Implications of industrial megaprojects complexity in data-driven forecasting

Abstract

The fourth industrial revolution has led to widespread digitalization across the delivery chain of construction and project industries. The digitalization of project processes and other technological enhancements such as digital project delivery, building information modeling, and digital twins have created vast amounts of promising data. At the same time, data science, artificial intelligence, and machine learning are becoming ever more streamlined and available for a variety of tasks, raising debates and discussions about what is project analytics and how analytics processes and functions can create a competitive advantage. Systematic approach to this issue requires a holistic view into three aspects of it: first, inherent complexities of projects as complex and interdependent systems; second, nature of project delivery processes, management, and controls; and third, knowledge of high performance analytical algorithms, tools, and trends in technological innovation. This paper provides an overview of the authors’ experiences and perspectives on project complexities, and success factors in dealing with such complexities. A framework is then proposed for successful project analytics functions.