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BIDS-collaborative / purchasing

Working with Andrew Clark on optimizing purchasing with data
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Brainstorming ideas #6

Closed choldgraf closed 9 years ago

choldgraf commented 9 years ago

Right now, we're focusing on the network analysis component to this project. There are many other ideas worth pursuing, and we should hash out some thoughts here. Here is the basic set of questions that Andrew asks when doing analyses:

  1. What are we buying
  2. Who are we buying it from
  3. How much are we paying
  4. Who on campus is buying it
  5. How are we buying it

Additionally from Andrew, regarding what's useful regarding distributors vs. suppliers:

We go wherever there is most value. Anecdotally, our spend tends to be pretty fragmented at the manufacturer level. As a result, we focus on the distributors who are less fragmented. Ultimately, we can negotiate with either manufacturers or distributors and routinely ink contracts with both sets.

If you force me to pick… We typically have more leverage with distributors, simply because we can swap them out without impacting product choice on campus. When I tell someone they can’t have a specific product, they tend to get angry at me, especially as the complexity of the product increases.

That said, here is a preliminary list of ideas (italics means in progress):

  1. Classification of the spend using a heuristic or machine learning. Maybe a logistic regression to predict the likelihood of being COTS Software? We have examples of COTS software and the ongoing maintenance transactions for them. Not a lot of examples, but probably enough.
  2. Visualization of the processes used to buy. I suspect spend is traversing the procurement network in 10 or 20 different ways. Seeing those ways would show the scale of the problem and value of the opportunity.
  3. Identifying the seasonality in the spend (if any). That would help identify when the need arises (generally) for software. I would guess there is a pattern to research buys that corresponds to the academic calendar, but have no idea. Or a forecasting in general would be cool.
  4. Identifying (or predicting) the major places on campus (physical or departmental) where software buying takes place to identify the key markets. This would be cool as a bipartite graph showing relationships between departments on campus and software they buy. I.e. Matlab is used in Chemistry. Adobe Professional is used in Art History. Etc. Maybe using Gephi for some graph analysis?
  5. Using text analysis and clustering/classification to assign "product types" to POs by looking through their description, supplier, etc.
choldgraf commented 9 years ago

Hey all - I've put all active projects on our github wiki, which you can find here. We can use this space for brainstorming new projects.