KI

What is artificial intelligence in intralogistics?

Artificial intelligence (AI) refers to a class of algorithms and software systems that learn from historical and current data, recognize patterns, and independently draw conclusions from them. Unlike classical rule-based logic, where a person explicitly defines if-then conditions, AI models develop their decision-making foundation directly from data. Common sub-disciplines include machine learning (ML), deep learning, optimization methods, and natural language processing (NLP). In intralogistics, these techniques are increasingly being integrated into warehouse management systems, control software, and planning tools.

How is AI used in practice?

The application areas can be broadly divided into three categories:

• Forecasting and demand planning: AI models analyze order and movement data, identify seasonal patterns, and deliver sales or inventory forecasts that are more precise than simple averaging methods.
• Process control and optimization: Warehouse systems use AI to dynamically assign storage locations, optimize picking routes, or control resources such as conveyor technology and personnel according to demand.
• Anomaly and error detection: Algorithms detect deviations in real-time data — such as unusual throughput times, impending bottlenecks, or erroneous shipments — and trigger alerts or countermeasures.

In many projects, AI initially operates in recommendation mode: the system makes a suggestion and a person decides. Only as data maturity increases and trust in the models grows are decisions fully automated. How quickly this transition succeeds depends heavily on data quality and team acceptance.

What matters when making a selection?

Before deploying AI solutions, you should clarify several questions. First: Is the data foundation sufficient? AI models require clean, complete, and historically deep datasets — without these, even good algorithms produce poor results. Second: How transparent are the outputs? Many ML models are so-called black boxes whose decision logic is difficult to understand. For regulated areas or safety-critical processes, explainable models (Explainable AI) are preferable. Third: How is value measured? Without clear KPIs and baselines, it is impossible to determine whether an AI system actually performs better than the previous approach. Fourth: What level of integration is possible? AI only realizes its value when it is closely connected to WMS, ERP, or control systems and can process data in real time. The choice between a best-of-breed approach and an integrated suite influences how complex this integration will be.