TY - CONF TI - Identifying dynamic bottleneck in modular construction using simulation and probabilistic analysis AU - Moghimi, Nima AU - Tetik, Müge AU - Mei, Qipei AU - Gonzalez, Vicente A. AU - Hamzeh, Farook AD - Corresponding Author, Ph.D. Student, Department of Civil and Environmental Engineering, University of Alberta, Canada, nmoghimi@ualberta.ca, orcid.org/0009-0008-4733-1276 AD - Postdoctoral Researcher, Department of Civil Engineering, School of Energy Systems, Lappeenranta-Lahti University of Technology, Lahti, Finland, muge.tetik@lut.fi AD - Assistant Professor, Department of Civil and Environmental Engineering, University of Alberta, Canada, qipei@ualberta.ca AD - Hal Kvisle Professor and Tier 1 Canada Research Chair in Digital Lean Construction, Infrastructure Human Tech (IHT) Lab, Strategic Projects Insight Centre in Engineering (SPICE), Department of Civil and Environmental Engineering, Faculty of Engineering, University of Alberta, Canada, vagonzal@ualberta.ca, orcid.org/0000-0003-3408-3863 AD - Professor, Department of Civil and Environmental Engineering, University of Alberta, Canada, hamzeh@ualberta.ca, orcid.org/0000-0002-3986-9534 ED - Hamzeh, Farook ED - Poshdar, Mani ED - Garcia-Lopez,, Nelly P. ED - Gan, Vincent PY - 2026 DA - 2026/06/22 T2 - Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34) C3 - Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34) CY - Singapore, Singapore SP - 849 EP - 861 DO - 10.24928/2026/0247 SN - 2789-0015 (ISSN) AB - Bottleneck identification is fundamental to Lean production, as system constraints limit throughput and generate waste through waiting, excess Work-in-Process (WIP), and flow interruptions. Lean practices such as Value Stream Mapping and takt–cycle time analysis, together with indicators including utilization, queue length, and active-period methods, are commonly used to diagnose flow restrictions. However, these approaches assume relatively stable conditions and sufficient buffering—assumptions that rarely hold in modular construction factories. High product variability, reliance on manual labor, limited space, and stochastic task interactions cause bottlenecks to shift dynamically, making conventional diagnostics inconsistent. To address this challenge, this study introduces a probabilistic diagnostic principle for dynamic production environments. The approach employs Interdeparture Time (IDT) analysis and Cumulative Distribution Function (CDF) matching to distinguish true system constraints from stations experiencing temporary flow disruptions. The diagnostic logic is implemented within a hybrid Discrete-Event and Agent-Based simulation framework and applied to a timber wall assembly line. Results show that traditional Lean and utilization-based indicators produce conflicting bottleneck locations, while the probabilistic approach consistently identifies predominant constraints. The study further introduces a decision-support mechanism using sequential sensitivity analysis to quantify capacity thresholds and anticipate bottleneck migration. KW - Dynamic bottleneck KW - modular construction KW - probabilistic analysis KW - simulation KW - production controls. L1 - https://www.iglc.net/papers/details/2542/pdf UR - https://www.iglc.net/papers/details/2542 DB - IGLC.net LA - English N1 - Export date: 04 October 2026 ER -