Among the causes of the low success rate of the projects (around 35% of the total) is the low level of maturity of the technologies available for the management of the projects themselves. However, today many researchers, startups and innovative companies are starting to apply artificial intelligence (AI), machine learning and other advanced technologies to the field of project management. By 2030 the industry will undergo significant changes. By using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol this paper explores the intersection of project risk management and AI. The study highlights how AI-driven methodologies and tools can revolutionize the way project risks are managed throughout the project lifecycle. Specifically, 215 papers have been analysed to explore how the scientific community has been moving so far on the topic. Besides, a cross-sectional investigation of the PM processes and AI categories/tools was carried out to identify any path that is prevalent, where the prevalence comes from, and for which PM process or sector it is most successful. Finally, from this study several gaps emerged that scientific research would have to fill to effectively implement AI in PM and that have been turned into opportunities for future research in the form of a research agenda.

Forecasting demand is a crucial issue for driving efficient operations management plans. This is especially the case in the fashion industry, where demand uncertainty, lack of historical data and seasonal trends usually coexist. Many approaches to this issue have been proposed in the literature over the past few decades. In this paper, forecasting methods are compared with the aim of linking approaches to the market features.