Mathnal

Are you making supply chain decisions based on incomplete data? 📦📊

In a recent case study, we analyzed a 72% unconditional stockout probability. On the surface, that number is terrifying. But the "unconditional" view rarely tells the whole story.

By applying Conditional Probability, we discovered that the risk wasn't uniform. By shifting orders to Supplier S3, we could immediately optimize that probability down to 50%.

Key Takeaways from the Optimization Session:

The "Reverse" Forecasting Shift: Stop splitting monthly forecasts into equal weekly buckets. The most accurate approach is "Bottom-Up": Start with daily demand $\rightarrow$ Aggregate to weekly $\rightarrow$ Finalize monthly.

Identifying High-Risk Zones: Using SUMPRODUCT and COUNTIFS, we isolated SKUs where Lead Time (> 15days) and Forecast Error (> 30%) overlapped. This revealed a 75% stockout risk for specific items (SKUs 2, 3, and 5) that required immediate intervention.

Data-Driven Prescription: Don't just identify the problem—solve it. For high-risk SKUs, the mandate is clear: tighten lead-time controls or diversify to more reliable suppliers.

Technical Tip: If you're still using nested IF statements for complex conditions, try the SUMPRODUCT method with double-unary (--) conversion. It’s faster, cleaner, and much easier to audit.

How are you using probability to guide your procurement strategy this year? Let's discuss in the comments. 👇

🚨 Most supply chain teams are still forecasting in Excel.

Meanwhile, Python can do in seconds what takes them days.

I wrote two books to change that.

📦 Supply Chain Optimization with Python
🔮 Forecasting & ML-Driven Supply Chain Planning

Here's what's inside 👇

✅ Demand forecasting using Prophet, ARIMA & XGBoost
✅ Inventory optimization with linear programming (PuLP)
✅ Transportation & network routing models
✅ End-to-end ML pipelines for procurement & planning
✅ 30+ ready-to-use Python notebooks

Whether you're a supply chain analyst, ops manager, or data scientist — these books will level up how you think and work.

🎯 Who is this for?
→ Supply chain & ops professionals tired of manual tools
→ Data scientists moving into supply chain roles
→ Students & researchers in logistics & SCM
→ Consultants building analytical capabilities

💡 Real datasets. Real case studies. Real results.

🔗 Link in comments to grab your copy.