This paper proposes a model that integrates information from product, process and organisation domains with a view to help manage these complex interrelationships with multiple layers of interaction. This model incorporates an integrated mechanism that simulates change effects during the design of complex manufacturing system by populating a Multi-layered Domain Matrix (MDM) and applying a Change Prediction Model (CPM) propagation mechanism to interconnected elements.@en

In Product Development (PD), there is an inherent complexity in deciding what resources should perform which tasks taking into account their effectiveness towards completing the task, while adjusting to their availabilities. The right resources must be applied to the right tasks in the correct order. In this context, process modeling 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 "designers". Despite their crucial importance to the delivery of the product, resources such as computational hardware, software, testing resources, amongst others have been overlooked during process planning stages. This paper presents a new method to model different resource types (designers, computational, testing) and studies the impact of using different options of those resources by simulating the model and analyzing the results. Thus, the new approach, which extends a task network model with Bayesian Networks (BN), allows testing the influence of using different resources on process performance. The method 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. Thus, activity behavior is shaped depending on the chosen resource option to perform it. The approach enhances the capability to explore resource combination design space. It was applied to an aerospace case study to identify insights such as the best performing resource combinations, critical resources, resource sensitive activities, and the probability of a resource reaching performance targets.@en