Reducing risks in megaprojects: The potential of reference class forecasting

Abstract

Large infrastructure projects often suffer from cost and schedule overruns, mainly due to optimism bias and strategic misrepresentation. Reference class forecasting (RCF) offers a potential remedy. This study presents a comprehensive analysis of the RCF literature with the aim of providing practitioners with key insights and identifying areas for future research. Through a review of 41 selected papers, the paper shows that the effectiveness of RCF is mainly applicable to large-scale projects and depends on the definition of the reference class. The paper calls for the development of an empirically based framework for reference class formation and urges the exploration of RCF's adaptability across industries, challenging the current one-size-fits-all approach. Theoretically, the paper critically assesses the current applications of RCF, while practically it outlines directions for future research and improvements. Overall, the study emphasises the need for detailed, data-driven methodologies and highlights their potential for risk management in projects worldwide.