In this article, let’s look at hygiene but an important element in effective product lifecycle management – Data Cleansing.
PLM Data Cleansing as per Migration Standards – Template
In today’s product-driven industries, Product Lifecycle Management (PLM) systems serve as the digital backbone for managing product data from concept to retirement. However, even the most advanced PLM systems are only as good as the data they contain. Dirty or inconsistent data can result in design errors, production delays, compliance risks, and poor decision-making.
This is why PLM data cleaning is critical to the success of In today’s product-driven industries, Product Lifecycle Management (PLM) systems serve as the digital backbone for managing product data from concept to retirement.
However, even the most advanced PLM systems are only as good as the data they contain. Dirty or inconsistent data can result in design errors, production delays, compliance risks, and poor decision-making.
PLM implementation and long-term usage.
What is Data Cleaning in PLM Context :
PLM data cleaning is the process of identifying, correcting, or removing inaccurate, incomplete, or irrelevant data.
Why Clean Data Matters in PLM Systems :
A PLM system manages complex and interconnected information: BOMs (Bill of Materials), CAD files, specifications, supplier data, change histories, and more. If this data is incorrect or poorly maintained, it undermines the entire system.
Here are a few reasons why clean data is vital in PLM :
1. Ensures Accurate Product Development
Engineers rely on PLM data to design and develop products. Wrong data can lead to incorrect part selection, version mismatches, and flawed designs.
2. Improves Collaboration Across Departments
Design, manufacturing, procurement, and quality teams use PLM data. Clean, consistent data ensures everyone is on the same page, reducing misunderstandings and delays.
3. Facilitates Regulatory Compliance
Industries such as aerospace, automotive, and medical devices must meet stringent compliance standards. Clean data ensures traceability, proper documentation, and audit-readiness.
4. Enables Efficient Change Management
When engineering change orders (ECOs) are initiated, clean data helps identify affected parts, assemblies, and documentation, ensuring smoother change implementation.
5. Boosts Integration with Other Systems
PLM often integrates with ERP, MES, and CAD tools. Clean, structured data ensures seamless integration and accurate data flow between systems.
Some Best Practices’ for PLM Data Cleaning :
Cleaning PLM data isn’t a one-time effort. It requires a systematic approach to ensure ongoing data quality.
Here are the best practices that product manufacturers and PLM teams should follow:
1. Conduct a Data Audit Before any Migration or Upgrade
Before importing data into a new PLM system (or even major upgrades), conduct a comprehensive data audit:
- Identify duplicates
- Check for missing attributes
- Detect legacy naming conventions
- Assess file link integrity
2. Standardize Naming Conventions
Consistent naming conventions for parts, assemblies, documents, and attributes eliminate confusion and make data easier to manage. Create a naming schema that aligns with engineering logic and business needs.
3. Establish Data Ownership
Assign clear ownership for data creation and updates. For example, engineers own part specs, procurement owns supplier info, and QA owns compliance docs. This accountability helps maintain data quality over time.
4. Remove Duplicates and Obsolete Records
Duplicates are a major source of confusion in PLM. Use de-duplication tools or scripts to identify and merge duplicates. Archive or delete obsolete parts and documents with proper traceability.
5. Validate Data Relationships
Ensure parent-child relationships between assemblies and parts are properly structured. Validate links between BOM items, documents, CAD models, and change records.
6. Automate Wherever Possible
Use scripts and tools to automate recurring data cleaning tasks—such as attribute validation, duplicate detection, or missing metadata checks.
7. Use Controlled Vocabularies and Picklists
Avoid free-text fields wherever structured inputs are needed. Controlled vocabularies (e.g., dropdowns for material types or units) reduce errors and improve data consistency.
8. Track Data Quality Metrics
Define key metrics to monitor data quality—such as percentage of parts missing metadata, number of duplicates, or outdated files. Review these metrics regularly.
9. Provide User Training and Documentation
Train users on proper data entry practices and provide updated documentation. Users are the first line of defense against dirty data.
10. Implement a Continuous Cleaning Program
Data cleaning is not a one-time effort. Establish ongoing processes—monthly reviews, exception reports, and system alerts to keep the PLM data healthy.
Popular Tools for PLM Data Cleaning
Note: The choice of tool depends on the type of PLM system, data complexity, and IT landscape.
Common Data Structure Followed by Manufacturers
To ensure clean and manageable data, most manufacturers follow a structured data model within their PLM systems. Here’s a typical structure:
1. Part Master Records
Each part has a unique identifier and key attributes:
- Part Number
- Description
- Revision
- Classification (e.g., mechanical, electrical)
- Material
- Unit of Measure
- Lifecycle State (Released, Obsolete)
2. Bill of Materials (BOM)
Hierarchical structure showing parent-child relationships:
- Quantity per assembly
- Effectivity dates
- Alternate/substitute parts
- Reference designators
3. Document Management
All related documents (specs, drawings, certifications) are linked to part records:
- File type (CAD, PDF, Excel)
- Document Number
- Version Control
- Approvals
4. Change Management
Records capturing any modification:
- Engineering Change Request (ECR)
- Engineering Change Order (ECO)
- Affected Items List
- Approval history
5. Supplier and Compliance Information
For purchased components:
- Approved Vendor List (AVL)
- RoHS/REACH certifications
- Cost and lead time data
- Supplier part number cross-references
By maintaining this structured approach, manufacturers ensure data is searchable, connected, and compliant, which lays the foundation for digital transformation and smart manufacturing.
Conclusion
PLM systems are not just data repositories—they are dynamic, collaborative platforms that drive innovation, manufacturing efficiency, and product quality. But their effectiveness hinges on the quality of the data they contain.
Investing in PLM data cleaning—through audits, tools, best practices, and structured models—is no longer optional. It’s a strategic enabler for faster time-to-market, reduced costs, and competitive advantage.
Manufacturers that treat data as an asset and adopt a proactive, disciplined approach to cleaning and maintaining it will reap long-term benefits from their PLM systems.
If you’re planning a PLM implementation, migration, or system upgrade, don’t treat data cleaning as a side project. Make it a core pillar of your strategy—because clean data is the bedrock of product excellence.
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