Preparing Data for Teamcenter Classification
Blog Article | July 31, 2025
Author
Dourna Jamshideasli | PLM Application Engineer at Saratech
- In Siemens’ Teamcenter Inceptor Program for hands-on Teamcenter administration, configuration, and best practices
- 2 years of experience improving manufacturing processes using Lean Six Sigma principles
- 5 years of technical support in engineering design and simulation in academia
- 9 years of experience with CFD, FEA, CAD, and CAE in research projects
- Passionate about using engineering tools to solve challenges and streamline development
- Ph.D. in Mechanical Engineering with a minor in Physics from Auburn University; Member of ASME and Women in Technology
Implementing Teamcenter Classification is a powerful step toward building a more intelligent, organized, and reusable product data environment. At Saratech, we support engineering and manufacturing organizations in harnessing the full potential of classification to accelerate part searches, reduce duplicates, and streamline bill of materials (BOM) creation. To ensure successful implementation, it is essential to properly prepare your data before getting started.
In this article, we’ll walk you through the key steps to prepare your data for Teamcenter Classification. When implemented by experts, this process can save time, reduce effort, and lower costs in the long term.
Teamcenter Classification: Introduction & Business Value
Throughout the product development process, organizations often face challenges such as design optimization, component duplication, or database management. Teamcenter Classification, a module within Siemens’ Teamcenter PLM software, addresses these issues by promoting part reuse and streamlining data organization. It captures comprehensive product descriptions and integrates seamlessly with upstream and downstream business processes.
Teamcenter supports both Basic and Advanced Classification. Basic Classification focuses on defining objects with properties using standard features and custom hierarchies. Advanced Classification extends these capabilities by capturing supporting ECLASS standards, deeper hierarchies, and modern data exchange formats such as JSON and BMEcat.
Classification data is organized through hierarchical, metadata-driven taxonomies, accessible via the Classification Center within the Teamcenter client. This centralized interface allows users to browse class hierarchies, assign classifications, and manage attribute values. Administrators can define new classes and enforce taxonomy rules, ensuring consistency and compliance. By leveraging structured classes and attributes, engineering teams can more easily locate the right components, whether they are fasteners, circuit boards, or complex assemblies. Seamless integration with CAD and BOM processes further enhances the utility of Teamcenter Classification, ensuring consistent engineering data flow throughout the product lifecycle.
Teamcenter also offers a powerful, flexible search feature that helps users locate parts based on specific criteria, such as resistors within a defined ohms range or bolts of a particular diameter. Classification AI is a key differentiator, which uses machine learning to recommend appropriate classification for new parts based on historical patterns. These auto-classifications occur based on an established network between Teamcenter and Classification data, ensuring high accuracy.
To further support data clarity and control, Teamcenter Classification includes a Library Management module that presents data in a clean, uncluttered format. It supports multiple libraries, integrates with Active Workspace, and improves both data entry and long-term data quality.
When implemented effectively, Teamcenter Classification delivers measurable operational business value. Most notably, it drives cost savings through part reuse. By enabling users to locate existing parts that meet engineering requirements, it reduces duplicate design work, lowers inventory costs, and shortens sourcing cycles. Studies show it can reduce design time by up to 80%, generating annual savings between $4,500 to $23,000. Additionally, reusing validated components ensures consistent, reliable product quality.
The Five Data Preparation Steps
Classification is only as good as the data that feeds it. If your data is inconsistent, incomplete, or misaligned, your classification system won’t deliver the results you expect. Below are five key steps to prepare your data for a successful rollout.
1
Define Class Hierarchies
Start by defining how your organization naturally groups parts and components. A thoughtfully designed hierarchy allows for both intuitive navigation and efficient filtering.
- Group parts by category, such as fasteners, electrical components, sheet metal, etc.
- Consider using industry standards like UNSPSC, eClass, or IEC as a starting point—or create a custom hierarchy that reflects your specific products and processes.
2
List & Normalize Attributes
Once you have your class structure, determine the attributes needed to describe each type of part. These attributes serve as the searchable metadata for your classification system.
- Common attributes might include length, diameter, voltage, color, weight, tolerance, material.
- Standardize units and formats across your dataset. For example, choose “mm” over a mix of “mm,” “millimeters,” and “inches.”
- Where applicable, use value ranges or pick lists to reduce data entry errors and inconsistencies.
3
Clean Your Data
Before importing data into Teamcenter Classification, take time to audit and clean your legacy part data.
- Remove or flag duplicate parts for review.
- Eliminate obsolete parts or clearly mark them as deprecated.
- Fill in missing attribute values and correct obvious errors.
- Apply consistent naming conventions and standard units.
While this step is often most time-consuming, it is also one of the most important. Clean data ensures accurate search results and builds user trust in the system.
4
Map Legacy Data to the New Structure
With the clean data in hand, you can begin mapping your existing parts to the new classification system.
- Use Excel or scripting tools to assign legacy parts to their respective classes
- Match each part to its required attribute fields
- Use tools or customized scripts to assist with the import process
- Validate mappings through test imports before committing to full-scale deployment
This step builds the bridge between your current data and the future classification structure.
5
Plan for Governance
Classification is an evolving structure that must be maintained, not a one-time setup.
- Assign ownership for each major class or group of attributes.
- Establish clear policies for modifying, adding, or retiring classifications.
- Document naming conventions, formatting rules, and attribute guidelines.
- Set periodic review cycles to validate that classification structures and data remain current.
Strong governance ensures your classification system stays clean, consistent, and valuable over time.
Why is Initial Implementation by Saratech Best?
Implementing Teamcenter Classification is a complex project that demands a deep understanding of Teamcenter’s data modeling, classification configuration, and security layers.
A poorly implemented classification system can cause:
- Ineffective searches and frustration for users
- Inconsistencies in metadata that disrupt reporting or automation
- Costly rework and reconfiguration if the structure needs to change after go-live
That’s why we recommend having your first-time implementation led by Saratech’s certified Teamcenter experts. Our team has helped hundreds of companies set up classification systems that are scalable, accurate, and easy to maintain.
Working with Saratech: What to Expect
- A classification structure tailored to your business goals
- Data that is cleaned, normalized, mapped, and ready for production
- Proven best practices and governance strategies that you can rely on
- Comprehensive training for both administrators and end users
- A validated rollout plan with dedicated support before and after go-live
We go beyond tool configuration to ensure you’re getting measurable value across your organization from day one.
Concluding Remarks
Teamcenter Classification lays the foundation for better product data management. When done right, it helps teams save time, reduce costs, and accelerate product development by making engineering data easier to find and reuse. However, success starts with preparation. You can lay the groundwork for an efficient classification system that grows with your business by defining your hierarchies, normalizing your attributes, cleaning your data, and planning governance. With Saratech by your side, you can gain access to trusted PLM experts who can guide you every step of the way.
