The planning and scheduling of appropriate resources is essential in engineering design for delivering quality products on time, within cost and at acceptable risk. There is an inherent complexity in deciding what resources should perform which tasks taking into account their effectiveness towards completing the task, whilst adjusting to their availabilities. The right resources must be applied to the right tasks in the correct order. In this context, process modelling and simulation could aid in resource management decision making. However, most approaches define resources as elements needed to perform the activities without defining their characteristics, or use a single classification such as human designers. Other resources such as computational and testing resources, amongst others have been overlooked during process planning stages.

In order to achieve this, literature and empirical investigations were conducted. Firstly, literature investigations focused on what elements have been considered design resources by current modelling approaches. Secondly, empirical studies characterised key design resources, which included designers, computational, testing and prototyping resources. The findings advocated for an approach that allows allocation flexibility to balance different resource instances within the process. In addition, capabilities to diagnose the impact of attaining specific performance to search for a preferred resource allocation were also required.

Therefore, the thesis presents a new method to model different resource types with their attributes and studies the impact of using different instances of those resources by simulating the model and analysing the results. The method, which extends a task network model, Applied Signposting Model (ASM), with Bayesian Networks (BN), allows testing the influence of using different resources combinations on process performance. The model uses BN within each task to model different instances of resources that carries out the design activities (computational, designers and testing) along with its configurable attributes (time, risk, learning curve etc.), and tasks requirements.

The model was embedded in an approach and was evaluated by applying it to two aerospace case studies. The results identified insights to improve process performance such as the best performing resource combinations, resource utilisation, resource sensitive activities, the impact of different variables, and the probability of reaching set performance targets by the different resource instances.