The data provided for this project included a significant volume of 10 million lines, updated daily. The information was accessible via an API REST and a SQL database, offering comprehensive details on sales, stock levels, and demand forecasts.

The high frequency of updates and the diversity of data sources allowed for in-depth and real-time analysis, essential for adjusting inventory management strategies.

This project engaged a team of DMSLOG experts, including a Data Scientist and an AI Solutions Architect. The Data Scientist brought expertise in data modeling and predictive analysis, while the AI Solutions Architect designed and implemented the technological solutions needed to integrate AI models into Walmart’s existing systems.

Together, they ensured an effective and seamless implementation, delivering tangible and measurable results.

Thanks to our intervention, Walmart was able to significantly reduce its storage costs while improving product availability in its stores. The demand forecasting models developed allowed for the optimization of stock levels, avoiding shortages and surpluses.

Additionally, the interactive dashboards and detailed reports provided clear visibility into trends and performance, enabling the management team to make informed decisions quickly. This optimization not only improved operational efficiency but also contributed to better customer satisfaction.