Retail — E-commerce Demand Forecast Feature Engineering Pipeline

Overview

The primary purpose of this DAG is to extract and transform historical sales and inventory data to create relevant features for demand forecasting models in the retail sector. The data sources include ERP transaction logs, inventory records, and external data feeds for promotions and weather. The ingestion pipeline begins with data extraction from these sources, followed by normalization to ensure consistency across datasets. The processing steps include adding exogenous variables such as promot

The primary purpose of this DAG is to extract and transform historical sales and inventory data to create relevant features for demand forecasting models in the retail sector. The data sources include ERP transaction logs, inventory records, and external data feeds for promotions and weather. The ingestion pipeline begins with data extraction from these sources, followed by normalization to ensure consistency across datasets. The processing steps include adding exogenous variables such as promotional events and weather forecasts, which are crucial for improving model predictions. After feature generation, the DAG implements validation checks to ensure the quality and relevance of the created features. The final outputs are stored in a centralized data warehouse, making them readily available for model training. Monitoring key performance indicators (KPIs) such as feature importance and model accuracy is essential to assess the effectiveness of the generated features. By leveraging this DAG, retail businesses can significantly enhance their demand forecasting capabilities, leading to improved inventory management and optimized sales strategies.

Part of the Market & Trading Intelligence solution for the Retail industry.

Use cases

• Improves demand forecasting accuracy for better inventory management
• Reduces stockouts and overstock situations through precise predictions
• Enhances promotional planning with data-driven insights
• Optimizes supply chain operations by anticipating demand fluctuations
• Increases sales through targeted marketing strategies based on forecasts