The comprehensive study of Key Performance Indicators (KPIs) in nowadays manufac-turing systems has become a need for improving the efficiency of production processes, pressed by the market demand. To achieve this management, industrial control systems are being modelled robustly by using standards.
This causes developers to use semantic technologies to deal with the complexity of data integration and modelling of systems. On the other hand, ISA-95 is an international standard that reduce human efforts by helping directly on the business logistics of man-ufacturing systems. Then, ISA-95-based implementations can be developed for data integration.
Moreover, the current trend on systems modelling is the use of ontologies which provide models to be more descriptive, allowing knowledge to be decoupled from busi-ness logic, and extending modularity of the domain knowledge. In addition, the applica-tion of AI to industrial control systems is being developed by the interoperability be-tween a knowledge base, created by ontologies, and a reasoner.
This thesis proposes a methodology for modelling configuration for heterogeneous data integration while considering the various information models contained in the sys-tems of the modelled manufacturing systems. The implementation of this work not only presents how to model a production line as an example of manufacturing system, but also allows carrying out the configuration of a Function Block Network (FBN) by speci-fying a KPI. Then, this work pretends to present a possible manner of KPI classification and its calculation by configuring a FBN, all of this by the use of ontologies
