AI-Powered Delay Prediction for Portfolio Management
G.C. dos Santos Rocha (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Arie Deursen – Mentor (TU Delft - Software Engineering)
Rini van Van Solingen – Mentor (TU Delft - Software Engineering)
U.K. Gadiraju – Graduation committee member (TU Delft - Web Information Systems)
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Abstract
Delayed software projects are one of the biggest threats to the integrity of many project portfolios. If portfolio managers were able to foresee delays, they could better manage risks, make adjustments to the planning and reduce delay propagation. In their 2023 paper "Dynamic Prediction of Delays in Software Projects using Delay Patterns and Bayesian Modeling", Kula et al. propose an AI solution for the problem of ineffective delay prediction of software projects. Even though Kula et al. achieved positive results, they are bound to ING’s data, and thus may not be representative of software projects in other companies or industries. This thesis builds on Kula et al.’s work by applying the same methodology to a new dataset - Coca-Cola Hellenic's Project Portfolio. By doing so, it assesses the robustness and generalisability of Kula et al.'s delay prediction model. The results clearly indicate that the model was unsuccessful at Coca-Cola Hellenic, as it proved no better than random guessing.
Differences in dataset size and quality were identified as the primary cause for the lack of performance. Furthermore, contextual factors were likely a major contribution to the difference in results, namely differences in industry, organisational structure and agile maturity. These findings are valuable to anyone attempting to replicate this solution, or to organisations aiming to adopt AI-powered analytics.
Future research directions are suggested, such as a requirement framework for AI solutions and further replication of Kula et al.'s work in different contexts.