In aeronautics today, manufacturers make extensive use of modelling and simulation capabilities with the purpose to design and evaluate specific engineering tasks and related parameters. The objective with these activities is to further reduce cost, lead-time and increase quality to strive for greater competitiveness, market share and sustainability. In recent years, aeronautics have shown interest in the concept of providing a Total Offer (TO) or selling a Functional Product (FP) [Alonso-Rasgado et al, 2004] (a.k.a. Product Service System (PSS) [Matzen et al, 2005]). The functional product, consisting of both hardware and service components developed simultaneously, provided as a function to the customers, calls for a different approach in the development process, i.e. a Functional Product Development (FPD) process [Nergård et al, 2006]. The main reason for this is the perspective of the product’s life cycle. Instead of components being sold to and owned by the customer, the hardware and service provided as FP implies that ownership and thus the risk remains with the manufacturer throughout the life cycle of the provided function. In order to reduce the risk, and make use of the possibilities for continuous product development and remanufacturing, companies are moving towards making more use of modelling and simulation capabilities not only for the design but also in order to decide whether to offer the product as a FP or as a traditional hardware product. Modelling and simulation methods such as Computer Aided Design (CAD), Finite Element Analysis (FEA), multi-body dynamics (MBS) and Computational Fluid Dynamics (CFD) are relatively mature and extensively used in most product development projects in aeronautics as design and development support tools. As these methods are maturing, they are integrated in support tools for engineers, such as Knowledge Based Engineering (KBE) applications to a larger extent. However, these support tools are still used for the design and verification of specific engineering activities. This perspective supports design on a micro (individual activities) level, while on a macro level, with a holistic perspective of the product development process (PDP), the individual PD-activates can be seen as building blocks of the total system. Although it is possible to use these Knowledge Engineering (KE) applications to model the overall macro-level PD-process there are some issues that makes them less suitable. The level of detail in KE applications is not interesting in a macro-level model where the behaviour and interaction between applications, people, and resources are more interesting. The time each iteration takes has also to be considered, due to the fact that a model that takes hours to simulate is not suitable for use in a macro-level model where the simulation is run over longer model time intervals. Agent-based modelling [Sichman et al, 1998] is an approach where agents (i.e. micro level activities) are utilised to build a system (i.e. process) bottom-up by modelling the behaviour and interaction of the agents in a certain environment. This approach seems to be suitable from a macro level perspective, as information about PD-activities can be included in the agent’s behaviour. The objective of this paper is to discuss agent based modelling and simulation as decision support in functional product development, and to show example of the approach.