Developing and applying a methodological framework for selecting/prioritising projects

Contemporary organisations increasingly rely on structured decision-support approaches to prioritise projects under conditions of uncertainty, competing objectives, and limited resources. Despite the extensive literature on Multi-Criteria Decision-Making (MCDM), limited guidance exists on how decision-makers should select the most appropriate method for a specific organisational context. This thesis addresses this gap by proposing a structured methodological framework that supports transparent and defensible project prioritisation. The research adopts a methodological and applied approach, combining theoretical synthesis with empirical validation. A unified comparative structure is used to analyse major MCDM techniques, mapping each method to problem characteristics, data requirements, and application domains. Based on this analysis, a Decision Tree is developed to guide method selection and reduce ad-hoc decision-making practices. The proposed framework is demonstrated through a banking-sector case study, where projects of different types are evaluated using the Fuzzy TOPSIS method. The approach integrates quantitative data with expert judgement through fuzzy representations, enabling the systematic handling of uncertainty while maintaining interpretability for decision-makers. The resulting ranking highlights the importance of balanced performance across multiple criteria and illustrates how structured methodologies can enhance transparency in portfolio decisions. Beyond the specific application, the thesis contributes by bridging theoretical MCDM concepts with organisational practice, offering a practical decision-support tool adaptable to diverse strategic environments. Future research directions include the integration of hybrid MCDM approaches and artificial intelligence techniques to support dynamic and data-driven project portfolio management.