Glossary

Data analysis refers to the systematic examination, evaluation, and interpretation of data to identify correlations, patterns, and opportunities for optimization. It lays the foundation for informed decisions and the continuous improvement of processes.

In logistics, large volumes of process, measurement, and movement data from systems such as DWS, WMS, TMS, or sensor technology are analyzed. Using statistical methods, artificial intelligence, and automated analyses, it is possible, for example, to identify throughput, utilization rates, sources of error, or process deviations. Targeted data analysis helps companies make processes more transparent, use resources more efficiently, and better predict future developments.

Practical example:

A logistics center analyzes the measurement and motion data from its conveyor system to identify bottlenecks and optimize system performance in a targeted manner.

See also: Big Data, Data Integrity, DWS, Artificial Intelligence (AI), Predictive Analytics, Process Optimization, Sensor Technology, Supply Chain Visibility

Data capture involves the recording, collection, and storage of information from physical or digital processes. It forms the basis for the further processing, analysis, and control of automated workflows.

In logistics, for example, data is captured using scanners, sensors, cameras, DWS systems, or RFID technologies. This includes information such as dimensions, weight, identification characteristics, status, or movement data for goods and load carriers. Automated data capture reduces manual data entry, improves data quality, and enables transparent control of warehousing, transportation, and shipping processes. When integrated with ERP, WMS, and TMS systems, it creates a seamless digital process chain.

Practical example:

As a package passes through a measuring station, its barcode, dimensions, and weight are automatically captured and made available for shipping, sorting, and freight billing.

See also: Barcode, DWS, Identification, RFID (Radio-Frequency Identification), Sensor Technology, Master Data Capture, System Integration, Track & Trace

Data capture accuracy describes the precision with which a measurement or identification system determines data from packages, pallets, or other loading units. It indicates how reliably the captured values correspond to the actual characteristics of an object.

Capture accuracy is influenced by various factors, such as sensor technology, measurement methods, conveyor speed, environmental conditions, and the nature of the object being measured. High accuracy is crucial for automated logistics processes, as it enables error-free master data, reliable freight billing, and secure process control. Modern DWS systems achieve consistently high measurement quality through the use of 3D sensors and intelligent analysis software.

Practical example:

In an e-commerce fulfillment center, thousands of shipping boxes are measured daily with high measurement accuracy so that warehousing and shipping processes can run without manual follow-up checks.

See also: 3D sensors, dimensional measurement, DWS, calibratability, weight measurement, measurement accuracy, sensor technology, master data capture

Data exchange involves the transfer and sharing of information between different systems, applications, or devices. It enables end-to-end data processing and the automated coordination of digital processes.

In logistics, reliable data exchange is crucial for integrating ERP, WMS, TMS, and automation systems. Through interfaces, for example, order data, master data, measurement values, status information, or shipping data are transferred between the involved systems. Structured data exchange reduces manual data entry, improves data quality, and lays the foundation for transparent and efficient processes.

Practical example:

A DWS system automatically transfers recorded dimensions, weight, and identification data to the transport management system so that the shipment can be processed further without manual post-processing.

See also: Data capture, data integrity, data interface, digitization, ERP system, system integration, transport management system (TMS), warehouse management system (WMS)

Data integrity refers to the completeness, accuracy, and consistency of data throughout its entire lifecycle. It ensures that information is stored, processed, and transmitted without alteration, in a traceable manner, and reliably.

In logistics, a high level of data integrity is essential for error-free material flows and automated processes. It is supported by standardized data capture, secure transmission channels, and plausibility checks. Accurate master data forms the foundation for transport planning, warehouse management, freight billing, and end-to-end traceability.

Practical example:

At goods receipt, the automatically captured master data for a package is verified and transmitted unchanged to all connected systems, ensuring that downstream processes operate on a uniform data basis.

See also: Data exchange, DWS, freight data, master data capture, supply chain management (SCM), Track & Trace, traceability, quality assurance

A data interface enables the standardized exchange of information between different software or hardware systems. It establishes the technical connection through which data can be automatically transferred, processed, and used in downstream processes.

In logistics, for example, data interfaces connect DWS systems, ERP, WMS, and TMS solutions, as well as automation components. They are used to exchange order data, measurement values, identification information, and status messages. A reliable data interface reduces manual data entry, improves data quality, and enables end-to-end digital workflows. It is a central component of modern, networked logistics systems.

Practical example:

A DWS system automatically transmits the measured dimensions, weight, and identification data of a shipment to the transport management system via an interface.

See also: Data exchange, Data capture, Digitization, ERP system, System integration, Transport management system (TMS), Warehouse management system (WMS), Data integrity

Deep learning is a subfield of machine learning based on artificial neural networks with multiple processing layers. It enables the automatic recognition of complex patterns and relationships in large amounts of data.

In logistics, deep learning is used for applications such as image processing, object recognition, quality control, and the automatic analysis of packages. The algorithms learn from extensive training data and continuously improve their recognition accuracy. This makes it possible to automate processes, reduce errors, and support decision-making based on precise data.

Practical example:

At a package sorting center, a deep learning model automatically detects damaged boxes, missing labels, and protruding cargo during the conveyor process.

See also: Artificial Intelligence in Logistics, Computer Vision, Data Analysis, Artificial Intelligence (AI), Machine Learning, Quality Control, Master Data Capture, 3D Sensors

The degree of automation describes the extent to which processes or work steps are carried out autonomously by technical systems. It indicates the proportion of automated processes relative to manual tasks.

A high degree of automation is achieved through the use of sensor technology, robotics, conveyor systems, and intelligent software systems. In logistics, it enables faster, more precise, and reproducible handling of material flows, as well as a reduction in manual intervention. This increases efficiency, process reliability, and the quality of the recorded master data.

Practical example:

In a parcel sorting center, a DWS system increases the level of automation by automatically identifying, measuring, and weighing packages during the conveyor process.

See also: Automatic measurement, DWS, conveyor technology, Industry 4.0, intralogistics, material flow, master data capture, system integration

A digital twin is a digital representation of a physical object, a piece of equipment, or a process. It realistically depicts the current state based on continuously collected data and enables its analysis, monitoring, and simulation.

Digital twins link sensor data with virtual models and are used in logistics to optimize material flows, facilities, and warehouse processes. Through continuous synchronization with real-time operational data, processes can be analyzed, changes simulated, and opportunities for optimization identified early on. This allows decisions to be made based on up-to-date and reliable data.

Practical example:

In a logistics center, a digital twin maps the material flow in real time and simulates the effects of changed conveyor routes on the entire process.

See also: 3D data, data analysis, Industry 4.0, artificial intelligence (AI), material flow, process optimization, sensor technology, master data capture

Digitalization involves the conversion of analog information, processes, and business workflows into digital data and automated applications. It lays the foundation for more efficient processing, networking, and use of information.

In logistics, digitalization enables the end-to-end collection, processing, and analysis of process data. Sensors, DWS systems, cloud applications, and networked IT systems provide transparency into the flow of goods and enable automated decision-making. By integrating ERP, WMS, and TMS solutions, processes can be optimized, resources can be allocated more effectively, and new digital business models can be developed. Digitalization is thus a key driver of modern, data-driven logistics processes.

Practical example:

A logistics center digitizes its shipping processes by automatically capturing measurement, weight, and identification data and transferring it to connected systems without manual entry.

See also: Data analysis, DWS, Industry 4.0, intralogistics, process automation, sensor technology, system integration, supply chain visibility

Dimensioning systems are measurement systems designed to automatically capture the length, width, and height of packages, pallets, or other loading units. They provide precise dimensional data for logistics processes, master data entry, and freight billing.

Depending on the application, dimensioning systems use laser scanners, 3D sensors, or camera-based measurement methods. They can be stationary or integrated into conveyor systems and capture objects both at rest and in motion. The collected data is transferred directly to warehouse management, transport management, or ERP systems and forms the basis for automated processes, optimal load space planning, and high data quality.

Practical example:

In a parcel center, a dimensioning system automatically measures every shipment so that shipping costs can be calculated correctly and conveyor processes can be controlled without manual intervention.

See also: 3D sensors, automatic measurement, dimension capture, DWS, freight measurement, weight capture, master data capture, volumetric weight

DIN EN ISO 9001 is an internationally recognized standard for quality management systems. It specifies requirements that enable companies to systematically control, monitor, and continuously improve their processes.

The standard is based on a process-oriented approach and promotes, among other things, customer focus, risk-based thinking, and the continuous improvement of processes. Companies certified to DIN EN ISO 9001 demonstrate that their processes are documented, traceable, and regularly reviewed. In logistics, this helps ensure consistently high product and service quality as well as reliable and transparent workflows.

Practical example:

During an audit, a logistics company uses its DIN EN ISO 9001-certified quality management system to demonstrate that all processes—from incoming goods inspection to shipping—are documented and continuously improved.

See also: Audit, Documentation, Process Optimization, Quality Control, Quality Management, Traceability, Master Data Entry, Certification

A distance sensor is a sensor used to measure the distance between the sensor and an object without physical contact. It provides precise distance readings and forms the basis for numerous measurement, control, and automation processes.

Depending on the application, distance sensors use technologies such as laser, infrared, ultrasound, or time-of-flight. In logistics, they are used for position detection, dimensional measurement, and object recognition, and are often combined with cameras or 3D sensors. This enables reliable tracking of packages and loading units in automated material flows.

Practical example:

In the goods receiving area, a distance sensor measures the position of a package and supports automatic measurement in the DWS system.

See also: 3D sensors, dimensional measurement, DWS, cargo measurement, infrared fan-beam scanner (scanning laser scanner), laser scanner, sensor technology, Time-of-Flight (ToF)

Distributed Order Management (DOM) is a system for the intelligent control and distribution of customer orders across multiple warehouse, production, and shipping locations. It uses defined rules to determine the optimal fulfillment location for each order.

A DOM system takes into account, among other factors, inventory levels, delivery times, shipping costs, priorities, and available capacity. This allows orders to be flexibly distributed across different locations and delivery processes to be efficiently coordinated. Especially in omnichannel retail and internationally interconnected logistics networks, Distributed Order Management helps shorten delivery times, make better use of inventory, and increase customer satisfaction.

Practical example:

An online retailer uses a DOM to automatically determine whether an order should be shipped from a central warehouse, a regional branch, or directly from a retail store to ensure the fastest possible delivery.

See also: ERP system, warehouse management system (WMS), omnichannel logistics, process optimization, supply chain management, transport management system (TMS), goods flow, Warehouse Management System (WMS)

Document handling encompasses the automatic creation, processing, management, and delivery of documents within logistics and industrial processes. It ensures that all relevant documents are complete, accurate, and available at the right time.

Document handling includes, among other things, the creation, assignment, archiving, forwarding, and issuance of shipping labels, delivery notes, waybills, customs documents, and other process-related documents. Through integration with ERP, WMS, and transport management systems, documents are automatically linked to the corresponding orders or shipments. This reduces manual steps, prevents errors, and increases the transparency and efficiency of the entire process chain.

Practical example:

After automatic measurement is complete, the system generates all necessary shipping documents, assigns them to the shipment, and transmits them digitally to the warehouse management system.

See also: Data capture, ERP system, waybill, redtagger, shipment document handling, shipping label, warehouse management system (WMS), customs clearance

Documentation involves the structured collection, storage, and provision of information regarding products, processes, systems, or procedures. It serves to make knowledge traceable and to create a uniform basis for operation, testing, and further development.

In logistics and automation, documentation includes, for example, technical documentation, process descriptions, measurement data, maintenance records, and shipping information. Digital documentation systems enable centralized management and support the traceability of processes. Complete and up-to-date documentation contributes to quality assurance, process reliability, and compliance with legal and customer-specific requirements.

Practical example:

For an automated measurement system, commissioning logs, test results, and maintenance information are digitally documented and stored for future reference.

See also: Audit, Data Integrity, Document Handling, Quality Assurance, Traceability, Validation, Maintenance, Certification

A DWS (Dimension-Weight-Scanning) system is an automated measurement system for simultaneously capturing the dimensions, weight, and identification data of packages or loading units. It provides the data required for logistics processes, master data entry, and freight billing in a single data-capture operation.

A DWS system combines dimension capture, weight capture, and barcode or OCR recognition into an integrated solution. Depending on the application, data capture takes place while the package is stationary or while it is in motion on a conveyor system. The information obtained is immediately transferred to warehouse management, transport management, or ERP systems and forms the basis for automated material flows, high data quality, and transparent shipping processes.

Practical example:

On an automated conveyor line, a DWS system captures each shipping package in a matter of seconds and transmits all measurement and identification data directly to the transportation management system.

See also: Automatic measurement, barcode, dimensioning systems, dimension capture, freight measurement, weight capture, master data capture, volumetric weight

Dynamic measurement refers to the process of capturing the dimensions of packages or load units while they are in motion. It enables precise and automated dimensional measurement without interrupting the flow of materials.

Depending on the application, dynamic measurement uses 3D sensors, laser scanners, or camera-based measurement systems that deliver reliable measurement results even at high conveyor speeds. It is an essential component of modern automatic weight and dimension measurement (DWS) systems and helps companies efficiently track large volumes of shipments, automate processes, and ensure high data quality.

Practical example:

In a package distribution center, several thousand shipments per hour are automatically measured as they travel along the conveyor system without having to halt the material flow.

See also: 3D sensors, automatic measurement, dimensioning systems, dimension capture, DWS, cargo measurement, conveyor speed, static measurement