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The increasing complexity of aerospace products and programs and the growing competitive pressure is facilitating the aggregation of small, medium and large enterprises of certain geographical regions into more integrated and collaborative entities (clusters). Clusters are by their same nature formed by heterogeneous companies, with huge differences not only in size but also for their core competences: such a diversity is a strength of the cluster, but it also increases its complexity. The purpose of this paper is to describe a benchmarking methodology that can be adopted to assess the performances of companies belonging to a cluster from different perspectives: economics and financials, competitive differentiators, specific know how, business strategies, production and logistic effectiveness, quality of core and supporting processes.

Simulation-based tolerance analysis is the accepted standard for dimensional engineering in aerospace today. Sophisticated 3D model-based tolerance analysis processes enable engineers to measure variation in complex, often large, assembled products quickly and accurately. Best-in-class manufacturers have adopted Quality Intelligence Management tools for collecting and consolidating this measurement data. Their goal is to completely understand dimensional fit characteristics and quality status before commencing the build process. This results in shorter launch cycles, improved process capabilities, reduced scrap and less production downtime. This paper describes how to use simulation-based approaches to correlate the theoretical tolerance analysis results produced during engineering simulations to actual as-built results. This allows engineers to validate or adjust as-designed simulation parameters to more closely align to production process capabilities.

Given the fast changing market demands, the growing complexity of features, the shorter time to market, and the design/development constraints, the need for efficient and effective verification and validation methods are becoming critical for vehicle manufacturers and suppliers. One such example is fault-tree analysis. While fault-tree analysis is an important hazard analysis/verification activity, the current process of translating design details (e.g., system level and software level) is manual. Current experience indicates that fault tree analysis involves both creative deductive thinking and more mechanical steps, which typically involve instantiating gates and events in fault trees following fixed patterns. Specifically for software fault tree analysis, a number of the development steps typically involve instantiating fixed patterns of gates and events based upon the structure of the code. In this work, we investigate a methodology to translate software programs to fault trees.