A large enterprise operating across multiple business units relied on a centralized material master database to support its procurement, inventory, and supply chain operations. As the organization grew, a massive surge in new material requests forced teams to process thousands of records while strictly maintaining data governance.
Accurate master data was critical for inventory visibility, reporting consistency, and operational control. However, because the material onboarding process relied entirely on manual interpretation, classification, and validation, it created a severe bottleneck that hindered both speed and scalability.
Maintaining a high-quality material master takes far more than just generating new records. Teams must analyze each request to spot existing duplicates, classify the material correctly within the enterprise hierarchy, and enrich it with standardized codes and descriptions.
Because this process was largely manual, it heavily drained domain experts. Teams had to interpret messy, non-standardized descriptions, hunt for duplicates, and juggle multiple reference systems just to assign a single valid material code.
The organization needed a smarter approach that could automate data enrichment, improve consistency, and accelerate material onboarding without compromising governance standards.
15-minute processing times bottlenecking large-scale material onboarding.
Weak duplicate detection missing near-matches and naming variations.
Inconsistent material descriptions ruining searchability and reporting accuracy.
Manual classification processes leading to inconsistent master data.
Complex code assignments driving up risky manual cross-referencing.
Unscalable batch processing triggering massive administrative overload.
ScryAI implements its AI-based Product Matching solution to turn material record management into an intelligent, automation-driven process.
By ingesting bulk spreadsheet requests, the platform eliminates manual analysis. It accurately matches products by evaluating multiple material attributes like part numbers and descriptions simultaneously.
The system catches exact duplicates and easily identifies near-matches that traditional search methods often miss. This provides clear confidence scores and smart alternative recommendations.
The platform automatically predicts and assigns the material class, product hierarchy, and commodity category. It also generates standardized short and detailed long descriptions to maintain a clean central data environment.
Generating the next valid material code, the platform cross-checks existing records. If any information is missing or conflicting, workflows flag the data, ensuring incomplete entries never enter your system.
| Metric | Outcome |
|---|---|
| Material Processing Time | Reduced from approximately 15 minutes per record to 1 minute or less |
| Productivity Improvement | More than 90% reduction in manual processing effort |
| Duplicate Detection | Improved identification of duplicate and near-duplicate materials |
| Data Quality | Consistent material descriptions, classifications, and attributes |
| Material Onboarding | Faster creation and approval of new material records |
| Operational Efficiency | Automated bulk processing of 100 to 1,000+ records |
| Cost Optimization | Reduced duplicate procurement and inventory-related inefficiencies |
| Scalability | Supported enterprise-wide growth without proportional increases in manual effort |
Free your team from the manual grind of material creation. ScryAI handles the heavy lifting of duplicate detection, classification, and code generation, so you can accelerate onboarding, lock down data governance, and scale your supply chain effortlessly.