The engineering body of knowledge contains an array of methodologies and techniques to address the effectiveness and efficiency of operational activities within a manufacturing environment. One such example is simulation modelling, a powerful analytical tool that can potentially be valuable in assisting decision makers, managers and engineers to gauge improvement opportunities and achieve process advancements. However, the cost of ownership for simulation models is not insignificant even for large multinationals, this stems from the requirements for specialist skills in simulation software, model development, data mining and statistical analysis. Simulation projects typically require a large investment to develop and usually are used-once-and-thrown-away. To reuse the model, it would require repeating a large portion of the development cycle. In order for simulation modelling to achieve wider recognition as a decision support tool there is a necessity to reduce the cost of model maintainability, promote reusability, increase flexibility and improve user friendliness. The research proposed framework intends to achieve four goals. i.) Improve and advance the deployment and maintenance requirements of simulation projects in comparison to traditional methods. ii.) Integrate automation into model deployment phase of a simulation projects. Thus, allowing unique user-specified simulation models to be generated by automatically extracting and manipulating data from factory databases. iii.) Enforce a strong documentation technique to achieve interoperability and re-traceability of project progress, therefore permitting programme code or even entire models to be reused and utilised in future projects. iv.) Advance user friendliness and acceptance towards simulation modelling. Reducing the expertise required to conduct simulation studies will improve the programming exercise image associated with typical simulation studies. This framework assists in developing customised simulation modules. These modules facilitate automated online rapid development of reconfigurable, flexible, self-maintaining simulation models, aiming to deliver tailored analysis to support real-time operational decision making.