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ACTIVE PROJECTS
• Network / graph analysis• Text classification / clustering• Behavioral clustering and efficiency
metrics
Social Network AnalysisObjective
1. Visualize the structure of the supplier network at UCB.
2. Develop measures to analyze what departments have similar supply chain networks.
Analysis and Metrics
• Three analytical measures will be discussed– Density: Total number of nodes connected in the
network divided by the total number of possible connections
• An overall measure of connectivity in the network.– Centrality: Measure of the most influential actors
within the network.– Structural Equivalence: A measure of supply chain
similarity among actors in the network.
We can quantify structural equivalence by using a similarity score
Example: Molecular and Cell Biology
Conclusion and Next Steps
• Social network analysis can be used as an effective tool to understand the structure of supply networks at UCB.
• Develop dynamic visualizations and animations using Gephi.• Need to further develop measures.• Expand the analysis to compare departments within a field. • Compare UCB supplier network with other UC campuses.
Conclusions
• Product descriptions can be used to find common “types” of products
• More work to be done interpreting these categories
• Next step is to use this information to predict the type of incoming purchases, and to find clusters of departments with similar purchase categories
Annual purchase behavior and clustering
• Detect common patterns of buying behavior during the year• Use this to predict large purchases, and identify opportunities to improve
efficiency
Conclusions
• There are distinct motifs of purchasing behavior over the year
• These may be used to predict when purchases will be made, and to find opportunities to improve department behavior.
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