UC SOURCING

Purchases in UC systemAlmost all UC campuses over the last 4-7 years

THE TEAM

ACTIVE PROJECTS

• Network / graph analysis• Text classification / clustering• Behavioral clustering and efficiency

metrics

Social networks ofdepartments/manufacturers

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.

March 2013 PurchasesDepartments: Red | Suppliers: white

Centrality:Measure of power within a network

The formation of the non-catalog network, first 21 days, March 2013

Structural EquivalenceHow similar are the department supply

chain networks?

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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.

PO Text Classification•

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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

Clustering departments using purchasing behavior

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.

Thanks