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EDA, the European Defence Analytics System, is a Modelling and Simulation Project for the WIP European defence strategy. It's a supranational solution for the EU members to intensify the military cooperation among the EU Member states
SWOT Analysis for EDA |
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Helpful | Harmful | |
Internal |
Strengths• Programme Preparation: development of future operating concepts and capability management activities. • Operational Analysis: analytical techniques used to inform defence decision making • System Development: acquisition, development and fielding of new or enhanced military capabilities • Training: development of in-service doctrine, analysis to identify training gaps, retention issues, alternative training methods, and Live, Virtual or Constructive military training: • Support to Operations: decision making support to the planning and conduct of operational activities. |
Weaknesses• Low ease of use • High implementation/ customisation cost • The technologies in question must be mature enough to be included in defence platforms and systems. |
External |
Opportunities• Internet of Things • Autonomous systems (drones etc) • Additive Manufacturing (AM), known as 3D-printing, has been identified by the European Commission as one of the key enabling technologies to improve European industrial competitiveness given its ability for rapid, delocalised and flexible manufacturing. • Understanding the impact on Modelling & Simulation across the full breadth of its use in the life cycle of future military systems. • Exploring the Big Data domain to understand how its tools and techniques could best be applied to Modelling & Simulation (M&S) activities in the Defence environment • Investment in innovative hardware and software architectures (such as open-standard Hadoop Distributed File System and associated application MapReduce). • Enhancing military capabilities |
Threats• Cyber-attack: a constant battle which constrains the pace of change • Political, cultural, sociological and regulatory issues as well as their potential ethical and legal implications • Safety constraints need to be considered (autonomous systems) • Volume and velocity of data • Rapid acceleration of data growth • Increased data volumes pushed into the network • Growing variation in types of data assets for analysis • Alternative and unsynchronised methods for facilitating data delivery • Rising demand for real-time integration of analytical result. |
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