Lean 5.0: A Predictive, Human-AI, and Ethically Grounded Paradigm for Construction Management
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908671157469184 |
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| author | Khoshkonesh, Atena Mohammadagha, Mohsen Ebrahimi, Navid Sadeghigolshan, Narges |
| author_facet | Khoshkonesh, Atena Mohammadagha, Mohsen Ebrahimi, Navid Sadeghigolshan, Narges |
| contents | This paper introduces Lean 5.0, a human-centric evolution of Lean-Digital integration that connects predictive analytics, AI collaboration, and continuous learning within Industry 5.0 and Construction 5.0 contexts. A systematic literature review (2019-2024) and a 12-week empirical validation study demonstrate measurable performance gains, including a 13% increase in Plan Percent Complete (PPC), 22% reduction in rework, and 42% improvement in forecast accuracy. The study adopts a mixed-method Design Science Research (DSR) approach aligned with PRISMA 2020 guidelines. The paper also examines integration with digital twin and blockchain technologies to improve traceability, auditability, and lifecycle transparency. Despite limitations related to sample size, single-case design, and study duration, the findings show that Lean 5.0 provides a transformative paradigm connecting human cognition with predictive control in construction management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18651 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lean 5.0: A Predictive, Human-AI, and Ethically Grounded Paradigm for Construction Management Khoshkonesh, Atena Mohammadagha, Mohsen Ebrahimi, Navid Sadeghigolshan, Narges Computational Engineering, Finance, and Science Artificial Intelligence Machine Learning This paper introduces Lean 5.0, a human-centric evolution of Lean-Digital integration that connects predictive analytics, AI collaboration, and continuous learning within Industry 5.0 and Construction 5.0 contexts. A systematic literature review (2019-2024) and a 12-week empirical validation study demonstrate measurable performance gains, including a 13% increase in Plan Percent Complete (PPC), 22% reduction in rework, and 42% improvement in forecast accuracy. The study adopts a mixed-method Design Science Research (DSR) approach aligned with PRISMA 2020 guidelines. The paper also examines integration with digital twin and blockchain technologies to improve traceability, auditability, and lifecycle transparency. Despite limitations related to sample size, single-case design, and study duration, the findings show that Lean 5.0 provides a transformative paradigm connecting human cognition with predictive control in construction management. |
| title | Lean 5.0: A Predictive, Human-AI, and Ethically Grounded Paradigm for Construction Management |
| topic | Computational Engineering, Finance, and Science Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2511.18651 |