The Role of Machine Learning in Managing Uncertainty in Projects – A View on Early Warning Systems

Abstract

Machine learning techniques deals with, among other things, pattern recognition in large amounts of data to identify trends and possible events in the future regarding a given topic of interest. Machine learning methods are useful for addressing challenges in and creating new benefits for organisations. This paper looks at how machine learning can contribute to manage projects effectively. Many organisations apply the concept of project. A part of them are purely project-based organisations, and a part of them carry out projects in addition to their mass-production activities and permanent operations. Within the realm of project management, this paper sets its focus on studying the role of machine learning in handling unexpected events and uncertainty in projects. One of the ways to deal with unexpected events and uncertainty is to capture early warning signs that can predict unexpected events. A major failure of projects can be seen as a combined effect of a series of small failures, negative results or problems that have occurred over a period of time. Project teams may not notice or just ignore early warning signs of these problems and choose to work further in the project. This could finally lead to a major failure, at which point no preventive actions could save the project from the major failure. Several researchers have researched on early warning signs and systems within the context of projects. Early warning signs can be seen as some kind of a pattern recognition from a pool of relevant data. This paper aims to answer the following two interrelated research questions: (1) What role does machine learning have in early warnings in projects? (2) How can machine learning contribute to effective project management (for example, handling uncertainty in projects)? This is a conceptual paper, based on literature study.