Article Data Reliability
A poorly structured article catalog creates cascading errors: wrong picks, skewed forecasts, inconsistencies between the e-commerce site and the WMS. This tool normalises the article repository from a SAP export. The AI detects inconsistent labels, mixed units and missing categories. The team validates before reimport, keeping control throughout.
Impact chiffré
data accuracy reached
up from 85% before the project
accuracy points gained
fewer picking errors
estimate
channels kept in sync
SAP · e-commerce · WMS
Business value
Fewer picking errors, reliable data for sales forecasts, guaranteed consistency across all channels
Who it's for
Supply chain and purchasing teams
Who it helps
Logistics, e-commerce and forecasting teams who need a consistent catalog to work efficiently
Input
Raw SAP catalog export: non-standard labels, inconsistent units (cl, L, kg mixed), missing categories
Output
Normalised, categorised, team-validated article catalog ready to reimport into SAP
How it works
Purchasing team
Exports the catalog from SAP
non-standard labels, inconsistent units, missing categories
Raw CSV file
Catalog with inconsistencies and missing data
Normalisation engine
Standardises the catalog automatically
- ·Article label standardisation
- ·Unit normalisation (cl, L, kg…)
- ·Missing category completion
- ·Anomaly report generation
Human validation
The catalog manager validates corrections
- ·Anomaly report review
- ·Human arbitrates complex cases
Reliable catalog in SAP
Normalised repository ready for operations
- ·Reliable sales forecasts
- ·e-commerce / WMS consistency
Stack