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Digital maturity models: comparing manual and semi-automatic similarity assessment frameworks

Article dans une revue avec comité de lecture
Author
COGNET, Bruno
136804 Ecole de Technologie Supérieure [Montréal] [ETS]
RIVEST, Louis
226819 Faculty of Engineering and Applied Science
136804 Ecole de Technologie Supérieure [Montréal] [ETS]
DANJOU, Christophe
ccPERNOT, Jean-Philippe
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]

URI
http://hdl.handle.net/10985/22779
DOI
10.1504/ijplm.2021.10043128
Date
2021-11-29
Journal
International Journal of Product Lifecycle Management

Abstract

The fourth industrial revolution is forcing companies to define their digital strategy, making it imperative that they assess their digital maturity as a basis for improvements. As a result, a variety of maturity models have emerged. However, it can be difficult to identify which one is most appropriate. This paper introduces a new methodology to compare a manual and a semi automatic framework for assessing the similarity of digital maturity models. It allows identifying the most adequate framework for comparing maturity models. Both frameworks have been designed to identify correspondences between KPIs. The analysis of the matches and the obtained results are then used to tune the semi-automatic framework. The proposed comparison methodology has been validated using two digital maturity models and shows that the semi-automatic framework provides good results in a very efficient manner. Several insights have been derived and will help to develop a new maturity model.

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