Enhancing Supply Chain Intelligence with Synthetic Data
A global Automotive OEM used Rockfish DataFuel to generate a scalable, rule-compliant synthetic order bank—enabling buildability validation and AI-driven analysis.Download PDF Version
Customer Overview
A Tier-1 North American Automotive OEM, managing a highly configurable vehicle lineup—where each order consists of a unique identifier plus a combination of interior, exterior, seating, wheel, color, and technology features—partnered with BIMCON to solve a critical challenge: validating complex vehicle configurations across thousands of feature families while preserving data privacy.
Due to limited historical order-bank data and strict governance requirements, the OEM needed a scalable approach to generate privacy-preserving synthetic data that maintained engineering constraints and supported machine learning workloads.
Key Stats
~290K Configurations, 1000s Feature Families, 5X Data Scale, 100% Privacy Safe
Config Complexity
Thousands of interdependent feature families with strict engineering constraints.
Data Scale limitations
Limited historical order-bank data restricted machine learning and scenario analysis.
Privacy & Governance
Strict data governance preventing production order data from being shared externally.
The Challenge
Balancing data needs with privacy requirements while managing complex engineering constraints
Business Challenges
- Data Scarcity
Limited historical order-bank data restricted ML training and scenario analysis - High Validation Costs
Manual configuration validation was time-intensive and error-prone - Privacy Constraints
Production data contained sensitive information preventing external collaboration - Buildability Risks
No systematic way to verify configurations against manufacturing constraints
Technical Challenges
- Complex DependenciesThousands of interdependent feature families with non-obvious constraint relationships
- Rule EnforcementHundreds of engineering rules governing valid feature combinations
- Scale RequirementsNeeded significantly more data volume while maintaining statistical fidelity
The Rockfish Solution
- Schema-based synthetic generation for thousands of feature families
- Cluster-based modeling to enhance accuracy and diversity
- Automated constraint enforcement, including inclusion/exclusion and regulatory rules
- Package-level alignment for multiple trim or configuration groups
- Buildability Analyzer to classify hundreds of thousands of orders at scale
- Synthetic buildable order bank that matches original feature take rates
5x
Dataset Scale-Up
Expanded from limited production data to comprehensive synthetic dataset
~157K
Valid Buildable Configurations
Validated configurations ready for production planning
1000s
Feature Families
Automated rule compliance across complex constraint systems
Business and Technical Impact
- Eliminated dependence on expensive real-world data collection
- Enabled data at scale to support scenario modeling, demand simulation, and constraint validation
- Provided privacy-preserving data for engineering partner collaboration
- Simplified analysis of rare features, hypothetical features, and end-of-life transitions
Key Success Factors
- Clear rule templates enabling automated constraint enforcement
- Collaborative iteration between the OEM’s engineering partner and Rockfish
- High-fidelity modeling using cluster-based generation
- Scalable buildability analysis applied to a large configuration space
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