Retail — Forecast Performance Monitoring for E-commerce Models

Overview

The purpose of this DAG is to continuously monitor the performance of forecasting models used in retail e-commerce, ensuring they deliver accurate predictions and operate within acceptable latency thresholds. The architecture consists of several key components, starting with the ingestion of data from various sources, including historical sales data, real-time transaction logs, and model performance metrics. The data pipeline begins with the collection of these inputs, followed by processing ste

The purpose of this DAG is to continuously monitor the performance of forecasting models used in retail e-commerce, ensuring they deliver accurate predictions and operate within acceptable latency thresholds. The architecture consists of several key components, starting with the ingestion of data from various sources, including historical sales data, real-time transaction logs, and model performance metrics. The data pipeline begins with the collection of these inputs, followed by processing steps that analyze the accuracy and latency of the forecasting models. Quality controls are implemented to detect any performance drifts or prediction failures, triggering alerts to notify stakeholders of potential issues. The outputs of this DAG include detailed performance reports and alerts, which are stored for further analysis and reporting. Key performance indicators (KPIs) monitored include prediction accuracy, latency, and the frequency of alerts triggered. This systematic approach not only enhances the reliability of forecasting models but also adds significant business value by enabling timely interventions and informed decision-making. By ensuring that forecasting models remain effective, retailers can optimize inventory management, improve customer satisfaction, and ultimately drive sales growth.

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

Use cases

• Improved accuracy of sales forecasts enhances inventory management
• Timely alerts reduce the risk of operational disruptions
• Data-driven insights support strategic decision-making
• Increased customer satisfaction through reliable stock availability
• Enhanced ability to respond to market changes swiftly