I’ve optimized EBOM and contract splitting performance for several large-scale implementations. The 15-20 minute call-off creation time for 500 items is definitely addressable through systematic optimization across all three key areas.
Contract Splitting Criteria Optimization:
The performance issue stems from evaluating complex splitting logic at runtime for every EBOM item. Your current approach likely queries supplier capacity, calculates lead times, and checks min/max quantities for each of 500 items during call-off creation. With database queries for each evaluation, this becomes the primary bottleneck.
Implement pre-computation architecture:
- Create a nightly batch job that evaluates splitting criteria for all active EBOMs
- Store results in a ‘Splitting Profile’ custom table with fields: EBOM_ID, ITEM_ID, OPTIMAL_SPLIT_SIZE, PREFERRED_SUPPLIER, LEAD_TIME_CLASS
- During call-off creation, read from this profile table instead of recalculating
This shifts expensive computation to batch processing (off-peak hours) and makes call-off creation a simple table lookup operation. We’ve seen this reduce call-off creation time by 65-75% for similar scenarios.
Additionally, simplify your splitting criteria where possible. Instead of calculating exact supplier capacity in real-time, use capacity bands (LOW/MEDIUM/HIGH) that are updated weekly. The marginal accuracy loss is negligible compared to the performance gain.
EBOM Table Indexing Strategy:
Your 3x table growth without corresponding index optimization is causing query performance degradation. EBOM tables need specific indexes to support contract splitting queries:
Create these composite indexes:
- EBOM_ITEMS: (EBOM_ID, ITEM_NUMBER, MATERIAL_ID, SUPPLIER_ID)
- EBOM_HEADERS: (EBOM_ID, STATUS, CREATED_DATE)
- CONTRACT_SPLITS: (CONTRACT_ID, EBOM_ID, CALL_OFF_NUMBER, STATUS)
- SUPPLIER_CAPACITY: (SUPPLIER_ID, MATERIAL_GROUP, VALID_FROM)
These indexes support the typical query patterns in contract splitting logic - joining EBOM items with contracts and suppliers, filtering by status and dates. Without these, database performs full table scans which scale poorly as tables grow.
Also implement filtered indexes on frequently-queried subsets. For example, create filtered index on EBOM_ITEMS where STATUS=‘ACTIVE’ since most queries only care about active items, not historical closed ones.
Database Growth Management:
Uncontrolled table growth is a long-term performance killer. Implement comprehensive data lifecycle management:
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Archiving strategy: Archive completed call-offs after 120 days. This moves historical data to archive tables while keeping active working set small. Configure SAP PLM archiving objects for EBOM and contract data. We typically see 60-70% reduction in active table size after initial archiving run.
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Table partitioning: Partition EBOM tables by created date (quarterly or yearly partitions). This allows database to perform partition pruning - queries for recent call-offs only scan relevant partitions. For a query on last 30 days of call-offs, database scans single partition instead of entire table.
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Purging old split metadata: Contract splitting creates intermediate calculation data that’s often not cleaned up. Implement periodic purge of split calculation logs and temporary data older than 30 days. This prevents unnecessary bloat.
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Regular table maintenance: Schedule weekly database statistics updates and monthly index rebuilds. As EBOM tables grow and change, statistics become stale and indexes fragment. Regular maintenance keeps query optimizer making good decisions.
Additional Optimization - Splitting Granularity:
Review your splitting logic granularity. Creating 50-100 small call-offs from a 500-item EBOM generates excessive database records and processing overhead. Implement minimum call-off size thresholds:
- Minimum 10 items per call-off
- Minimum value threshold (e.g., $5,000 per call-off)
- Maximum split factor (e.g., don’t split into more than 20 call-offs)
This keeps splitting benefits while preventing over-fragmentation. We’ve found that optimal splitting typically creates 8-15 call-offs from a 500-item EBOM, not 50+.
Implementation Roadmap:
For immediate impact (implement first):
- Add critical database indexes (1-2 day effort, immediate 40-50% improvement)
- Implement splitting profile pre-computation (1 week effort, 30-40% additional improvement)
For sustained performance (implement within 30 days):
3. Configure archiving for completed call-offs (2-3 day effort)
4. Implement table partitioning (3-5 day effort)
5. Optimize splitting granularity thresholds (1-2 day effort)
With this comprehensive approach, your 500-item EBOM call-off creation should complete in 3-5 minutes instead of 15-20 minutes. We’ve deployed similar optimizations for clients processing 2,000+ item EBOMs with sub-10-minute call-off creation times.