Skip to content

Summit 26 from June 1-4 in San Francisco

Lead your organization in the era of agents and enterprise intelligence.

Register now
Snowflake for Developers/Guides/Supply chain network optimization using Linear Programming

Supply chain network optimization using Linear Programming

Brett Klein
Fork Repo

Overview

The solution architecture shows how to optimize supply chain networks by solving for production planning, inbound purchasing, outbound selling, and transportation logistics decisions using Linear Programming.

  • Use Faker() library to create datasets for Factory, Distributor, and Customer entities
  • Leverage Snowpark dataframes to create views for shipments, distributor_rates and so on
  • Invoke Snowflake Cortex LLM SQL functions to enrich the dataset with additional fields
  • Using the PuLP package (a Linear Programming modeler) and CBC solver for minimizing distance, minimizing freight costs and minimizing total fulfillment costs
  • Build a Streamlit application to serve as a web interface for supply chain decision makers to use this solution

Solution Architecture: Supply Chain Network Optimization using Snowpark

Architecture Diagram
  • Demonstrates Supply Chain Network Optimization entirely on Snowflake
  • Explains linear programming and how it can benefit customers
  • Demonstrates using Snowflake Cortex LLM functions to enrich supply chain data
  • Includes geospatial analytics and Streamlit UI

Get Started

Updated 2026-04-28

This content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances