Retail — Demand Forecast Model Training for E-Commerce

Overview

The purpose of this DAG is to train demand forecasting models specifically tailored for the retail e-commerce sector. The workflow ingests various data sources, including historical sales data, promotional activity logs, and customer behavior analytics. The data pipeline begins with the extraction of these inputs, followed by data cleaning and transformation to ensure quality and consistency. In the processing phase, machine learning algorithms are applied to train the models, leveraging techniq

The purpose of this DAG is to train demand forecasting models specifically tailored for the retail e-commerce sector. The workflow ingests various data sources, including historical sales data, promotional activity logs, and customer behavior analytics. The data pipeline begins with the extraction of these inputs, followed by data cleaning and transformation to ensure quality and consistency. In the processing phase, machine learning algorithms are applied to train the models, leveraging techniques such as regression analysis and time series forecasting. The models are evaluated using key performance indicators (KPIs) such as Mean Absolute Percentage Error (MAPE) and Weighted Sum of Prediction Loss (WSPL) to assess their accuracy and reliability. Upon successful training, the results are stored in a centralized repository, and the models are versioned for effective tracking and management. In case of any failures during the training process, a robust recovery mechanism is in place to automatically restart the training, minimizing downtime and ensuring continuous improvement. This comprehensive approach not only enhances forecasting accuracy but also provides valuable insights into demand trends, ultimately driving better inventory management and customer satisfaction in the retail space.

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

Use cases

• Improved demand forecasting accuracy reduces stockouts.
• Enhanced inventory management minimizes holding costs.
• Informed decision-making boosts promotional effectiveness.
• Increased customer satisfaction through better product availability.
• Streamlined operations lead to higher profitability.