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Case Study

Transforming Demand Forecasting for Leading Retail Chain with AI

Europe's largest supermarket chain, enhanced its demand forecasting capabilities with advanced AI solutions, resulting in significant improvements in inventory management, operational efficiency, and sustainability.
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Overview

The client, with over 1,100 stores in Europe, faced challenges in accurately forecasting demand for its 27,000-item product line. Recognizing the need to improve, the retailer sought to ensure product availability and minimize overstock through enhanced, data-driven forecasting and inventory management.

Driven by a commitment to excellence and sustainability, the client aimed to enhance its processes and reduce waste through innovative technologies. This effort was to refine inventory strategies, minimize manual efforts, and maintain leadership in retail efficiency and environmental stewardship. Focused on the future, the European retailer intended to use data insights for better decision-making, thereby improving customer satisfaction and inventory precision. The ultimate goal was to develop a nimble, adaptive supply chain ready for market changes.

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Innovative AI-Driven Approach

Xebia crafted a tailored AI strategy to boost the retail chain's demand forecasting and inventory management, incorporating advanced technologies to meet the retail sector's specific needs and align with the client's strategic goals. 

Integrated Data Platform

Xebia employed DBT on a Spark engine within Databricks to merge historical and real-time data, creating a solid foundation for precise demand forecasting. This data integration enabled the retailer to leverage both past patterns and current market conditions, greatly improving predictive accuracy.

Custom AI & Machine Learning Models

Xebia developed tailored machine learning models specifically designed for retail forecasting, including techniques such as time series forecasting and regression analysis. These models were refined to analyze data across 27,000 products, significantly enhancing the accuracy of predictions.

Scalable Cloud Architecture

The solution was deployed on the client’s Azure cloud infrastructure, with Apache Airflow managing nightly forecasts up to 50 days ahead. This architecture was designed to scale with the business and adapt to a rapidly evolving market environment.

 
 
"The team at Xebia has shown a ‘hands-on, can-do’ mentality from the very beginning. They brought their experience in machine learning, data engineering, and best practices with them, and transferred their knowledge to the rest of the team from the get-go."
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Data Science Team Lead

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