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Applying the Method of Reflections through an Event Log for Evidence-based Process Innovation

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Date
2019
Dewey
Recherche opérationnelle
Sujet
Process Innovation; Bipartite Network; Evidence-based Decision Support
DOI
http://dx.doi.org/10.1109/ICPM.2019.00025
Conference name
2019 International Conference on Process Mining ICPM 2019
Conference date
06-2019
Conference city
Aachen
Conference country
Germany
Book title
2019 International Conference on Process Mining ICPM 2019
Author
Müller, Hausi
Publisher
Institute of Electrical and Electronics Engineers
Year
2019
Pages number
146
ISBN
978-1-7281-0919-0
URI
https://basepub.dauphine.fr/handle/123456789/20437
Collections
  • LAMSADE : Publications
Metadata
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Author
Delias, Pavlos
115536 autre
Acheli, Mehdi
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Grigori, Daniela
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Type
Communication / Conférence
Item number of pages
105-112
Abstract (EN)
Flexibility and adaptability have been praised and empirically validated as critical for an organization's performance. They are associated with the number of different behaviors that the organization exhibits, as well as with their level of sophistication, which in turn implies the number and the quality of the available knowledge and capabilities in place. In this work, we propose the creation of a bipartite network, involving organizations and behaviors, through an event log, to capture the prospects of both organizations and behaviors for process innovation. By using the Method of Reflections, we claim that the structure of that network can expose a set of sophistication metrics, that eventually suggest i) the potential of behaviors for better organizational performance; ii) the most reachable innovation paths; iii) an "order of significance" over the behaviors considering their improvement contribution power. We show the practical potential of our approach through a Proof of Concept based on a real dataset.

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