Demand Forecasting in Retail

Demand Forecasting in Retail equips participants with essential methodologies and quantitative techniques for predicting consumer behavior and inventory needs. The course emphasizes hands-on, project-based learning, allowing participants to apply theoretical concepts to real-world scenarios. By engaging in interactive projects, learners will develop the skills to analyze historical sales data, identify trends, and create accurate forecasts that drive strategic decision-making in retail environments.
Participants will explore various forecasting models and tools, focusing on practical applications that enhance operational efficiency and profitability. The course culminates in a final project where learners will present their findings and forecasts, potentially contributing to Cademix Magazine. This collaborative approach not only solidifies understanding but also fosters a professional network among peers and industry experts.
Introduction to Demand Forecasting: Concepts and Importance
Overview of Retail Analytics and Data Sources
Time Series Analysis Techniques
Regression Analysis for Demand Forecasting
Seasonal Decomposition of Time Series
Machine Learning Approaches in Forecasting
Inventory Management and Demand Planning
Evaluating Forecast Accuracy: Metrics and Techniques
Case Studies: Successful Demand Forecasting in Retail
Final Project: Developing a Comprehensive Demand Forecast for a Retail Scenario

Equip participants with the ability to apply advanced forecasting techniques to real-world retail scenarios, enhancing their analytical and decision-making skills.

Graduates, job seekers, business professionals, and optionally researchers or consultants.

Basic understanding of statistics and data analysis; familiarity with Excel or similar data analysis tools.