Applied algorithmic machine learning for intelligent project prediction: towards an AI framework of project success

Abstract

A growing number of emerging studies have been undertaken to examine the mediating dynamics between intelligent agents, activities, and cost within allocated budgets, in order to predict the outcomes of complex projects in dint of their significant uncertain nature in achieving a successful outcome. For example, prior studies have used machine learning models to calculate and perform predictions. Artificial neural networks are the most frequently used machine learning model with support vector machine, and genetic algorithm and decision trees are sometimes used in several related studies. Furthermore, most machine learning algorithms used in prior studies generally assume that inputs and outputs are independent of each other, which suggests that a project's success is expected 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 different dimensions, for example through distributed overlapping teams. 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 has been found to be 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.

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Identifiers

ORCID for Nicholas Dacre: orcid.org/0000-0002-9667-9331

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