AbstractIn 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.
Industrial Application of Multi-criterial Decision Support to improve the Resource Efficiency**The research leading to these results was done within the project MORE (Real-time Monitoring and Optimization of Resource Efficiency in Integrated Processing Plants). The project has received funding from the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement No 604068.
