends the flammability limits, enhances combustion stability, and reduces CO₂ and CO emissions, while maintaining or improving electrical efficiency. However, these benefits are accompanied by higher NO_x formation under stoichiometric conditions, which can be mitigated by operating under lean-burn regimes. The review further examines hybrid solutions that integrate electrolyzers, photovoltaic systems, and oxygen-enriched combustion to improve system flexibility and sustainability. The findings consistently show that moderate hydrogen fractions (5–20% vol.) provide optimal trade-offs between efficiency gains and emission control, supporting the role of H₂NG as an intermediate step toward fully hydrogen-powered CHP technologies. Technical challenges related to ignition control, thermal recovery efficiency, and infrastructure adaptation are also discussed, along with emerging strategies for techno-economic optimization. This comprehensive assessment contributes to understanding how hydrogen blending can accelerate the transition to low-carbon, distributed energy systems. Full article

The evolution of digital technologies is reshaping parliaments worldwide, driving fundamental changes in their operations. Parliaments, being traditionally conservative institutions, typically lean toward “mature” emerging or disruptive technologies through cautious, incremental digital transformation attempts, resulting in complex digital parliamentary environments for their users, processes, systems, and tools. The paper employs an integrative literature review as its methodological tool, examining the concept of the “digital parliament” and the technologies that enable it. Using a PRISMA-informed methodology as a guide, we conducted an integrative review covering the period 2006–2025, and in this context, we retrieved 535 publications, screened 260, thoroughly examined 57, and analyzed and synthesized 34 studies addressing digital parliamentary technologies, digital platforms, and cooperative workspaces. We found that while specific parliamentary technology (ParlTech) applications—including big data analytics, artificial intelligence (AI), and hybrid parliamentary tools—are reaching institutional maturity, the concept of a digital parliament remains fragmented, lacking a unified definitional and operational framework. Key research gaps have been identified concerning user classification, the digitization of parliamentary functions, operations, and processes, as well as the institutionalization of cooperation platforms. Based on these findings, we propose strategic directions toward establishing a responsible, inclusive, and evidence-based digital parliament. This research contributes as a guideline for parliamentary organizations seeking to create, retain, and disseminate public value through the responsible adoption of emerging digital technologies. Full article

A recurring challenge in the development of information systems (ISs) across complex organizational domains is the lack of integration and alignment between strategic, tactical, and operational levels, resulting in methodological fragmentation that constrains traceability, innovation, and organizational value generation. This study proposes and applies to the Integrated Innovation Framework for Information System Development (IIF-ISD) to overcome this gap. The research was structured through a systematic literature review, following the PRISMA and ROSES protocols, and validated through an exploratory single-case study involving the development of an IS supporting the Selo Casa Azul (SCA) environmental certification process in a Brazilian construction company, a context chosen for its multi-level organizational complexity and ESG compliance requirements, representative of broader certification IS development challenges. The framework integrates DSRM, agile methodologies, Design Thinking, and Lean Startup through three governing principles—Hierarchical Embedding, Functional Complementarity, and Traceability by Design—achieving cross-level alignment between strategic objectives, tactical performance monitoring, and operational execution. Empirical evaluation (n = 9; 14 weeks) yielded SUS scores of 76.8–82.1/100, a 76% reduction in data entry error rates, and a 78% stakeholder engagement rate, providing initial support for the framework’s practical effectiveness. Full article

Tower cranes (TCs) as essential lifting equipment in construction engineering, play a critical role in prefabricated buildings (PBs). However, current construction scheduling primarily relies on manual observation and operator experience to execute repetitive tasks, leading to low efficiency, heavy workload, and potential safety risks. In typical PB construction projects, multiple buildings are often constructed in parallel, where each TC is assigned to serve a specific group of buildings independently. This allocation strategy is generally predetermined by the site layout plan to ensure operational safety and avoid inter-crane interference. To enhance lean construction performance and management efficiency in PBs, this study develops a scheduling optimization model that explicitly considers the initial hook position and the specific locations of prefabricated component (PC) supply and demand points. The proposed model is solved and compared using three meta-heuristic algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Artificial Bee Colony (ABC). Numerical results indicate that PSO outperforms GA and ABC in terms of convergence speed and cost minimization performance. After optimization, the operating times of two TCs are reduced by 23.94% and 12.16%, respectively, saving ¥207.29 and ¥293.96 per day in operating costs, and reducing total construction cost by approximately 8.0%. These results demonstrate that the proposed model can effectively improve construction efficiency and support lean management under the considered planning assumptions. Full article

Real-time roadway surveillance now leans hard on automated detection. How a model trained in one geographic context actually behaves on another, though, is still underexplored for Southeast Asian deployments. We answer that question for Thai roadway closed-circuit television with a cross-domain validation framework. A YOLOv11n (Ultralytics v8.2.0; Ultralytics, Los Angeles, CA, USA) detector trained with focal loss feeds a confidence-aggregation step that turns per-detection scores into a per-frame accident score, and we put four aggregation operators head-to-head. Reliability comes from DeLong variance estimation paired with non-parametric bootstrap on 1245 Thai frames that carry 23 positive accident events. Under maximum-class aggregation the proposed configuration reaches a frame-level AUROC of 0.959 ± 0.020 across three random seeds. Under top-K aggregation it reaches 0.965 ± 0.018. Per-seed DeLong 95 percent intervals exclude chance performance throughout. We also evaluate three baseline configurations: YOLOv5su comes in at 0.738, YOLOv8n at 0.868, and a Chiang Mai-tuned YOLOv11n variant at 0.918. The architectural progression seen on standard benchmarks therefore carries cleanly into the cross-domain setting. The same Chiang Mai-tuned variant reached an in-domain mAP50 of 0.952 yet only 0.918 cross-region AUROC on a separate Thai region, which is a quiet but clear signal that geographic proximity within a country does not on its own remove distributional shift. Bounding-box localisation appears as a secondary diagnostic because the operational target here is frame-level alerting rather than pixel-precise annotation. Edge deployment optimisation falls outside the present scope. What the work leaves behind is a reproducible baseline and a statistical protocol that follow-up Southeast Asian roadway-safety research can build on. Full article

Among pathways toward carbon neutrality, substituting hydrocarbons with hydrogen-carrier fuels such as ammonia presents significant potential for carbon emission reduction. This study examines the combustion characteristics of ammonia (NH₃) and simulated LPG consisting of 70% propane (C₃H₈) and 30% butane (C₄H₁₀) by volume blends under non-premixed conditions using a crossflow slot burner. Flame appearance, OH* chemiluminescence, flame temperature, and CO and NO_x emissions were evaluated at equivalence ratios (Φ) of 0.4, 0.7, and 1.0, with ammonia fractions ranging from 0% to 70%. Increasing ammonia content decreased OH* chemiluminescence intensity, indicating a reduced radical pool and lower reaction intensity, particularly under lean conditions. Nevertheless, stable combustion was achieved at Φ = 1.0 due to improved mixing and heat recirculation. Flame temperature declined by only 9.3%, even at 70% ammonia, confirming good thermal stability. NO_x emissions exhibited non-monotonic behavior, increasing at moderate ammonia fractions due to fuel-bound nitrogen and thermal mechanisms, and then decreasing at higher ammonia levels as flame temperature and radical activity diminished, while CO emissions remained low up to 50% ammonia near stoichiometric conditions but increased under ultra-lean operation because of limited oxidation kinetics. These results highlight the feasibility of simulated LPG–NH₃ blends as transitional low-carbon fuels in practical combustion systems. 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

Recent advances in ozone-assisted combustion for both compression ignition and spark ignition engines are discussed. Ozone, which can be produced by an electrical discharge in air or pure oxygen, decomposes at high temperature to yield highly oxidizing oxygen atoms, which enhance fuel/air reactions. As a result, flames are faster and more stable, ignition is enhanced and low-temperature chemistry is promoted. In the literature, the beneficial influence of ozone on standard and innovative fuels, including low-carbon (syngas) and zero-carbon (hydrogen and ammonia) fuels, has been assessed. In addition, the impact of ozone seeding on new combustion strategies has also been discussed. Ozone enables better control of ignition in Homogeneous Charge Compression Ignition (HCCI) engines, and improves the combustion stability in low-load Gasoline Compression Ignition (GCI) engines, as well as in lean Spark-Assisted Compression Ignition (SACI) and spark ignition (SI) engines, thus broadening the operating range of these engines. Full article

Break-bulk cargo operations are characterized by high variability and complex resource synchronization, yet they have received limited research attention compared to containerized logistics. This paper proposes an integrated lean-informed simulation framework for evaluating vessel service time (VST) in multipurpose terminals handling break-bulk cargo. The framework sequences three analytical stages: Value Stream Mapping paired with Ohno’s waste taxonomy to diagnose non-value-adding activities, a discrete-event simulation model built in Simio to quantify their impact on VST, and Sobol sensitivity analysis to decompose the remaining variability across operational factors. Demonstrated at DP World Lirquén, a multipurpose terminal in Chile, the lean diagnostic identified 101 min of waste per cycle across waiting, motion, and overproduction categories. Scenario evaluation showed that eliminating shift-transition delays and standardizing load composition reduced VST by 14.3% and 10.6%, respectively, without capital investment. The sensitivity decomposition revealed that warehouse machinery composition, particularly the interaction between equipment types, dominates VST variability, while truck fleet size operates as an independent factor. These findings demonstrate that coordination-related policy interventions outperform incremental resource additions. More specifically, machinery allocation must be optimized jointly rather than by equipment type in isolation. Full article

The cutting-edge era emphasizes developing key solutions to improve productivity and promote economic growth within limitations. To achieve this, the production management team employs various process improvement approaches to empower operations management. The aim of this article is to examine the recent trends in operations management scenarios in which industry professionals seek an ingenious path for selecting process improvement approaches through a systematic literature review. The study employed the PRISMA framework for a systematic literature review of 176 papers published from 2000 to 2026. The key finding shows the methodologies used for operations management scenarios, considering Industry 3.0–6.0. The methodologies include traditional approaches, concurrent approaches, data-driven assessment, real-life assessment for competent approaches, and sustainable approaches. The study focused on identifying obstacles to selecting and implementing a suitable decision-making process improvement approach to mitigate operations management issues in the Industry 3.0–6.0 work environment. These obstacles are recognized as several challenges and problems that arise on the shop floor and reduce the organization’s sustainability. This study identifies an emerging research area: the development of innovative, AI-driven operations management platforms for flexible, emerging work settings. Full article

Although Industry 4.0 has successfully advanced lean manufacturing through digitalization and automation, its primary focus on operational efficiency leaves emerging strategic priorities—human-centricity, sustainability, and resilience—outside its original scope. The Industry 5.0 agenda explicitly elevates these three pillars, creating new potential to drive lean transformation. However, how Industry 5.0 can systematically drive lean manufacturing transformation remains unclear. To address this knowledge gap, this study develops a strategic roadmap. First, a content-centric literature review identifies 12 key enablers for Industry 5.0-driven lean manufacturing. Second, Fuzzy Interpretive Structural Modeling (FISM) and expert opinions determine hierarchical relationships among the enablers and construct a multi-level structural model. Third, Matrices d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis evaluates the driving power and dependence of each enabler. Finally, a strategic roadmap is developed based on expert synthesis. The findings reveal that “government regulation and incentives” and “employee skill training” are the most critical enablers, while “value chain design and improvement” and “resource input and return” are the most complex and difficult to develop. The roadmap highlights the mediating role of “stakeholder participation and collaboration.” Importantly, the roadmap addresses potential tensions in lean implementation—such as the carbon footprint trade-off of frequent small-batch transport—by embedding sustainability assessment into value chain design and technology governance. This study offers a practical guide for manufacturers to prioritize investments and sequence actions toward lean transformation in the Industry 5.0 era. The main contribution of this study is a strategic roadmap that explains how Industry 5.0 can enable lean manufacturing transformation through prioritized actions and hierarchical enablers, while reconciling efficiency with sustainability and resilience goals. This roadmap offers a practical guide for manufacturers and policymakers to sequence investments and actions toward lean transformation in the Industry 5.0 era. Full article

Reversible pump turbines (RPTs) play a key role in pumped hydro energy storage systems, where increasing grid flexibility requires frequent operation under off-design conditions. In turbine mode, deep partial load and no-load operation are often associated with severe flow instabilities, rotating stall, and strong rotor–stator interactions, which can limit operational flexibility and increase mechanical stress. Previous studies have shown that blade lean can influence hydrodynamic stability; however, its effect under no-load conditions remains insufficiently understood. In this work, the influence of runner blade lean on flow instabilities and rotor–stator interaction in a reversible pump turbine is numerically investigated. Two runner configurations, featuring a 0° and a $-15°$ blade lean angle, are analyzed through unsteady CFD simulations during the transition from deep partial load to no-load operation. The analysis focuses on flow field characteristics, blade loading, and the spectral content of pressure, torque, and radial forces. The results show that the negatively leaned runner significantly mitigates flow recirculation near the hub, reduces pressure and torque fluctuations, and strongly suppresses higher-order harmonic components associated with rotor–stator interaction. In particular, radial force amplitudes at blade-passing harmonics are substantially reduced under no-load conditions. These findings demonstrate that a negative blade lean improves hydrodynamic stability and reduces vibratory loads, contributing to the enhanced operational reliability of reversible pump turbines. Full article