I wanted to share our success story implementing automated part number validation in TC 12.4’s part management module. Before automation, our data quality team was spending 15-20 hours per week correcting invalid part numbers entered by engineers across five global sites.
We built a validation framework using regex validation patterns to enforce our corporate part numbering standards, implemented duplicate part checks using Teamcenter’s query API, and integrated everything into the part creation workflow. The validation framework setup took about 6 weeks with one Java developer and one PLM admin.
Results after 3 months: data entry errors dropped from 12% to 4.8% of new part creations (60% reduction), and our data quality team now spends only 5-6 hours per week on corrections. The validation catches issues in real-time, so engineers get immediate feedback rather than finding out days later when data quality reviews their work. I’ll share implementation details in the thread.
Did you integrate this with the testing/QA process? We’re looking to implement similar validation but want to ensure it doesn’t break our existing part creation workflows during testing phases. How did you handle test data that intentionally violates validation rules?
Great question about test data. You need a validation bypass mechanism for test environments. We typically implement a ‘Test Mode’ flag in preferences that disables strict validation. Alternatively, use a special part number prefix (like TEST-) that has relaxed validation rules.
Let me provide the complete implementation details for anyone looking to replicate this validation framework. I’ll break it down by the three key focus areas:
Regex Validation Patterns:
We implemented a flexible pattern matching system that supports multiple part types and legacy formats. Here’s the architecture:
Validation logic executes on part number field blur event and pre-save validation hook. The validator class loads patterns from preferences and applies them based on part type classification.
For legacy compatibility, we added exception patterns:
This allows legacy parts to be referenced without blocking workflows, but displays warnings to encourage migration.
Advanced pattern features we added:
Sequence range validation: Ensures XXXX sequence is within allocated ranges per site
Check digit validation: Last character of YYY is a calculated check digit (mod-11 algorithm)
Site prefix enforcement: First digit of XXXX must match site code (1-5 for our five sites)
Duplicate Part Check:
This was the most technically challenging aspect due to performance requirements. Our implementation:
In-memory cache layer:
LRU cache holds 10,000 most recent part numbers
Cache is synchronized across method servers using JMS messaging
Cache invalidation on part deletion/modification
Hit rate: 95% for duplicate detection
Database query optimization:
Created composite index: (partNumber, itemType, releaseStatus)
Query uses EXPLAIN PLAN optimized path
Query timeout set to 1000ms (fails fast if database is slow)
Pseudocode for query logic:
// Optimized duplicate check pseudocode:
1. Check in-memory cache for exact match (O(1) lookup)
2. If not in cache, execute indexed database query
3. Query filters: WHERE partNumber = ? AND releaseStatus != 'Obsolete'
4. Return match count and similarity score
5. If count > 0, retrieve matching part details for error message
Fuzzy matching for near-duplicates:
Implemented Levenshtein distance check for similar part numbers
Warns user if part number is within 2 characters of existing part
Catches common typos: P-1234-ABC vs P-1234-ABD
Performance monitoring:
Average validation time: 420ms (95th percentile: 890ms)
Cache hit rate tracked in metrics dashboard
Database query time logged for performance tuning
Validation Framework Setup:
The framework integrates at multiple points in the part creation lifecycle:
UI-level validation (immediate feedback):
Custom widget validator attached to part number input field
Real-time validation on field blur (debounced 500ms)
Visual indicators: green checkmark (valid), red X (invalid), yellow warning (near-duplicate)
Error messages display specific validation failure reason
Workflow integration:
Pre-save validation handler registered on Part creation workflow
Blocks save operation if validation fails (hard stop)
Validation results stored in part audit trail
Workflow routing based on validation status (auto-approve if valid, route to data quality if warnings)
Batch validation utility:
Created admin tool to validate existing parts against new rules
Generates violation reports by site, part type, validation rule
Supports bulk correction through CSV import/export
Used during initial deployment to clean up 15,000 existing parts
Configuration management:
Validation rules stored in version-controlled preference files
Change management process for rule updates
Rule testing framework: 200+ unit tests covering edge cases
Week 7-8: UAT with power users, performance tuning
Week 9: Production deployment (phased rollout to one site at a time)
Week 10-12: Monitoring, bug fixes, user training
Lessons Learned:
User training is critical - engineers initially frustrated by validation “blocking” their work. We conducted 15-minute training sessions emphasizing time savings from catching errors early.
Start with warning mode, not blocking mode. We ran validation in warning-only mode for 2 weeks to build confidence before enabling hard stops.
Performance matters more than perfect accuracy. Users will disable validation if it slows them down. Our 500ms target was based on UX research.
Provide clear error messages with examples. “Invalid part number” is useless. “Part number must match format P-XXXX-YYY. Example: P-1234-A5C” is helpful.
Build admin tools from day one. You’ll need to troubleshoot validation issues, analyze patterns of failures, and update rules. Don’t rely on database queries.
Duplicate part creation: 3.2% → 0.8% (75% reduction)
User satisfaction: 4.2/5.0 (initial resistance overcome)
ROI: 8-month payback period based on data quality labor savings
The validation framework has become a model for other data quality initiatives. We’re now extending it to supplier part validation, document numbering, and project code validation. The key success factor was balancing strictness with usability - validation that helps users rather than blocks them.
How did you handle the duplicate part check performance? We tried implementing something similar but the query to check for existing part numbers was taking 3-4 seconds, which users complained about. Did you use any caching or indexing strategies?
Yes, we support multiple patterns based on part type. Purchased parts use P-XXXX-YYY format, manufactured parts use M-XXXX-YYY, and assemblies use A-XXXX-YYY where X is numeric and Y is alphanumeric. We also validate that the XXXX sequence doesn’t conflict with legacy part numbers from our old system. The regex patterns are configurable through preference files, so we didn’t need code changes when business rules evolved.