Abstract
This dissertation presents a systematic literature review (SLR) that explores the application of predictive analytics techniques to mitigate project delays in R&D project management organizations. The review aims to identify predictive tools and techniques, including their associated benefits in R&D organizations, specifically R&D funding organizations and R&D conducting research organizations. This knowledge can effectively assist project managers in proactively managing project uncertainties and delays in R&D project management phases. The literature indicates a limited number of systematic reviews have assessed the application of predictive analytics in R&D management. Therefore, this study assessed 12 peer-reviewed articles published between 2015 and early 2025, following the PRISMA reporting guidelines, to investigate the use of predictive analytics techniques and the benefits associated with their implementation in R&D organisations. The findings suggested that machine learning algorithms, including neural networks, decision trees, random forests, naïve bayes, deep learning models, and logistic regression, are commonly used in R&D project management to predict risks, predict performance, and allocate resources. Furthermore, these tools offer benefits such as enhanced decision-making processes and stakeholder communications, reduced project delays and risks, and improved performance evaluation. These tools have shown robust performance and excellent compatibility with existing project management systems. However, smaller R&D organizations must be aware of potential barriers, such as the complexity of technology and limited resources during implementation. It is therefore recommended that future research assess how R&D organizations can mitigate these potential barriers to ensure the effective adoption of these technologies.