Robustness Analysis of Total Project Cost and Schedule Delay and Overrun Indicators of Heavy Industrial Projects

Abstract

Barriers such as cost overruns and schedule delays make it challenging to achieve successful project outcomes. Their early detection, however, allows mitigation strategies to be implemented before too much delay has occurred; therefore, the objective of this study was to identify the total project cost and schedule overrun indicators and evaluate their reliability for large-scale heavy industrial projects. To accomplish the study’s goals, a structured survey was developed and disseminated to construction industry professionals to gain their input on the most important cost overrun indicators (COIs) and schedule overrun indicators (SOIs). The collected data were analyzed by applying various statistical tests, and a list of the significant COIs and SOIs was compiled utilizing all-possible regression. The extreme bound analysis (EBA) method was adopted to determine the robustness of the identified indicators. The results revealed the nine COIs are design phase contract type, change management approach, degree of internal stakeholders’ alignment, delay in the delivery of long-term facility machinery, proportion of artisanal labor sourced locally, proportion of design completion before the budget approval of the project, preparing for startup deployment, agreement including delayed completion charges, and number of design organizations; two of these were fragile indicators. The results also demonstrated the nine influential indicators for total schedule overrun are implementation of front-end planning, efficacy of the change management process, project population density, average number of project management (PM) team participants in the procurement phase, prior designer and contractor partnership, delay in the delivery of long-term facility machinery, average size of PM team design phase, efficacy of designers group interaction, and number of subcontracting firms; six of these were robustly related to the model. The findings of this study can help construction project decision makers focus on the most reliable indicators and reduce the number of revisions required throughout the execution of a project.