Using conventional solvers, the simulation of a complex and large system such as the automotive paint ovens can be quite time consuming - of the order of a several weeks or even months. A reduced order computational model of the oven that can predict thermal distribution quicker is useful in performing optimization studies and in directing finer design changes to the oven and the car body. This research focuses on the development of such a lumped capacitance thermal model (defined here in as the reduced order model: ROM) for predicting the heat of curing of an object that is inside an industrial oven. Essentially, the heat transfer modes are computed through a set of linear ordinary differential equations, by conceptualising the the physical object is conceptualised as a series of inter-connecting nodes that are linked by thermal resistors. The aim of this model is to be able to predict the heat transfer modes in the oven within an engineering-acceptable range in a significantly lower time and effort when compared to a fully transient computational fluid dynamics (CFD) simulation. The main motivation for this research is the fact that use of CFD to predict the fluid flow and heat transfer in an industrial oven is very expensive and time-consuming due to the transient behaviour and complicated physical geometry in the target object. In this regard, the ROM follows the strategy of decoupling the CFD into providing the flow-derived inputs for the thermal calculations, which are subsequently performed in the ROM.Two experimental validations were carried out on the ROM - one using a simple laboratory oven set-up and the other using an actual automotive paint shop oven with a full car body inside it. The results from the validation studies are in good agreement and helped achieve a significant level of confidence, reinforcing the applicability of the ROM.