TutorialIntermediate2026-02-09

22 practical Data Science projects for Supply Chain teams

Python Machine Learning Forecasting Optimization Data Analytics

Supply Chain teams often struggle to move from general AI education to projects that improve actual planning and operational decisions. In this tutorial, Samir Saci proposes 22 practical Data Science projects that professionals can use to develop applied skills while addressing recognizable Supply Chain problems.

What the tutorial covers

The projects span demand forecasting, inventory management, logistics, transportation, warehouse operations, sustainability and automation.

How Supply Chain teams can use it

The tutorial can serve as a capability-building roadmap. Each project should be tied to a defined decision, operational baseline, performance metric and accountable owner.

Recommended validation approach

Before production use, teams should validate the data source, refresh frequency, decision boundaries, expected performance and escalation process.

#Samir Saci #Data Science for Supply Chain #Demand Forecasting #Inventory Optimization #Logistics Analytics #Python #Supply Chain Education

Keywords

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Read the source— Samir Saci