AI
Also: Artificial Intelligence, Künstliche Intelligenz, KI, Machine Learning, ML, AI/ML
AI-generated
Artificial Intelligence (AI) refers to systems that learn from data, recognize patterns, and on this basis support or automate decisions.
Key facts
- AI in logistics refers to systems that learn from data and support or automate decisions in forecasting, optimization, and process control.
- The quality of an AI model depends directly on the completeness and consistency of the underlying data.
- Explainability of model outputs is a key criterion, because operational decisions must be transparent and comprehensible for planners and managers.
- AI is not a single solution but a class of methods applied across a wide range of logistics applications.
- Vendor lock-in through proprietary models and cloud dependencies should be actively assessed when selecting a vendor.
What is Artificial Intelligence in logistics?
Artificial Intelligence (AI) encompasses methods that enable software systems to learn from historical and current data, identify regularities, and derive predictions or recommendations from them. The foundation consists of mathematical models — drawn from areas such as machine learning, neural networks, or statistical optimization. Crucially, AI systems typically improve their results as the volume and quality of data grow, without rules needing to be manually programmed.
In logistics, AI is not a single product but a class of methods applied across a wide variety of use cases — from demand forecasting and dynamic route planning to anomaly detection in goods receipt processes.
How does AI work in practice?
A typical use case is demand forecasting in the warehouse: the model processes sales data, seasonal patterns, and external signals such as weather data or promotional plans, and produces a demand estimate on which procurement and inventory management can act. Another example is the control of automated guided vehicles (AGVs), where AI calculates routes in real time, detects obstacles, and coordinates vehicle flows.
In automated high-bay warehouses, AI can dynamically adapt put-away and retrieval strategies — for example, based on picking patterns or projected demand. This distinguishes AI-based control from conventional rule-based warehouse management systems, which operate with fixed priority rules.
- Forecasting and planning: demand forecasting, replenishment planning, capacity prediction
- Optimization: route planning, slotting, resource allocation
- Detection: supply chain anomalies, quality deviations, departures from process standards
- Automation: document processing, document classification, chatbots for planners
What matters when making a selection?
When introducing AI solutions in logistics, several aspects are critical and apply across vendors. First, the data foundation: AI models are only as good as the data on which they were trained. Missing, inconsistent, or outdated master data is a common reason for disappointing results. Second, explainability: for operational decisions — such as whether to increase a safety stock level — planners and logistics managers expect comprehensible justifications. Whether a model delivers these varies considerably depending on the method and vendor. Third, integration: AI components must be connected to existing ERP, WMS, and TMS systems; interfaces and data pipelines are often more complex than the model itself. And finally, dependency: proprietary models and cloud services can lead to vendor lock-in — this should be taken into account when structuring contracts and making architecture decisions.
Related terms
- AGV
AGV (Automated Guided Vehicle) refers to driverless transport vehicles that automatically move goods in warehouses and production environments. They follow predefined or sensor-controlled routes without a human driver.
- Absatzplanung
Sales planning refers to the systematic forecasting of future sales volumes for defined products, regions, or channels. It forms the foundation for all downstream planning processes in production, procurement, and logistics.
- Absatzprognose
The sales forecast estimates future demand for products or items over a defined period. It forms the basis for inventory, capacity, and procurement planning in logistics and purchasing.
- Hochregallager
A high-bay warehouse is an automated storage facility with racking from approximately twelve meters in height, where stacker cranes automatically store and retrieve pallets.
- Kommissionierung
Order picking is the process of assembling individual items from warehouse stock to fulfill a specific customer order — the most labor-intensive step in the warehouse.