An approach to support reference class forecasting when adequate project data are unavailable

Abstract

This study presents an approach to enhance the effectiveness of Reference Class Forecasting (RCF) in managing cost overruns in large infrastructure projects. RCF performs poorly when adequate reference classes are unavailable. The proposed approach addresses this limitation by adapting reference classes using empirical weights, allowing for the use of diverse and previously unsuitable reference data. The contribution of this study is the development of a simple, accessible approach for RCF application in scenarios with limited reference data. This approach is validated through practical examples, showing its potential to yield more accurate RCF reference classes compared to traditional practices. This research enhances RCF's utility by providing a potential solution for its primary limitation: the accurate definition of reference classes when data is scarce. It opens new avenues for RCF application for projects where reference data was not available.