Digital technologies are rapidly reshaping the architecture, engineering, construction and operations (AECO) sector. Increasingly, researchers across construction engineering and management, construction informatics and automation and robotics are moving beyond traditional paradigms that frame research challenges as solely production-, people- or technology-centric. Instead, integrative people–production–technology approaches are gaining prominence, positioning organizational dynamics and culture, human-centric design and human–computer/machine/information interactions at the core of this integration. Arguably, this shift stems from a growing recognition that projects and organizations in the AECO sector function as inherently socio-technical systems (Walker et al., 2024).Reflecting this conceptual and methodological shift, leading research communities – such as the International Group for Lean Construction (IGLC) and the International Association for Automation and Robotics in Construction (IAARC) – have increasingly embraced integrated and holistic research contributions within their conference series. Consequently, new transdisciplinary research streams rooted in innovative design and production management philosophies, including Lean thinking, Industry 4.0/5.0, agile management and Lean 4.0 (González et al., 2022), are advancing the synergies between digital technologies and a more human-centric construction industry.This special issue of Engineering, Construction and Architectural Management advances scholarly dialog on the evolving convergence of digital technologies and Lean construction, with particular emphasis on human-centered design systems and their potential to reshape the AECO industry. It builds upon the seminal discussion presented in González et al. (2022), which articulated the foundational integration of lean construction, people and culture and digital technologies through the lens of human-centered design. The issue distinguishes itself by promoting inclusive, human-centered engineering paradigms and by encouraging innovative research that combines lean-informed approaches with contemporary digital and automation technologies. We contend that such an integrative agenda offers a substantive and timely pathway toward addressing the persistent productivity and digitalization challenges that continue to confront the AECO sector.In this editorial, we present an overview of the contributions included in this special issue, highlighting the specific insights offered by each paper, the emerging synergies between digital technologies and lean construction, and the future research opportunities these works collectively contribute to the AECO sector.Thirteen papers were accepted in this special issue. These papers represent contributions from the Americas, Oceania, Europe and Asia, including the following countries: Australia, Brazil, Canada, Chile, China, Finland, India, New Zealand, Singapore and Switzerland. Different topics combining Lean construction principles and methods, people and organizations and digitalization and automation were identified. As such, five topical areas were defined to sort the papers received: Building Information Modeling (BIM), robotics, human-centric and organizational transformation, digital visualization for decision-making and artificial intelligence (AI) and computer simulation modeling.The paper “Integration of building information modelling and digital twin in mechanized tunnelling construction” by Martinez et al. (2026) presents a case study of the implementation of a digital twin system for mechanized tunneling construction on a 2.4 km tunnel project in France. The digital twin framework integrates monitored data from the tunnel boring machine to generate real-time status information to reduce waste of rework, aligning with Lean operations principles. The case study involves the development of computational design algorithms that automate data integration, testing and empirical data collection of the digital twin system's applications in use cases as a proof of concept. Through the experiments, the corresponding technical challenges are indicated for a continuous development.The paper “Scan-to-BIM approach for enhanced semi-automated cost management in modular off-site construction” by Mehdipoor et al. (2026) merges Lean construction principles with scan-to-BIM to enhance cost management in modular off-site construction. The design science research methodology is applied to compare a resulting Lean-driven 5D-BIM framework against traditional cost management. The Lean-driven 5D-BIM framework – enhanced with 3D laser scanning and scan-to-BIM – automates measurement, improves accuracy and embeds real-time cost data for precise quantity takeoffs. In contrast, it was observed that traditional cost management is manual, less efficient and lacks the waste-reducing, value-maximizing benefits of Lean and modular off-site construction. The main finding from this research reports that the Lean-BIM integration cuts reporting time and improves cost accuracy noticeably.The paper “Benefits of construction robots for on-site Lean construction: a comparison and evaluation framework” by Hu et al. (2026) evaluates the contributions of construction robots to on-site Lean construction using two case studies in Singapore. The plastering and spray-painting robots examined in the study deliver significant improvements in productivity, quality consistency, worker safety and material use. By analyzing impacts at the micro, meso and macro levels, the authors show that robotics, when coupled with digital positioning tools and redesigned workflows, can reduce waste, minimize physical strain and help stabilize production systems. The results confirm that construction robotics can meaningfully advance Lean objectives in real-world site applications.The paper “Human-centered design and development framework of autonomous inspection robot-based systems for Lean Construction 4.0” by Wang et al. (2026) introduces a human-centered framework for developing autonomous inspection robot systems under Lean Construction 4.0's theoretical umbrella. The purpose is to automate hazardous, non-value-adding tasks while prioritizing user adaptability and value generation. The design methodology integrates human-centered design, Lean startup and agile principles across four phases: empathize/define, ideate/prototype, develop/deploy and monitor/improve. Validated through a case study, the findings demonstrate that the framework significantly improved usability, enhanced information flow efficiency and minimized human involvement in dangerous tasks. These results confirm the framework's ability to optimize workflows and increase value in robot-driven construction systems.The paper “A human-centered framework for assessing task complexity in construction: a cognitive load perspective” by Eltahan et al. (2026) addresses how cognitive demands and task complexity (TC) impact construction worker performance. It introduces a scalable framework integrating cognitive load theory, Lean thinking and physiological metrics to assess these factors. Using the design science research methodology, the authors validated the framework via a controlled experiment simulating visual complexity using object speed (OS) and structural equation modeling (SEM). The results from this study confirmed meaningful relationships between TC, cognitive load and performance, identifying OS as a key determinant. The results validate the framework's ability to capture complexity-performance dynamics and predict errors, offering a structured approach to managing cognitive load in construction.The paper “Enabling Lean Construction 4.0 through human-centric digital transformation: organisational leadership insights” by Bidhendi et al. (2026) aims to study human-centric digital transformation for addressing organizational barriers regarding Lean implementations through Lean 4.0. Through the analysis of 20 senior leaders across multiple countries, the authors identified challenges, including strategic vision and communication, that are interconnected in organizations. Also, the industry's low-profit margins could limit investment capacity and long-tenured employees transitioning. The initial work draws insights for practitioners to understand the current state of digital transformation to different extents, where Lean 4.0 could be implemented in practice. The work provides insights for future research with larger sample sizes to validate these findings.The paper “AI-assisted decision-making and dynamic trust in Lean construction: synergy mechanisms and insights” by Liu and Liu (2026) investigates how AI-assisted decision-making influences dynamic trust within Lean construction organizations. Drawing on empirical data from 293 practitioners, the authors reveal that AI enhances trust by improving information clarity, reducing bias and facilitating consistent decision-making. They further identify the mediating roles of risk preference and task transfer resilience, as well as the moderating impact of decision chain length. These findings underscore the socio-technical pathways through which AI strengthens Lean collaboration, illustrating how digital decision-support can reinforce interpersonal confidence and organizational alignment.The paper “Enhancing quality assurance in precast production using mixed reality and ray casting: a Lean Six Sigma approach” by Sandagomika et al. (2026) proposes a robust quality management approach with a knowledge-intensive engineering application in a mixed reality (MR) environment. They found that geometric dimensions of virtual precast components were accurately measured within ±5 mm for length and width and ±2 mm for height, achieving tolerance limits essential for precast production. With MR, they found that process capability indexes improved significantly. This approach provides helpful implications in the prefabrication industry to reduce human intervention, rework, and downtime while improving measurement precision and decision-making reliability.The paper “Lean Construction 4.0: enabling autonomous decision-making through digital visual management minimizing waste” by Görsch et al. (2026) reinforces the state-of-the-art technological frameworks of Lean that further align centralized and decentralized components in decisions of construction. The autonomous framework ensures coherence and consistency in project processes and fosters a better understanding of the interdependencies between different system units and agile problem-solving. The framework substantially supports the continuous improvement of construction processes.The paper “Can machine learning approach classify making-do waste cases in construction sites?” by Maciel et al. (2026) combines exploratory and experimental research to investigate the viability of using machine learning (ML) to classify making-do wastes, which focused on a dataset written in Brazilian Portuguese for training and validation of the material datasets. It is demonstrated as an effective application of ML in waste classification. The findings present a promising opportunity to enhance operational efficiency, reduce costs and improve the overall quality of waste analysis in the construction industry.The paper “Prediction of the project schedule performance outcome with last planner system and social network analysis indicators” by Lagos et al. (2026) introduces an ML modeling approach to forecast a project's final Schedule Performance Index (SPI) by integrating Last Planner System (LPS) data with social network analysis (SNA) and earned value method (EVM) metrics. The goal is to provide earlier, more actionable diagnostics than traditional methods allow. Trained on 160 projects, a support vector regression (VSR) model achieved high accuracy and significantly reduced reliance on lagging result-oriented metrics. The findings confirm that combining collaboration data with performance metrics enhances predictive robustness and effectively highlights team-interaction drivers of schedule risk, as demonstrated by the LPS-based ML analyses in conjunction with data science and SNA techniques.The paper “Automatic daily salary settlement for construction workers in photovoltaic engineering using Lean construction and AIoT” by Jiang et al. (2026) proposes an automatic daily salary settlement system (ADSSS) that integrates Lean construction principles with Artificial Intelligence of Things (AIoT) technology to optimize labor productivity, as well as address ongoing challenges in photovoltaic (PV) construction including operational inefficiencies and inequitable payroll distribution. The authors conducted design science research and a case study with AIoT technology to collect real-time data via intelligent safety helmets and the YOLO algorithm to analyze and process data to validate the prototype. ADSSS could strengthen human resource management and optimize construction workflows and potentials for strategic human capital development.The paper “A discrete event simulation-based value stream mapping framework to support digital Lean construction” by Sreram and Thomas (2026) introduces a discrete-event simulation-based value stream mapping (VSM) framework that modernizes traditional Lean tools by embedding them in a dynamic digital environment. By automating the generation of current and future state maps and rapidly evaluating alternative workflow scenarios, the framework reduces manual effort and supports collaborative planning. By demonstrating measurable reductions in non-value-adding time, the study shows how simulation-driven VSM enhances transparency and responsiveness in process improvement, aligning directly with Lean's emphasis on flow reliability and continuous learning.The accepted papers collectively highlight a powerful and evolving synergy between Lean Construction principles and tools and the rapidly expanding ecosystem of digital technologies and automation shaping the AECO sector. Across diverse contexts and methodological approaches, a consistent message emerges: digital innovation does not replace Lean thinking – it amplifies it. Technologies such as simulation, mixed reality, AI, robotics, biosensing, digital twins and data-driven decision platforms enhance the visibility, predictability and stability of construction processes, strengthening Lean's foundational emphasis on waste reduction, value generation and reliable workflow. Also, digitalization assists knowledge transfer through visualization and exchange with data-rich models and analyses.Simulation tools extend Lean planning by enabling intuitive visualization, rapid iteration and collaborative experimentation with alternative workflows. By making processes more transparent and allowing teams to foresee and mitigate constraints, simulation directly supports Lean objectives of variability reduction and continuous improvement. Similarly, MR supports high-precision quality assurance, particularly in prefabrication settings, reducing human error and improving handoff reliability. These tools augment human cognition by lowering cognitive load and enhancing situational awareness, allowing teams to make better, more reliable commitments.AI further deepens Lean decision-making by providing more accurate, timely, and granular insights into project conditions. ML-based classification improves the consistency of defect detection and resource management, while AI-driven analytics create environments of data transparency that support trust-based coordination – an essential condition for stabilizing workflow. Robotics reinforces Lean's focus on value and flow by increasing execution precision, reducing rework and mitigating hazardous or ergonomically demanding tasks. Together, these technologies contribute to safer and more predictable operations that could avoid mistakes or hazards, advancing Lean aims of dependable production and human-centered improvement cycles.The showcased research also demonstrates the growing convergence between process optimization and data-driven decision ecosystems. Digital visual management, socio-technical collaboration frameworks and autonomous decision-support systems illustrate how technological platforms reshape team coordination, particularly in decentralized or dynamic project contexts. These tools integrate real-time data with organizational processes, strengthening knowledge integration and enabling more adaptive, resilient project delivery. They also enhance transparency and measurement accuracy, ensuring that deviations, constraints or emergent issues are identified early and addressed systematically.Case study evidence reinforces these theoretical insights. Digital twin applications in projects show clear potential to enhance Lean implementation by streamlining workflows, improving progress monitoring and supporting proactive constraint removal. Surveys and interviews with industry practitioners further reveal an emerging awareness that the combined use of Lean methods and digital technologies offers a pragmatic strategy to address persistent productivity, workforce and organizational challenges. While technology alone cannot resolve systemic issues, its integration with robust Lean management approaches provides a more holistic framework for addressing real-world complexities across domains and project stages.Across the reviewed papers, the inter-/trans-disciplinary nature of the research stands out and fills the gap in literature. This contribution fits well with the current needs in practice and spurs systemic innovation for the industry to think out of the box. Also, the integration of digitalization and Lean sparks the existing problems in the industry for a bottom-up transformation regarding technology and people. Human-centric concerns – ranging from worker safety and ergonomics to decision-making environments and organizational culture – are embedded within discussions of digital transformation. This highlights an important shift: Lean and digital technologies are increasingly understood not as parallel or competing paradigms but as mutually reinforcing components of socio-technical systems. Lean offers the theoretical grounding and practical purpose that guide technology adoption, ensuring that digital tools are implemented to solve real production problems rather than for novelty or automation alone. In turn, digital systems enrich Lean by expanding the sector's capacity for measurement, feedback and workflow stabilization.From a scientific standpoint, the variety of research designs – ranging from controlled experiments and ML benchmarking to interviews, surveys and field-based case studies – provides a nuanced methodological landscape that enables deeper understanding of these synergies. This diversity strengthens the robustness of the findings and opens avenues for interdisciplinary and transdisciplinary research that can further advance the state of knowledge in construction management and technological innovation.Overall, the body of work reveals a clear direction for the AECO sector: the future of Lean lies in data-rich, adaptive and human-aligned digital ecosystems, while the future of digital technologies must be grounded in Lean principles that ensure purpose, value and sustained relevance.Future research across these studies points to an increasingly integrated agenda for advancing Lean–digital synergy in the AECO sector. A central priority is the generalization and scaling of digital simulation, AI, robotics and predictive analytics across diverse project types, delivery methods and regional conditions. This includes developing simulation models that connect with real-time data streams to support continuous value-stream monitoring and expanding AI-assisted decision-making research to examine long-term organizational effects, trust dynamics and optimized decision-chain structures. Robotics research should advance toward multi-robot coordination, digital-twin integration and adaptive workflow redesign to enable safer, more flexible human–robot collaboration.Future inquiry must also move beyond isolated technological applications toward fully connected digital ecosystems that integrate reality-based quality assurance, ML analytics, autonomous visual management and socio-technical collaboration frameworks. Understanding how team culture, digital literacy and organizational readiness influence adoption will be essential for designing scalable, interoperable platforms that support prefabrication, retrofitting and closed-loop material flows. Broader empirical validation across geographical contexts, supply chain configurations and construction phases – including those beyond manufacturing and assembly in modular off-site construction – is likewise needed. Moreover, further research in the of human and digital tools is These not technical body of knowledge for human–robot collaboration, but also and of digital tools for implementation in construction projects through Lean thinking and human-centered construction a research and more diverse should be to validate task design approaches and insights into productivity, safety and work is to complexity metrics and their in real-world these pathways for holistic frameworks that integrate simulation, AI, and robotics into Lean management strengthening predictive stabilizing workflows and enabling more adaptive and human-aligned project delivery
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