R&D Project Selection Problem: An analysis and application of AHP and PROMETHEE

Nowadays, innovation is getting an ever-increasing importance as competition and demand acquire higher complexity. For this reason, R&D projects are crucially important for the success of companies. It is common that very few projects are selected among the initial proposals, because of time and resources constraints, and thus it is important for companies to have an established selection method to prioritize each proposal. Project selection is thus an essential process and is the focus of this thesis. Project Portfolio Selection has been extensively studied in the literature, defining plenty of approaches and methods to handle this issue. In this work, particular emphasis is put on the models that consider multiple criteria for the prioritization and selection of projects, and two among the most popular ones, AHP and PROMETHEE, will be studied in detail. The main objectives of this thesis are to illustrate the characteristics of project portfolio management and the projects selection process; describe R&D projects and R&D project selection challenges; identify selection criteria and MCDM methods; illustrate how the chosen methodologies can be implemented to successfully prioritize and select projects, and a final comparison between the two methods is conducted. In order to do so, a case study from Harvard Business School has been selected and analyzed. This case study regards the selection of R&D projects of Vertex, an American pharmaceutical company. Finally, an Excel template for implementation of AHP and PROMETHEE is modelled for the case study.