Back to Search Start Over

Study on project management in Portugal within the scope of the Portuguese Project Management Observatory.

Authors :
Miranda, Catarina
Tereso, Anabela
Gonçalves, A.Manuela
Sousa, Paulo
Engrácia, Pedro
Source :
Procedia Computer Science; 2023, Vol. 219, p1885-1892, 8p
Publication Year :
2023

Abstract

The Portuguese Project Management Observatory (PPMO), an initiative of the Portuguese Association of Project Management (APOGEP), is being developed by the University of Minho in partnership with other 17 Higher Education institutions. The main objectives of this research were to understand the tools and techniques most and least used by organizations, the use of agile methodologies, the maturity of each Project Management area, and success dimensions. The method selected for this study was a survey applied through an online questionnaire directed to Portuguese organizations. The results show that the most used Tools and Techniques are Kick-off Meeting, Progress Meetings, Project Work Description, Gantt Chart, and Activity List; and the least used are Monte Carlo Analysis, Decision Tree, Project Management Software for Simulation, Conferences for Bidding, and Parametric Estimation. Statistically significant differences were found between the use of various Tools and Techniques and factors such as gender, age, current position, education level, and activity sector. Agile methodologies are used in a large part of the respondents' organizations, however, no correlation was identified between the use of agile methodologies and the accomplishment of scope, time and cost of projects. The process identified as having the highest maturity is Definition of Activities in the Project Schedule Management area, followed by Project Execution in the Project Integration Management area, and the Schedule Development in the Project Schedule Management area. Customer Satisfaction is the KPI most used. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18770509
Volume :
219
Database :
Supplemental Index
Journal :
Procedia Computer Science
Publication Type :
Academic Journal
Accession number :
162590675
Full Text :
https://doi.org/10.1016/j.procs.2023.01.487