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<p>The Global Combat Support System-Marine Corps (GCSS-MC) system enables the logistics and supply chain management system for the United States Marine Corps. This system allows leaders, operators, maintainers, and suppliers to work together on a common platform and provides transparency and situational awareness in the logistics and supply cycle. The data associated with the GCSS-MC system is stored in a database. As the size of the data increases, challenges arise in obtaining insights into large data sets and its impact on network infrastructure. Network science identifies relationships between objects and provides tools to quantitatively determine objects whose influence impacts other objects or the system as a whole. This thesis applies network science techniques to determine important Table of Authorized Material Control Numbers (TAMCNs) in the GCSS-MC database, according to their impact on other TAMCNs in the database based on degree, eigenvector, betweenness, and closeness centrality. Additionally, this thesis develops a formula to rank components from most to least important. We further develop a process to identify pertinent tables within the database and export the information to create a complex network containing multiple layers that analyze various attributes associated with GCSS-MC. We find that the methodology identifies the most important TAMCN and provides a list of TAMCNs in order of importance. We also analyze the community and core structure of the GCSS-MC complex network and identify influential TAMCN regions in the database.
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<br>Subjects: GCSS-MC; database; centrality; influence; graph; network science</div>
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Castro, Victor G.</div>
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