SMY Inventory Intelligence

Inspiration

Small businesses and creators often lose time manually tracking ingredients, shipments, and usage in spreadsheets. I wanted to design a system that could intelligently analyze those files, forecast needs, and simplify decision-making; essentially turning messy Excel data into actionable inventory insights.

Building MSY Inventory Intelligence started from that goal: to make data organization, forecasting, and cost management feel effortless.

What it does

MSY Inventory Intelligence is a Streamlit web app that:

• Imports monthly Excel or CSV files for sales, recipes, shipments, and costs
  
• Cleans and merges the data automatically (no manual alignment needed)
  
• Visualizes usage vs. shipments across time periods
  
• Calculates COGS (Cost of Goods Sold) per ingredient or product
  
• Estimates reorder points based on usage and forecasted demand (using EWMA / moving-average modeling)
  
• Generates next-month usage forecasts to guide purchasing and production planning
  

The result: a single dashboard where users can see how inventory is moving, predict shortages, and reduce waste.

How we built it

• Data Handling: Python (Pandas + NumPy) for dataset merging and calculation
  
• File Support: OpenPyXL / xlrd for Excel import, RapidFuzz for fuzzy-column matching
  
• Hosting: Streamlit Community Cloud connected to a GitHub repository
  

Challenges we ran into

Aligning columns from different spreadsheet formats required custom fuzzy matching logic

• Building a reorder-point model that worked for both high- and low-volume items
  
• Managing slow Excel imports and optimizing Pandas operations for larger files
  
• Learning the Streamlit → GitHub → Deployment workflow for the first time
  

Accomplishments that we're proud of

-Built a fully functional, data-driven inventory dashboard from scratch

• Automated multi-file ingestion and forecasting with no manual data entry
  
• Designed a clean, professional interface for non-technical users
  
• Successfully hosted the project online with live interactivity through Streamlit Cloud
  

What we learned

-How to connect data-science logic (forecasting + inventory math) with real-time web apps

• How to structure and deploy Streamlit projects through GitHub
  
• The value of designing tools that are both analytical and user-friendly
  
• Fundamentals of inventory control usage rate, reorder point, and lead-time variability
  

What's next for SMY Inventory Intelligence

Add multi-user authentication and saved dashboards

• Integrate APIs for live supplier data and order tracking
  
• Expand forecasting models (ARIMA / Prophet) for better accuracy
  
• Include downloadable reports for finance or purchasing teams
  
• Eventually turn MSY into a full small-business inventory analytics platform

Built With

• chatgbt
• excel
• github
• panda
• python
• rapidfuzz
• streamlit