Project selection in project portfolio management: An artificial neural network model based on critical success factors

• We propose an innovative methodology to help managers in assessing projects during the selection phase.
• An artificial neural network (ANN) model automatically relates CSFs to project success, according to the company experience.
• This methodology can apply to any environment, industry or company, with general or dedicated framework of CSFs.
• We explain the practical use of the model, to clarify its use and managerial implications.
• We provide opportunities for future research for the use of ANN in project selection processes.

While a growing body of literature focuses in detecting and analyzing the main reasons affecting project success, the use of these results in project portfolio management is still under investigation. Project critical success factors (CSFs) can serve as the fundamental criteria to prevent possible causes of failures with an effective project selection process, taking into account company strategic objectives, project manager’s experience and the competitive environment.This research proposes an innovative methodology to help managers in assessing projects during the selection phase. The paper describes the design, development and testing stages of a decision support system to predict project performances. An artificial neural network (ANN), scalable to any set of CSFs, classifies the level of project’s riskiness by extracting the experience of project managers from a set of past successful and unsuccessful projects.