Procurement Glossary
ECLASS: International standard for product classification in Procurement
November 19, 2025
ECLASS is an international standard for the hierarchical classification of products and services, which plays a central role in Procurement for the systematic categorization and management of materials. The standard enables uniform communication between suppliers and purchasers through standardized product descriptions and characteristics. Find out below what exactly ECLASS is, which methods are used and how current developments affect procurement.
Key Facts
- International ISO/IEC standard for hierarchical product classification with over 45,000 product classes
- Four-level hierarchy: segment, main group, group and class of goods with unique 8-digit codes
- Supports multilingual product descriptions in over 16 languages for global use
- Enables standardized features and properties for precise product specifications
- Used by over 4,000 companies worldwide for e-procurement and catalog management
Contents
Definition: ECLASS
ECLASS defines itself as an international standard for the classification and unambiguous description of products and services in digital business processes.
Basic structure and layout
The ECLASS standard is based on a four-level hierarchy that enables all products to be classified systematically. The structure is divided into segments (top level), main groups, groups and product classes (most detailed level). Each class of goods is given a unique 8-digit code to ensure precise identification. In addition, ECLASS defines standardized characteristics and properties that enable a detailed product description.
ECLASS vs. other classification standards
Compared to other standards such as UNSPSC, ECLASS offers a deeper hierarchy and more comprehensive characteristic definitions. While UNSPSC was primarily developed for expenditure classification, ECLASS focuses on the technical product description and is particularly suitable for complex industrial products and their material classification.
Importance of ECLASS in Procurement
ECLASS enables standardized communication between all participants in the supply chain and supports spend analytics through uniform categorization. The standard facilitates supplier searches, price comparisons and the integration of different e-procurement systems through common data structures.
Methods and procedure at ECLASS
The successful implementation of ECLASS requires structured procedures and proven methods for classification and data maintenance.
Implementation strategy and rollout
The introduction of ECLASS begins with an analysis of the existing material group hierarchy and the definition of mapping rules. A step-by-step rollout by product category minimizes risks and enables continuous optimization. Strategically important Categories should be classified first before being extended to the entire product range.
Automated classification processes
Modern automatic spend classification uses machine learning algorithms for ECLASS assignment based on product descriptions and features. These processes significantly reduce manual effort and ensure consistent classification quality. Match merge rules help to identify similar products and avoid duplicates.
Data quality and governance
The maintenance of ECLASS data requires clear governance structures with defined roles and responsibilities. Data stewards monitor the classification quality and ensure that new products are classified correctly. Regular quality checks and the use of data quality KPIs ensure high data standards in the long term.
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Important KPIs for controlling
Measuring the success of ECLASS implementation requires specific key figures for monitoring quality, completeness and degree of utilization.
Classification quality and coverage
The classification rate measures the proportion of correctly classified products in the overall range and should be at least 95%. The degree of completeness indicates the percentage of all materials that have an ECLASS assignment. These KPIs are monitored by regular spot checks and automated validation rules.
Data quality and consistency
The Data Quality Score evaluates the accuracy and completeness of the ECLASS data based on defined quality criteria. The duplicate rate measures the proportion of products classified twice and should be less than 2%. Data quality KPIs also include the timeliness of classifications and compliance with data standards.
Degree of utilization and system performance
The adoption rate shows how intensively ECLASS is used in various business processes, measured by the number of transactions with ECLASS codes. The classification speed measures the time it takes to assign new products and should be continuously improved through automation. The degree of standardization evaluates the uniform application of ECLASS rules across different organizational units.
Risk factors and controls for ECLASS
The implementation and use of ECLASS entails various risks that can be minimized by suitable control mechanisms.
Classification errors and inconsistencies
Incorrect or inconsistent ECLASS assignments can lead to incorrect analyses and procurement decisions. Manual classification in particular increases the risk of assignment errors due to different interpretations of product characteristics. Regular employee training and the implementation of duplicate detection significantly reduce these risks.
Data quality and completeness
Incomplete or low-quality ECLASS data impairs the effectiveness of analyses and reporting. Missing characteristics or outdated classifications can lead to incorrect conclusions in spend analytics. The establishment of data quality scores and regular data audits helps to identify quality problems at an early stage.
System integration and compatibility
The integration of ECLASS into existing ERP and e-procurement systems can pose technical challenges. Incompatible data formats or inadequate interfaces can impair data quality and lead to system failures. Careful planning of ETL processes and comprehensive tests minimize these integration risks.
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Practical example
A mechanical engineering company implements ECLASS for the classification of 50,000 spare parts and components. Initially, critical wear parts such as bearings, seals and drive elements are categorized according to ECLASS. Thanks to the standardized classification, purchasers can now create precise tenders and find suppliers who offer exactly the right products. The automated classification reduces manual effort by 70% and significantly improves data quality.
- Mapping of existing part numbers to ECLASS codes by expert teams
- Integration into the ERP system for automatic classification of new parts
- Training purchasers in the effective use of the ECLASS structure
Current developments and effects
ECLASS is constantly evolving and integrating new technologies to improve classification efficiency and accuracy.
AI-supported classification and automation
Artificial intelligence is revolutionizing the ECLASS application through automatic product recognition and classification. Machine learning algorithms analyze product descriptions, images and technical specifications to identify the appropriate ECLASS category. This development significantly reduces the manual effort involved in material classification and improves the consistency of assignments.
Integration into digital ecosystems
ECLASS is increasingly being integrated into comprehensive digital platforms and supply chain analytics solutions. The connection with IoT systems and digital twins enables automatic product identification and classification in real time. This integration supports supply market intelligence through better market analysis and supplier assessments.
Extended data models and semantic web
The further development of ECLASS includes the integration of semantic web technologies and extended data models. These developments enable an even more precise product description and better interoperability between different systems. Linked data approaches significantly improve the findability and linking of product information.
Conclusion
ECLASS is establishing itself as an indispensable standard for modern procurement through precise product classification and standardized data structures. The integration of AI technologies and automated processes significantly increases efficiency and reduces manual effort. Companies benefit from improved spend analytics, optimized supplier relationships and strategic procurement decisions. The continuous further development of the standard ensures long-term relevance in digital business processes.
FAQ
What is the difference between ECLASS and UNSPSC?
ECLASS focuses on detailed technical product descriptions with extensive features, while UNSPSC was primarily developed for expenditure classification. ECLASS offers a deeper hierarchy with 4 levels and is particularly suitable for complex industrial products, while UNSPSC uses a broader but less detailed categorization.
How is ECLASS classification automated?
Automated ECLASS classification uses machine learning algorithms that analyze product descriptions, technical specifications and manufacturer information. The systems learn from existing classifications and can classify new products with a high degree of accuracy. Rule-based approaches supplement the AI methods for specific product categories.
What advantages does ECLASS offer for spend analytics?
ECLASS enables precise spend analysis through uniform categorization of all purchases. Companies can aggregate spend data, identify potential savings and compare supplier performance. The standardized structure facilitates benchmarking and supports strategic procurement decisions through better data transparency.
How is data quality ensured with ECLASS?
Data quality is guaranteed by defined governance processes, regular audits and automated validation rules. Data stewards monitor classification quality, while KPIs such as completeness and error rates are continuously measured. Training and clear guidelines ensure consistent application.
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