Python for Supply Chain Analytics and Inventory Optimization

Supply chain professionals are constantly pressured to reduce costs, minimize stockouts, and predict future demand with high accuracy. Traditional spreadsheet tools often struggle to scale with today's massive datasets and complex logistical networks. This course bridges the gap between logistics operations and modern data science. You will learn how to leverage Python and its powerful data libraries to analyze supply chain data, build predictive forecasting models, and optimize inventory levels. Starting from fundamental concepts, you will progress to writing clear, modern Python code to solve real-world logistical challenges. What you'll learn: - Understand foundational supply chain metrics and basic Python programming concepts. - Forecast customer demand using statistical models and modern predictive analysis libraries. - Optimize inventory levels and safety stock using mathematical models in Python. - Apply linear programming techniques to solve complex distribution and routing challenges. - Analyze pricing strategies and revenue management scenarios through data-driven simulations. - Utilize modern Python data libraries like Pandas and Polars to clean and manipulate large logistics datasets. The course begins with core supply chain definitions and Python setup basics before moving into hands-on data manipulation. You will then explore practical scenarios, writing code to automate forecasting, simulate inventory policies, and solve optimization problems. This course is designed for supply chain professionals, logistics analysts, and beginners looking to transition from spreadsheet-based analysis to programming-based data science. No prior programming experience is required. Start transforming your logistical workflows with the power of data science today.