• français
    • English
    français
  • Login
Help
View Item 
  •   Home
  • Laboratoire d’Ingénierie des Systèmes Physiques Et Numériques (LISPEN)
  • View Item
  • Home
  • Laboratoire d’Ingénierie des Systèmes Physiques Et Numériques (LISPEN)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Causality learning approach for supervision in the context of Industry 4.0

Communication avec acte
Author
AMZIL, Kenza
ccROUCOULES, Lionel
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
ccYAHIA, Esma
ccKLEMENT, Nathalie

URI
http://hdl.handle.net/10985/19401
Date
2021

Abstract

In order to have a full control on their processes, companies need to ensure real time monitoring and supervision using Key performance Indicators (KPI). KPIs serve as a powerful tool to inform about the process flow status and objectives’ achievement. Although experts are consulted to analyze, interpret, and explain KPIs’ values in order to extensively identify all influencing factors; this does not seem completely guaranteed if they only rely on their experience. In this paper, the authors propose a generic causality learning approach for monitoring and supervision. A causality analysis of KPIs’ values is hence presented, in addition to a prioritization of their influencing factors in order to provide a decision support. A KPI prediction is also suggested so that actions can be anticipated.

Files in this item

Name:
LISPEN_JCM_2020_AMZIL.pdf
Size:
946.4Kb
Format:
PDF
View/Open

Collections

  • Laboratoire d’Ingénierie des Systèmes Physiques Et Numériques (LISPEN)

Related items

Showing items related by title, author, creator and subject.

  • Automatic neural networks construction and causality ranking for faster and more consistent decision making 
    Article dans une revue avec comité de lecture
    AMZIL, Kenza; ccYAHIA, Esma; ccKLEMENT, Nathalie; ccROUCOULES, Lionel (Taylor & Francis, 2022-11-11)
    The growth of Information Technologies in industrial contexts have resulted in data proliferation. These data often underlines useful information which can be of great benefit when it comes to decision-making. Key ...
  • A Process Mining Based Approach to Support Decision Making 
    Communication avec acte
    ES-SOUFI, Widad; ccROUCOULES, Lionel; ccYAHIA, Esma (Springer, 2017)
    Currently, organizations tend to reuse their past knowledge to make good decisions quickly and effectively and thus, to improve their business processes performance in terms of time, quality, efficiency, etc. Process mining ...
  • Digital Continuity Based on Reinforcement Learning Model Transformation 
    Conférence invitée
    BRILHAULT, Quentin; ccESMA, YAHIA; ccLIONEL, ROUCOULES (Springer, 2022-09-25)
    With the importance gained by Service-Oriented Architectures (SOA) to simplify and decompose complex enterprise information system into autonomous, modular, reusable and, flexible model, the need to make models interoperable ...
  • Design process and trace modelling for design rationale capture 
    communication avec actes
    MOONES, Emna; ccROUCOULES, Lionel; ccYAHIA, Esma (2014)
    To face the high industrial concurrence and to remain competitive, companies are asked to work in a context of collaborative engineering environment where design rationale is a prerogative to reduce their product development ...
  • On the use of Process Mining and Machine Learning to support decision making in systems design 
    Communication avec acte
    ES-SOUFI, Widad; ccROUCOULES, Lionel; ccYAHIA, Esma (Springer, 2016)
    Research on process mining and machine learning techniques has recently received a significant amount of attention by product development and management communities. Indeed, these techniques allow both an automatic process ...

Browse

All SAMCommunities & CollectionsAuthorsIssue DateCenter / InstitutionThis CollectionAuthorsIssue DateCenter / Institution

Newsletter

Latest newsletterPrevious newsletters

Statistics

Most Popular ItemsStatistics by CountryMost Popular Authors

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales