Build a single-page Streamlit app from an inventory simulation model.
We’ll transition from a Supply Chain Analytics model in a Jupyter Notebook to a Web App, add clean visuals (Demand / Orders / IOH), and deploy it so your team can click and learn.
No prior web dev required, only Python and your Supply Chain knowledge!
🔗 Important Links:
Learn the basics of supply chain processes in 5 minutes: https://www.youtube.com/playlist?list=PLvINVddGUMQWRel1u0RIBbKIYRUQBdrt9
Live app: https://supplyscience-inventory.streamlit.app/
Tutorial - Part 1 Repository: https://github.com/samirsaci/tuto_inventory
Starter repo for this video: https://github.com/samirsaci/inventory-streamlit-app-starter
Final code: https://github.com/samirsaci/inventory-streamlit-app
Previous Tutorial: https://youtu.be/1oRebt_Q0dY
⏱️ Timestamps
00:00 : Intro of the tutorial
02:02 : Demo of the Supply Chain Web Application
03:57 : Recap of the previous tutorial - Inventory Analysis
11:23 : Setup of the project (local environment, libraries)
16:25 : Import libraries and create a streamlit app
21:26 : Create a side panel for the input parameters
39:39 : Calculations and visuals
51:47 : Generate the visualisation
53:52 : How to deploy this app?
56:23 : Conclusion
🧠 What you’ll build
- A Streamlit web app wrapping your inventory engine
- Inline KPI cards (Quick Context)
- A 3-panel chart: Demand / Orders / IOH
- "Lead-time aware” ordering vs. simple fixed-cycle
- Auto re-run on parameter changes after first click
#Streamlit #Python #SupplyChain #InventoryManagement #Logistics #Operations #DataAnalytics #WebApp #EOQ #Tutoria
