Modular Design Approach for the Challenges of Effects Based Operations


Effects Based Operations (EBO) is an approach for planning, executing, and assessing any type of operations. EBO focus on the effects of the operations rather than dealing with actions, targets, or even objectives. The literature on EBO has been growing day by day; however, there is still a need for modeling techniques and tools that provide more efficient and effective effects based assessment, planning and analysis in order to further develop the capabilities of the operations. In this context, this paper presents an introduction to EBO by focusing on its methodology, its challenges and also its applicability in different systems. Moreover, this paper illustrates the importance of modular system design in effects based planning stage. Modular design provides synchronization of the right actions and decisions, makes strategic aim consideration easier and provides efficiency in the cases where there are multiple strategic aims. The most important benefit of this research is its ability to facilitate the achievement of economy of national power for military EBO and economy of action sources for other systems. Approaches presented in this paper utilize clustering of effects and actions by using two neural network architectures; namely, Adaptive Resonance Theory (ART1) and Kohonen's Self Organizing Maps (SOM). The applications of the approach are illustrated with a defense industry related example in the development of a modular EBO system. Overall, the modular architecting approach has been successfully applied to the example and it is concluded that although ART1 is a good architecture for clustering, Kohonen's SOM is more helpful in defining modules for effects and actions in EBO. Finally, it is understood that further research of this paper would contribute to the modular design of EBO by applying other neural network architectures with larger input data sets.


Engineering Management and Systems Engineering

Keywords and Phrases

Effects Based Operations; Military Defense; Neural Network Architecture

Document Type

Article - Conference proceedings

Document Version


File Type




This document is currently not available here.