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How AI Suggests Standardized Naming Conventions for Multi-Plant Product Databases

By Glazix | June 10, 2025

One Catalog. One Language. AI Makes Naming Uniform Across Every Plant.

In multi-plant organizations—whether manufacturing architectural glass, structural ceramics, or high-performance refractories—product naming conventions are notoriously difficult to standardize. Different teams use different abbreviations, region-specific descriptors, or inherited legacy terms. Over time, ERP and PIM systems become littered with inconsistent records, leading to duplicate SKUs, poor searchability, and planning errors.

AI is now helping master data teams generate and enforce standardized naming conventions, not by brute force, but through intelligent pattern recognition and field-level suggestions.

Why Product Naming Gets Out of Control

Common causes include:

SKUs created locally with no global naming template

Copy-paste behavior from similar items, with inconsistent edits

Different plants using their own acronyms for finishes, pack sizes, or product forms

Naming formats that evolve—but older items are never updated

Human entry errors and outdated training documents

These inconsistencies degrade the value of even the most sophisticated ERP system, hurting usability, integration, and customer-facing documentation.

What AI Does Differently

AI platforms trained on your company’s product structure and nomenclature can:

Scan existing product names and group them by format and content structure

Detect anomalies or inconsistent field order (e.g., size before finish, or grade before color)

Flag duplicate or near-duplicate names with different content or attributes

Recommend standardized templates based on the most complete and consistent records

Apply rules like “Always show thickness after size” or “Capitalize finish codes” automatically

You don’t get enforced rigidity—you get scalable consistency that reflects your operations.

Real-World Example

A five-plant tile manufacturer had more than 7,000 SKUs named with combinations like “12×24 Gray Matte,” “Grey 12×24 Matt,” and “Tile M 12×24 G.” AI analysis suggested a uniform format: [Product Type] [Size] [Color] [Finish]. With adoption, internal item lookup time fell by 35%, and catalog publishing cycles shortened by 50%.

Key Benefits

Faster internal search, fewer misquotes

Easier eCommerce, EDI, and catalog integration

Cleaner data handoffs across facilities and functions

Better training and onboarding for new ERP users

AI helps product naming evolve from chaotic to consistent—without forcing every plant to speak the same dialect.


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