Supply Chain: the Best Domain for Data Scientists in 2026

🚚 Why Supply Chain Is the Best Domain for Data Scientists?
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After 10 years in supply chain analytics, Samir Saci has a clear answer: rich problems, beautiful mathematics, and tangible impact.

📊 The 4 Analytics Levels in Supply Chain
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📍 Descriptive – Operational visibility (how many pallets do we have in the warehouse?) 🔎 Diagnostic – Root cause analysis with Lean Six Sigma and statistics ⚙️ Prescriptive – Decision optimization (linear programming with PuLP) 🤖 Predictive – Forecasting and demand models

💡 Real Impact Cases
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🏭 Warehouse heatmap: A simple visualization identified congestion in high-rotation aisles → contract renewed for several million euros.

🚛 Chi-Squared Test: Before blaming drivers for avoiding difficult routes, data proved allocation was random. No conflict, just evidence.

🌐 Network Design: Global factory network optimization accounting for production costs per country and COGS fairness.

🛠️ Tech Stack
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Python + PuLP for optimization
Pandas + Seaborn for analysis and visualization
Streamlit for productizing solutions

💡 In Simple Terms
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The supply chain is the nervous system of any company: it connects factories, warehouses, transportation, and customers. Data flows at every point. For a data scientist, every inefficiency is an optimization problem waiting to be solved.