Datenanalyse
Also: Datenauswertung, Data Analysis, Logistik-Datenanalyse, Analytik, Datenauswertung Logistik
AI-generated
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.
Key facts
- Data analysis in logistics encompasses descriptive, diagnostic, and predictive methods, and evaluates data from WMS, ERP, TMS, and IoT systems.
- Data quality is the critical prerequisite – faulty or inconsistent input data renders even sophisticated analyses worthless.
- Value is only created when insights are translated into concrete operational measures and KPI management.
- Business Intelligence provides the technical platform; data analysis is the methodological work built upon it.
- Clearly defined, decision-relevant KPIs are a prerequisite for a targeted analytics strategy.
What is data analysis in logistics?
Data analysis describes the structured process of transforming raw data from operational systems into actionable insights. In intralogistics, this involves data from warehouse management systems (WMS), ERP systems, transport management systems (TMS), scanner logs, telematics, and IoT sensors. Depending on the question at hand, different forms of analysis are applied: descriptive analysis describes the current state and historical trends. Diagnostic analysis clarifies why specific deviations have occurred. Predictive analysis estimates future developments based on statistical models, for example in demand or capacity forecasting.
How does data analysis work in practice?
The analysis process begins with data acquisition and cleansing. Incomplete, duplicate, or inconsistent records distort results – data quality is therefore not an afterthought, but a fundamental prerequisite. Only then does the actual evaluation take place, which can range from simple pivot tables to algorithm-driven methods. Results are presented in dashboards or reports and linked directly to KPIs, such as picking rates, throughput times, inventory turnover, or error rates. For insights to be effective, they must be translated into concrete measures and reviewed on a regular basis – in practice, this step often fails due to unclear responsibilities or missing processes.
- Data acquisition and integration from heterogeneous source systems
- Data cleansing and quality assurance as an upstream step
- Selection of appropriate analysis methods based on the question at hand
- Visualization and communication of results to decision-makers
- Translation into operational measures and KPI monitoring
What matters when selecting and implementing a solution?
Whether an analytics initiative actually delivers value depends less on the software used than on three factors: clearly defined and decision-relevant KPIs, a reliable data foundation, and a team capable of interpreting results and translating them into processes. Vendor-dependent above all is how well analytics tools integrate into existing WMS and ERP landscapes. Business intelligence (BI) platforms provide the technical environment, but the actual methodological work – formulating hypotheses, validating models, and interpreting results – remains the responsibility of skilled professionals. Organizations introducing data analysis should also clarify how data sovereignty and access rights are governed, particularly when using cloud-based solutions.
Related terms
- ABC-Analyse
ABC analysis is a method for classifying items, vendors, or processes into three priority classes (A, B, C) based on their economic significance. It forms the basis for differentiated decision-making in warehousing, inventory management, and procurement.
- 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.
- BDE-System
A BDE system (operational data collection system) captures machine and manual process data in real time directly at the source – such as at machines, conveyor systems, or workstations.
- 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.
- Balanced Scorecard
The Balanced Scorecard is a performance measurement system for holistic business management that links financial and non-financial objectives across four perspectives.
- 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.
- Bestandscontrolling
Inventory controlling refers to the systematic monitoring, analysis, and management of stock levels using defined key performance indicators. It connects logistics and procurement to reduce tied-up capital and ensure delivery capability.
- Bewegungsdaten
Movement data captures all transactions and physical goods movements in the logistics process – from goods receipt and stock transfers to dispatch. It contains timestamps, quantities, locations and the units involved, and forms the basis for transparency, control and analysis in warehouse and…
- Bottleneck Analyse
Bottleneck analysis identifies the process step with the lowest capacity in a logistics system, making the limiting factor for overall throughput visible.