Identifying Inter-Project Relationships with Recurrent Neural Networks: Towards an AI Framework of Project Success Prediction

A growing number of emerging studies have been undertaken to examine the
mediating dynamics between intelligent agents, activities, and cost within allocated
budgets to predict the outcomes of complex projects in dint of their significant
uncertain nature in achieving a successful outcome. Emerging studies have used
machine learning models to perform predictions, and artificial neural networks are
the most frequently used machine learning model. However, most machine
learning algorithms used in prior studies generally assume that input features, such
as project complexity, team size and strategic importance, and prediction outputs,
are independent. That is, a project’s success is assumed to be independent of other
projects. As the datasets used to train in prior studies often contain projects from
different clients across industries, this theoretical assumption remains tenable.
However, in practice, projects are often interrelated across several dimensions,
such as distributed overlapping teams. Therefore, we argue that the inter-project
relationships should be taken into consideration to improve prediction
performance. Furthermore, an ongoing ethnographic study at a leading project
management artificial intelligence consultancy, referred to in this research as
Company Alpha, suggests that projects within the same portfolio frequently share
overlapping characteristics. To capture the emergent inter-project relationships,
this study aims to compare two specific types of artificial neural network prediction
performances; (i) multilayer perceptron and; (ii) recurrent neural networks. The
multilayer perceptron is one of the most widely used artificial neural networks in
the project management literature, and recurrent networks are distinguished by the
memory they take from prior inputs to influence input and output. Through this
comparison, this research will examine whether recurrent neural networks can
capture the potential inter-project relationship towards achieving improved
performance in contrast to multilayer perceptron. Our empirical investigation using
ethnographic practice-based exploration at Company Alpha will contribute to
project management knowledge and support developing an intelligent project
prediction AI framework with future applications for project practice.