Using mathematical knowledge management to support integrated decision-making in the enterprise

• Mathematical ontology for representing engineering systems.
• Integration of mathematical models using semantic technology.
• A case study based on supply chain and scheduling levels.
• Improved modeling understanding, knowledge sharing and data mining.

The basis of decision-making in the enterprise consists in formally representing the system and its subsystems in models which adequately capture those features which are necessary to reach consistent decisions. This work represents the elements of the enterprise which are included in mathematical models (i.e. decisions, parameters, constraints, performance indicators) in an ontology which captures the knowledge of the mathematical domain. Thus, this ontology relates the mathematical elements of the models to their corresponding semantic representation within the enterprise ontology. As a result, the mathematical symbolic abstractions of a given enterprise element in different models are directly linked to their actual unique meaning, and the integration of decisions in the enterprise is transparent and improved. The purpose of this work is illustrated in a case study related to capacity planning in the supply chain and scheduling problems.