I’ll provide comprehensive details on our implementation approach that others can adapt for their cloud migrations.
Automated Classification Strategy:
We developed a three-tier classification system that balanced automation speed with accuracy requirements:
Tier 1 - Rule-Based Classification (handled 45% of parts):
Deterministic rules based on explicit part attributes. For example, parts with ‘bolt’ in the name and material=‘steel’ automatically classified as Hardware > Fasteners > Bolts. We created 78 such rules covering common part families. This tier had 98% accuracy because rules were based on clear attribute patterns.
Tier 2 - Machine Learning Classification (handled 47% of parts):
Trained a random forest model on 3,200 manually-classified parts from our legacy system. The model learned complex patterns from 12 part attributes including material, dimensions, supplier category, and description keywords. Feature importance analysis showed that material type and primary function were the strongest predictors. The model achieved 87% accuracy on held-out test data.
Tier 3 - Manual Review Queue (8% of parts):
Parts where automated classification confidence was below 80% threshold, or where rule-based and ML classifications conflicted. These required human judgment - typically parts with unusual attribute combinations or missing critical data.
API-Based Migration Implementation:
The cloud API integration used Python with the requests library. Key technical aspects:
# Authentication and batch processing
headers = {'Authorization': f'Bearer {token}'}
batch_size = 50
for i in range(0, len(parts), batch_size):
batch = parts[i:i+batch_size]
payload = {'items': [{'id': p.id, 'classification': p.new_class}
for p in batch]}
response = requests.post(api_url, json=payload, headers=headers)
We implemented comprehensive error handling: logging all failed updates with part IDs for reprocessing, automatic retry with exponential backoff for transient failures, and daily reconciliation reports comparing source data to Aras cloud state to catch any synchronization gaps.
Rate limiting was managed through: batch processing (50 parts per request), throttling to 10 requests per second, scheduling during off-peak hours (10 PM - 2 AM), and progress monitoring with automatic pause if error rate exceeded 2%.
Data Validation Rules:
Implemented multi-level validation to ensure classification quality:
Pre-classification validation:
- Verify required attributes present (material, function, description)
- Check attribute values against allowed lists
- Flag parts with contradictory attributes
Post-classification validation:
- Verify classification path exists in Aras taxonomy
- Check for logical consistency (e.g., plastic parts not classified under metal categories)
- Compare to similar parts’ classifications using cosine similarity on attribute vectors
- Flag classifications that differ from 90% of similar parts
Ongoing validation:
- Monitor search result click-through rates by classification
- Track user reclassification actions (indicates automated classification errors)
- Quarterly review of classifications with <50% search relevance
Search Accuracy Measurement:
We used a rigorous testing methodology to quantify improvement:
- Created test dataset of 200 search queries from actual user search logs
- For each query, subject matter experts identified the 10 most relevant parts
- Measured search accuracy using normalized discounted cumulative gain (NDCG) metric
- Compared pre-migration vs post-migration search results
Pre-migration NDCG: 0.42 (many relevant parts not found or ranked low)
Post-migration NDCG: 0.75 (78% improvement)
We also tracked user behavior metrics: average search session length decreased from 8.4 to 4.7 minutes, parts found per search increased from 2.1 to 5.8, and user satisfaction surveys showed 89% reported improved search experience.
Downstream Benefits:
Beyond search improvement, automated classification enabled:
- Accurate spend analytics by part category (previously impossible with inconsistent classifications)
- Supplier performance analysis by part family
- Standardization initiatives identifying redundant parts within categories
- Better demand forecasting using classification-based historical patterns
- Improved PLM-ERP integration with consistent part categorization
The reduction in duplicate part creation (34%) came from engineers actually finding existing parts through improved search, rather than creating new parts because search failed. Over 12 months, this avoided approximately 1,600 duplicate parts, saving substantial engineering and procurement time.
Total project effort: 240 hours for development, 120 hours for validation and refinement, 40 hours for execution and monitoring. Estimated manual classification effort avoided: 2,400 hours. ROI was achieved within the first quarter post-migration.