Supply Chain Analysis Resources | Call to action: data contributions

Do you work in supply chain? Do you teach supply chain analysis?

RStudio and the supply chain analysis community are creating resources to help apply open source software to solve problems common in the field. We'd love for you to help!

What's the call to action?
As a starting point, we are asking the community to help contribute data that can be freely used by this project. Data would be used in tutorials and example scripts, to help this community share their approach to this work, without having to refer to proprietary datasets.

Do you have supply chain data (even simulated data) that you would be willing to share with the community?

If you are interested in helping you can comment here or reach out to

You can also join the conversation through:

What could this data set look like?

The ideal data set (the dream, but not necessarily needed) would include daily, weekly (both regular and calendar weeks) and monthly time bucket datasets, with the following combinations' attributes:

  • Lowest item level (e.g., SKU).
  • Aggregated item level #1 (e.g., Product Class).
  • Aggregated item level #2 (e.g., Product Family).
  • Lowest location level (e.g., store).
  • Aggregated location level #1 (e.g., DC).
  • Aggregated location level #2 (e.g., Country).
  • Demand (Bookings, Orders or Shipments).
  • Cancellations, etc.

Optional time series data:

  1. On Hand.
  2. Store Count.
  3. Open Orders / Customer Forecast.
  4. Well known causal factors for the specific industry (holidays, trends, seasonality, etc.).
  5. Promotional data.

Optional external data sources:

  1. Sentiment data.
  2. Industry trends.
  3. Competitors data.


  1. Data context should be well explained using a dictionary (industry, lead times, etc.).
  2. Dataset size should be at least with 1000 items at the lowest level.
  3. Time range should be at least for 2 years (preferably 3) historical data and 1 year of future attributes.

Supply Chain Meetup Recordings:


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