First Attempt Toward a Holistic Understanding of the Interdependent Rippled Impacts Associated with Out-Of-Sequence Work in Construction Projects: System Dynamics Modeling Approach

Out-of-sequence (OOS) work during construction is a major cause of productivity losses, cost and schedule overruns, and quality decline, either directly or indirectly. Given the complexity of construction projects, such impacts could be linear, nonlinear, sudden, delayed, direct, or rippled. Despite that realization, the construction literature falls short in studying how OOS is interrelated to the different project complex processes; thus, it is not possible to quantify the impacts of OOS on construction projects. The goal of this research is to develop and test an advanced model for analyzing the dynamics of OOS work. To this end, the authors used a multistep interdependent research methodology that involved (1) forming a dynamic hypothesis that explains the dynamics of the feedback structure of the problem in hand; (2) developing an advanced simulation model that captures the quantitative aspects of OOS and its corresponding relationship with progress, productivity, rework, and staffing, respectively; (3) developing a multistage calibration heuristic that enables users to calibrate the model to their projects to perform tailored and accurate analysis; (4) running verification tests on the model to ensure structural and behavioral validity; and (5) validating the model and its industry application using data from a mega construction project of around $500 million. Among other findings, the model demonstrated that not only the total magnitude of OOS work, but also the timing of such OOS work, has impacts on the project performance. The research has substantial intellectual merits to the construction management body of knowledge as it acts as the first to analyze the dynamic of OOS and capture its complexity. The research also introduces concepts and heuristics that add to the dynamic modeling field. As such, this study helps to better understand the relationship between OOS work and the different project feedback systems, visualize the rippled impacts of disruptions caused by OOS work, and provide informative forensic analysis of the corresponding project overruns. As for its practical merits, the presented model will assist project participants in properly analyzing and forecasting the direct and indirect effects of OOS in their projects and gauge the effectiveness of their mitigation policies