AI steers the supply chain: Data becomes the basis for decisions

How many items will be sold next week? Which store will need replenishment, and when? And how can the quality of perishable goods be maintained? Questions like these have a direct impact on product availability, costs, and customer satisfaction in retail – and they are increasingly being answered by AI systems.

The foundation of every AI application in the supply chain is data. Sales figures, inventory levels, delivery times, and quality information are generated throughout the entire value chain. Modern AI systems analyze these streams of data in real time and translate them into operational decisions for planning, replenishment, and logistics.

As the volume of available data grows, so do the precision and controllability of processes. As a result, data quality becomes a critical factor for efficiency and responsiveness – from forecasting to final delivery.

One of the most important levers is demand forecasting. AI models identify patterns in historical data, seasonal effects, and regional differences, enabling more accurate assortment and inventory planning.

• H&M uses AI to forecast demand at both the store and product level, particularly for rapidly changing trends and short product life cycles. The goal is to distribute merchandise more effectively and respond more quickly to shifts in demand.
• The Otto Group uses AI primarily for sales and returns forecasting. In e-commerce, these models help anticipate high return rates early and manage merchandise flows accordingly.
• KiK uses an AI-based planning and analytics platform to support assortment and quantity decisions with data-driven insights while standardizing planning processes.

The result: fewer excess inventories, fewer out-of-stock situations, and more precise merchandise distribution.

Transportation and distribution are also becoming increasingly data-driven. Instead of planning routes statically in advance, AI systems continuously adjust routes, capacity utilization, and delivery windows based on current data.

• ROSSMANN uses AI in route planning to better manage the growing complexity of its store network. The system simultaneously considers factors such as delivery windows, store demand, vehicle utilization, and route design, generating optimized delivery schedules.

The key innovation lies in its dynamic nature: plans are not created once and left unchanged but are continuously updated whenever conditions shift, such as unexpected demand changes or transportation delays.

The result is more stable operations, better utilization of transportation capacity, and greater flexibility in day-to-day business.

In addition to planning and transportation, quality assurance is becoming an increasingly important focus. AI-powered systems detect deviations early and support employees during inspections.

• Albertsons uses a computer vision solution for fresh-produce quality control. The company’s in-house AI tool helps employees in distribution centers maintain high standards of quality and consistency for fresh fruits and vegetables throughout the supply chain. Initially deployed for strawberries and grapes in selected distribution centers, the system is now being expanded nationwide to cover additional fresh-produce categories.

The technology complements existing processes and helps ensure that quality standards are implemented consistently across the entire supply chain.

The supply chain is evolving into a data-driven control system. AI analyzes information from planning, logistics, and quality management in real time and supports decision-making throughout the entire supply chain.

The foundation remains data quality: only when data is consistent and available in near real time across all stages can processes be managed with precision. At the same time, real value emerges from the collaboration between people and AI, where automated analysis meets operational experience.

Especially in an increasingly volatile environment, data-driven systems can help make supply chains more resilient and responsive to change. Looking ahead, this trend is expected to accelerate further – toward supply chains that largely manage themselves and continuously adapt to new conditions.