Causality learning approach for supervision in the context of Industry 4.0
TypeCommunications avec actes
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.
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