Title: PPM-HC: a method for helping project portfolio management based on topic hierarchy learnings

Authors: Ricardo A.M. Pinto; Flavia Bernardini; Ricardo M. Marcacini

Addresses: Graduate Program in Industrial Engineering and Computing Systems, Fluminense Federal University, Jardim Bela Vista, Rio das Ostras, RJ, Brazil ' Computing Institute, Fluminense Federal University, São Domingos, Niterói, RJ, Brazil ' Information Systems, Federal University of Mato Grosso do Sul, P.O. Box 210, Três Lagoas, MS, Brazil

Abstract: The projects categorisation is a crucial step in the project portfolio management (PPM). Categorising projects allows the organisation to identify categories with a lack or excess of projects, according to its strategic objectives. In this work, we present a new method for project portfolio management based on hierarchical clustering (PPM-HC) to organise the projects at several levels of abstraction. In the PPM-HC, similar projects are allocated to the same clusters and subclusters. PPM-HC automatically learns an understandable topic hierarchy from the project portfolio dataset, thereby facilitating the (human) task of exploring, analysing and prioritising the projects of the organisation. We also proposed a card sorting-based technique which allows the evaluation of the projects categorisation using an intuitive visual map. We carried out an experimental evaluation based on a benchmark dataset and we also presented a real-world case study. The results show that the proposed PPM-HC method is promising.

Keywords: project portfolio management; PPM; projects categorisation; topic hierarchy learning; hierarchical clustering.

DOI: 10.1504/IJBIDM.2021.114469

International Journal of Business Intelligence and Data Mining, 2021 Vol.18 No.3, pp.364 - 382

Received: 08 May 2018
Accepted: 02 Sep 2018
Published online: 23 Apr 2021 *

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