ML
Also: Machine Learning, Maschinelles Lernen, ML-Algorithmen, ML-Modell, maschinelles Lernen
AI-generated
Machine Learning (ML) is a subfield of Artificial Intelligence in which algorithms learn from data without being explicitly programmed. In intralogistics, ML is used primarily for demand forecasting, anomaly detection, and the optimization of warehousing and transport processes.
Key facts
- Machine Learning learns autonomously from historical data and improves predictions without needing to be manually reprogrammed.
- In intralogistics, demand forecasting, anomaly detection, and route optimization are the most common ML application areas.
- The quality of an ML model depends critically on the volume, consistency, and currency of the training data.
- ML models must be regularly monitored after deployment and retrained in the event of concept drift.
- The use of ML requires clear data ownership and defined processes for model maintenance.
What is Machine Learning?
Machine Learning refers to a class of methods in which a computer model independently derives patterns and relationships from historical data. Unlike rule-based software, ML requires no fully pre-programmed logic: the model iteratively improves its predictions as it is exposed to new data. ML is a subfield of Artificial Intelligence (AI) and forms the technical foundation for many applications that fall under the term "AI in logistics"
Related terms
- AI
Artificial Intelligence (AI) refers to systems that learn from data, recognize patterns, and on this basis support or automate decisions.
- 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.
- Automatisierung
Automation refers to the use of technologies and systems to execute processes fully or partially without human intervention. In intralogistics, the spectrum ranges from simple conveyor systems to fully automated high-bay warehouses with autonomous vehicles.
- BI
Business Intelligence (BI) refers to the systematic collection, preparation, and analysis of company data to support informed decision-making in logistics and supply chain management.
- Bedarfsplanung
Demand planning refers to the systematic process by which a company determines what quantities of materials, goods, or resources are needed and when. It forms the foundation for inventory management, procurement, and warehouse planning.
- Datenanalyse
Data analysis refers to the systematic evaluation of raw data from logistics and IT systems in order to measure performance indicators, identify the causes of deviations, and make well-informed decisions.
- Disposition
Disposition refers to the operational planning and control of resources, quantities, and orders in logistics – for example in transport or materials disposition. It encompasses capacity assignment, scheduling, prioritization, and response to disruptions.