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Material classes: Definition & important aspects for buyers
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Material classes in a nutshell:
Material classes are systematic groupings of materials according to common characteristics such as technical properties, intended use or economic importance. This classification enables purchasing to implement an efficient procurement strategy, structured supplier selection and optimized inventory management.
Example: An automotive supplier categorizes its 12,000 materials into 8 main classes (A-H), whereby class A "Electronic components" with 3,500 items accounts for the largest purchasing value of 45% of the total volume and is sourced from 25 specialized suppliers.
Contents
What are material classes?
Material classes are systematic groupings of materials and services that are procured by a company. They are used to group similar or related items under common categories. This classification facilitates the organization, management and analysis of procurement data and supports companies in gaining transparency about their purchasing activities and optimizing processes.
Core elements of material classification
Significance for purchasing
Material classes play a crucial role in procurement management as they form the basis for strategic purchasing decisions. Clear structuring enables companies to better control their expenditure, make supplier relationship management more efficient and exploit economies of scale. Classification makes it possible to bundle requirements, negotiate prices and streamline internal processes, which ultimately leads to cost savings and an increased competitive advantage.
Material classes: From manual classification to AI-supported material categorization
The efficient management of material classes is essential for effective procurement and warehousing. Traditionally, materials have been classified manually, which is time-consuming and can lead to inconsistencies. However, with the increasing complexity of supply chains and the amount of data available, there is a need to modernize this process. The integration of AI technologies offers new opportunities to automate and optimize material classification.
Old: Manual material classification
Traditional approach: In traditional material management, materials are manually divided into classes based on defined criteria. This is often done by clerks who assign a corresponding class to each new material master. The tools used are usually limited to simple tables or basic ERP systems without advanced classification functions. This process is not only time-consuming, but also prone to errors and inconsistencies. Different interpretations of classification criteria often lead to inconsistent master data management, which impairs transparency and efficiency in procurement.
New: Automated material classification with artificial intelligence
Automated Material Classification: The modern approach uses artificial intelligence and machine learning to automate material classification. By integrating AI technologies into ERP systems, material data can be analyzed using algorithms and automatically divided into the correct classes. This significantly reduces manual effort and increases the accuracy of classification. In addition, advanced systems make it possible to process large volumes of data in real time, ensuring that material master data is always up to date. The use of standardized standardization such as eCl@ss or UNSPSC is simplified by automation, which facilitates collaboration with suppliers and global procurement.
Practical example: AI-supported classification at an automotive supplier
A leading automotive supplier implemented an AI-supported system for automated material classification. Before the introduction, manual classification took an average of 15 minutes per material master. With over 50,000 material masters, this was an enormous amount of time. After implementing the AI solution, the classification time per material master was reduced to less than one minute. This resulted in time savings of over 12,000 working hours per year. In addition, the classification accuracy increased to over 98%, resulting in a significant improvement in materials management and more efficient procurement.
Conclusion on material classification
Material classes form the foundation for efficient procurement management. By systematically categorizing materials and services, companies can optimize their purchasing processes, reduce costs and make strategic decisions based on data. The success of a material classification system depends largely on well thought-out implementation, continuous maintenance and the active involvement of all departments involved. In view of future-oriented technologies such as AI and predictive analytics, the importance of structured material classes will continue to increase.
