I recently received an encouraging message from Ryan, a data scientist and supply chain modeler at a $5B+ logistics company, that I’d like to share with his permission.
Ryan reached out to the R for Data Science (R4DS) Supply Chain Slack channel, asking for recommendations on how to use R for recommending DC locations. After some problem scoping, I recommended that Ryan use the ompr package for recommending warehouse locations under constraints (for example, that customers be within 500 miles of their DCs).
Inspired by our conversation, I adapted my advice to Ryan into a Towards Data Science blog post, on how to start with supply chain design using R .
Ryan reached out to me later “to send a thank you for your help on the distance optimization question. My previous approach took 25 minutes to run for each potential DC. After I implemented your approach, it took 5 minutes to run ALL potential DCs. Thanks again for the boost.” No, thank you, Ryan! Happy to help!
If your team is looking for a boost in your supply chain modeling efforts, particularly utilizing open-source techniques, please contact me: ralph@datadrivensupplychain.com and follow Data Driven Supply Chain LLC on LinkedIn.
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