Modelling and simulation is at the heart of Digital Twin technology, which is revolutionising many industries. Organisations have access to legacy models that are too complex to maintain, preventing them from operationalising complex systems like Digital Twin. By creating an automated tool for model decomposition, it is possible to breathe life into complex models by extracting their embedded functionality as managable components. This thesis presents NewMODE as a network theory approach to model decomposition; a novel methodology that aims to automate the tedious task of model decomposition. Important contributions include the network theory metamodel specification, the decomposition criteria and the adaption of the Girvan Newman algorithm to identify components of the model. NewMODE has been implemented for models in developed in LSAT (Logistics Specification and Analysis Tool). In partnership with TNO and their Embedded Systems Innovation (ESI) group, NewMODE has been evaluated quantitatively and qualitatively with promising results to aid model developers in model decomposition.