Module - 1. Introduction to Retail Data (1 hour)

1.1 Overview of Retail Data

• • • • • Definition and importance of retail data
        • Common sources of retail data

1.2 Key Terms and Concepts

• • • • • Entities (products, stores, sales, customers)
        • Data columns and their significance

1.3 Real-World Examples

• • • • • Case studies or examples from various retail contexts

Module - 2. Datasets Used in Retail Applications (2 hours)

2.1 Forecasting Datasets

• • • • • What is forecasting?
        • Typical datasets (historical sales, promotions, seasonal trends)
        • Key columns (date, sales volume, promotional activity)

2.2 Allocation Datasets

• • • • • Purpose of allocation in retail
        • Typical datasets (inventory levels, store capacities, sales forecasts)
        • Key columns (store ID, item ID, current stock, expected demand)

2.3 Ordering Datasets

• • • • • Role of ordering datasets
        • Typical datasets (order history, supplier data, lead times)
        • Key columns (order ID, item ID, quantity, supplier)

2.4 Item Planning Datasets

• • • • • Importance of item planning
        • Typical datasets (item attributes, sales patterns, stock levels)
        • Key columns (item ID, sales trend, reorder point, safety stock)

Module - 3. In-Depth Analysis of Dataset Columns (2 hours)

3.1 Columns in Forecasting Datasets

• • • • • Detailed look at date, sales volume, promotional data
        • How these columns impact forecasting accuracy

3.2 Columns in Allocation Datasets

• • • • • Analysis of store ID, item ID, stock levels
        • Understanding how allocation decisions are influenced

3.3 Columns in Ordering Datasets

• • • • • Breakdown of order ID, item ID, quantities, suppliers
        • How ordering decisions are made based on these columns

3.4 Columns in Item Planning Datasets

• • • • • Examination of item attributes, sales trends, reorder points
        • How item planning utilizes these columns for effective inventory management

Module - 4. Practical Applications and Case Studies (2 hours)

4.1 Hands-On Exercise: Forecasting

• • • • • Analyzing a sample forecasting dataset
        • Identifying key columns and their impact

4.2 Hands-On Exercise: Allocation

• • • • • Working with a sample allocation dataset
        • Making allocation decisions based on dataset columns

4.3 Hands-On Exercise: Ordering

• • • • • Examining a sample ordering dataset
        • Understanding ordering decisions from dataset columns

4.4 Hands-On Exercise: Item Planning

• • • • • Using a sample item planning dataset
        • Making planning decisions based on dataset columns