Computer-aided engineering (CAE) software tools for design representation, analysis and optimization play a central role in aerospace engineering. Increasingly these tools are being integrated to automate data transfer, thereby reducing design cycle times and increasing the power of design search and optimization techniques. The majority of work concerning process integration and design automation has focused on the technical issues involved in improving the capability and interoperability of design tools and the means of incorporating them into automated workflows. Of lesser concern has been assessing the performance of these workflows prior to implementation. This paper reports on research at Rolls-Royce that used process mapping and discrete event simulation to help design and predict the performance of an automated design system that is being developed for the early stages of civil aero engine design. The findings of this research are that process simulation can add value to mapping the design process by quantifying the expected outcomes of different implementation scenarios and, thereby, indicating opportunities for further process improvements.@en

Many aerospace companies are currently making the transition to providing fully-integrated product-service offerings in which their products are designed from the outset with life-cycle considerations in mind. Based on a case study at Rolls-Royce, Civil Aerospace, this paper demonstrates how an interactive approach to process simulation can be used to support the redesign of existing design processes in order to incorporate life-cycle engineering (LCE) considerations. The case study provides insights into the problems of redesigning the conceptual stages of a complex, concurrent engineering design process and the practical value of process simulation as a tool to support the specification of process changes in the context of engineering design. The paper also illustrates how development of a simulation model can provide significant benefit to companies through the understanding of process behaviour that is gained through validating the behaviour of the model using different design and iteration scenarios. Keywords: jet engine design; life-cycle engineering; LCE; process change; design process simulation; applied signposting model; ASM.@en

Task-network modelling approaches are widely used to understand and improve product development (PD) processes. The best-practice application of these tools is often presented prescriptively. This paper proposes that best-practice, and academic understanding, of PD process modelling can be further developed through an empirical approach, in which modelling tools are taken as given and practice is analysed in terms of how industry stakeholders use these tools. We argue this inductive approach can result in process modelling guidelines which explicitly recognise the challenges of modelling in industry. We thus analyse four cases of process modelling at Rolls-Royce plc and find that: 1) modelling is a social process of knowledge-creation; 2) effective modelling requires selection of the appropriate methods and tools at the right time; and 3) understanding the purpose of each interaction between participants in the modelling process helps to choose the best approach. To conclude we present guidelines to suggest which modelling approach should be used in which context, where context is described in terms of the purpose of a given modelling interaction.@en

This paper discusses a case study in which process simulation was applied to support the integration of life-cycle engineering activities into the existing design process at a major UK manufacturer of capital equipment. we demonstrate the practical value of process simulation as a tool to support the specification of changes to a complex, concurrent engineering design process. The paper also liilustrates how development of a design process simulation model can provide significant benefit to companies, not just in terms of the numerical results of simulation analysis, but through the understanding of process behaviour which is gained through validating the behaviour of the model in different iteration scenarios.

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