Carrefour: Scaling Data & AI Products Across Retail Supply Chains

01 / 05

The challenge

At the scale of a major retail group, even small inefficiencies in supply-chain operations can generate significant operational costs. Demand forecasting, product availability, pallet optimization and inventory management require coordination between large datasets, complex business rules and operational systems. The challenge was to turn analytical capabilities into reliable, reusable products capable of delivering measurable improvements.

02 / 05

My contribution

I drove AI and analytics initiatives within the Analytics Factory, translating supply-chain requirements into product roadmaps and coordinating delivery across business and technical teams, as lead of a 10-person ML/MLOps engagement.

03 / 05

The approach

• One DemandDevelop demand-related analytics capabilities to support forecasting and supply-chain decision-making.
• Max Hauteur PaletteWork on pallet-height optimization to improve logistics operations and the use of transport and storage capacity.
• Analytics FactoryStructure the delivery of reusable data products and accelerate collaboration across business and engineering teams.
• Product leadershipPrioritize use cases through their business value, operational feasibility and expected outcomes.
• Continuous improvementUse operational indicators to guide product iterations and measure value creation.

04 / 05

Business impact

Improvement in the reported operational fill-rate indicator, associated with the supply-chain initiatives. This work demonstrated how AI product management can connect analytical systems to measurable operational outcomes at enterprise scale.

05 / 05

What I learned

Successful AI products are measured by the business indicators they improve, not by the complexity of their algorithms.