Retail — Advanced Sales Forecasting Pipeline for E-Commerce

Overview

The primary purpose of the retail_ecommerce_kmds_sales_forecasting DAG is to accurately predict future sales using advanced machine learning models. The architecture integrates various data sources, including historical sales data, seasonal trends, and promotional calendars, to create a robust sales forecasting model. The data ingestion pipeline begins with the collection of ERP transaction logs, promotional schedules, and historical sales records. These inputs are processed through several tran

The primary purpose of the retail_ecommerce_kmds_sales_forecasting DAG is to accurately predict future sales using advanced machine learning models. The architecture integrates various data sources, including historical sales data, seasonal trends, and promotional calendars, to create a robust sales forecasting model. The data ingestion pipeline begins with the collection of ERP transaction logs, promotional schedules, and historical sales records. These inputs are processed through several transformation steps, where data cleaning, normalization, and feature extraction occur to ensure high-quality input for the machine learning algorithms. The processing logic involves training predictive models on historical data, validating their accuracy, and generating sales forecasts that are updated regularly. The outputs of this DAG include a dashboard displaying forecasted sales figures, accuracy metrics, and alerts for any anomalies detected during processing. Key performance indicators (KPIs) include forecast accuracy, processing time, and system uptime, which are monitored continuously to ensure optimal performance. In the event of a failure, the DAG is designed to automatically restart, ensuring minimal disruption to the forecasting process. The business value of this DAG lies in its ability to provide timely and accurate sales forecasts, enabling retailers to optimize inventory levels, improve customer satisfaction, and enhance overall operational efficiency.

Part of the Predictive Maintenance solution for the Retail industry.

Use cases

• Improved inventory management through accurate sales predictions
• Enhanced customer satisfaction with better product availability
• Informed decision-making based on data-driven insights
• Reduced operational costs by optimizing stock levels
• Increased revenue through timely promotional strategies