Because a Clean Catalog Is More Than Just a List of Names
Accurate product specifications are critical across procurement, planning, logistics, and customer service. But many ERP catalogs suffer from missing units of measure (UOMs), incomplete weights and dimensions, or inconsistent packaging details—especially when SKUs are copied over, imported from suppliers, or updated during a rushed launch.
AI is now making it easy for catalog managers and ERP admins to auto-suggest missing values and detect UOM inconsistencies, improving both accuracy and user trust in the system.
The Most Common Data Gaps in Product Catalogs
No declared net or gross weight
UOM mismatch between order UOM and inventory UOM
Missing length/width/height for packaging specs
Missing density values for liquids, powders, or castables
Incomplete BOMs or classification tags (like UNSPSC codes or HTS codes)
These gaps trigger freight misquotes, incorrect PO quantities, planning errors, and regulatory compliance risks.
What AI Can Infer—And Why It Works
AI tools can analyze:
Product descriptions and attribute fields using natural language processing
Similarity across SKUs (e.g., “Alumina 90% brick” vs. “90% AL block”)
Supplier catalogs and prior transactions to infer standard packaging
Historical UOM conversions from transaction logs
Engineering files for standard dimensions and tolerances
This allows AI to suggest likely:
Base UOMs and valid alternate UOMs (e.g., LB → KG, EA → Box)
Weight, volume, and density values
Pack sizes and pallet load configurations
Product category tags for MDM alignment
Impact in the Real World
A cement refractory company had over 5,000 SKUs missing packaging dimensions and over 800 with mismatched UOM pairs. Within one quarter of AI-based cleanup:
Freight overcharges dropped by 17%
PO approval time shortened by 24%
Planning exception alerts fell by over 30%
Why This Matters for Cross-Functional Teams
Planners get cleaner UOM conversions for demand plans
Buyers avoid over-ordering due to unclear pack sizes
Warehouse teams stop guessing pallet configs
Customer service reps have reliable spec sheets on demand
In short: AI fills in the blanks, so humans can make better decisions—faster and with fewer costly errors.
