This graduation thesis aims to provide solutions for the improvement of the quality of estimations at the first stage (fuzzy front-end) of projects. In order to do so, three research questions were answered: How are the project front-end estimations currently made and by whom? What knowledge sources are used in the process of estimations and to what extent? And what are the main factors that make difficult the practice of front-end estimation and how can they be overcome? A qualitative research was carried out and four data collection methods were used: literature review, corporate archival records analysis, structured interviews and non-participatory observations. The research is carried out in the context of the Healthcare Imaging Systems industry; a sector that offers interesting characteristics for carrying out the proposed research. The study does an extensive exploration of how to improve the quality of estimations. A categorization scheme was developed to structure the discussions. It was found that obstacles and solutions can be divided in 6 major categories: Political, Organizational, Methodological, Technical, General and External. The study also has some limitations. The internal validity is limited at some extent due to the number of respondents. Results can hardly be generalized; however, they provide interesting insights as explained below. This limitation is compensated by the fact that all the interviewees possess extensive technical experience in the organization in addition to their managerial expertise thus providing a balanced mix between technical and managerial insights. Summarizing, the ultimate contribution of this research is to identify the main factors that hinder the practice of carrying out front-end estimations. By analyzing these issues and contrasting them with the good practices found in literature and in a complex environment like that of the Healthcare Imaging Systems sector, this study aims to shorten the research gaps mentioned above and contribute to the body of knowledge of project’s estimation. The main conclusions are that estimations in the context of this industry are carried out primarily using expert knowledge and standard times and that even though current estimations lead to the success of the organization, some opportunities of improvement were found. The high granularity and variety of the obstacles and solutions found in literature and in the empirics makes them quite precise, recognizable and addressable in the case study organization. However, it makes it difficult to consolidate them without losing valuable details.