Framework for Evaluating the Success of Integrated Project Delivery in the Industrial Construction Sector: A Mixed Methods Approach & Machine Learning Application
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Abstract
Integrated project delivery (IPD) has gained traction as a collaborative approach to managing complexity and uncertainty in large industrial capital projects. While IPD emphasizes team integration and process alignment to drive better outcomes, the lack of standardized benchmarks to evaluate its performance relative to traditional methods persists as a barrier. To bridge this gap, this study developed a practical, and unbiased Project Success Framework (PSF) for IPD on industrial projects. A mixed methods research approach including subject matter experts’ survey, research charrette, and validation survey was conducted to build and validate the PSF. In addition, this study proposed a machine learning (ML)-based application tool embedding PSF to enhance the practicality and applicability of PSF. The machine learning-based application tool was validated by comparing the results with the PSF suggested in this research. The PSF developed in this study allows researchers and practitioners to empirically evaluate the integrated project delivery's efficacy on key industrial project outcomes. In addition, it offers a method to compare project delivery methods across diverse projects, aiding organizations in precise selection using empirical evidence for optimal results. Moreover, this framework aids clients in crafting shared risk/reward models that foster successful outcomes by encouraging desirable behaviors.
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