Forecasting the success of megaprojects with Judgmental methods

Litsiou, K

Abstract

Forecasting the success of megaprojects is a very difficult and important task because of
the complexity of such projects, as well as the large capital investment that is required for
the completion of these projects. One could argue that forecasting is not needed in this
context: the master Gantt chart of the tasks with assigned person-hours plus the
respective Bill of Materials should suffice for an accurate estimation of the duration and
cost of a project. If that was the case then every project would finish on time and on budget
– but this is far from true as the numerous examples attest: HS2, Channel Tunnel, major
IT public projects in NHS, to name a few. In this research, we employ judgmental
forecasting methods to predict the success of megaprojects in as series of forecasting
experiments. In the first experiment,the participants forecast for one megaproject ('space
exploration') with Unaided Judgment (UJ), Structured Analogies (SA) and Interaction
Groups (IG) with IG showing the best results since IG>SA>SA. In the second experiment,
we use a second megaproject ('a major recreational facility in the very city centre of a
major cosmopolis') and see separately the success in terms of excesses in the budget and
the duration of the project. Furthermore, the participants forecast the extent to which the
socio-economic benefits are realised. We do analyse three different stakeholder
perspectives: that of the a) project manager, b) funder(s), and c) the public. We do control
for two levels of expertise – novices, and semi-experts, and the participants use UJ, SA, IG
and Delphi (D) as well, resulting IG>D>SA>UJ. In the third and final experiment, we
qualitatively explore the use of scenarios in forecasting the success of megaprojects.

Citation

Litsiou, K. Forecasting the success of megaprojects with Judgmental methods. (Thesis). University of Salford

• Thesis Type: Thesis
• Deposit Date: Oct 5, 2021
• Publicly Available Date: Oct 5, 2021
• Award Date: Jul 1, 2021