Predicting Safety Stock: A Time Series Forecasting Project

Balancing inventory levels is one of the most critical challenges in supply chain management. Keeping too much stock ties up capital, while keeping too little leads to missed sales and unhappy customers. This text-based course guides you through a practical, step-by-step project to forecast product demand and calculate optimal safety stock. You will transition from understanding foundational inventory concepts to performing exploratory data analysis and implementing seasonal forecasting models using modern Python libraries. What you'll learn: - Understand foundational inventory terminology, safety stock concepts, and the math behind demand uncertainty. - Clean and group multi-region historical sales data using modern dataframe operations. - Identify seasonal trends and patterns in product sales through systematic exploratory analysis. - Build and configure a Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict future demand. - Calculate optimal safety stock levels using forecasted demand and lead-time variability. - Evaluate model performance using key forecasting metrics to ensure reliable replenishment decisions. The course starts with essential definitions of inventory management and time-series data before moving into hands-on data manipulation. You will then progress through model building, parameter tuning, and final safety stock calculations. This course is designed for beginner data analysts, supply chain professionals, and aspiring data scientists. No prior forecasting experience is required, though a basic familiarity with Python is helpful. Start reading today to master the fundamentals of predictive inventory management.