P2P & SAP Data Quality for Siemens
Client Overview
Client Overview
Industry: Industrial
Scope: Data Quality for Procure-to-Pay (P2P) & SAP
Solution Implemented: Automated ESN categorization, spend data quality
management, and supplier master data quality
Complexity:
- 2 SRM systems (World vs. China) and dozens of SAP systems (6 divisions)
- Over 11 million PO lines per year, including >1M catalog orders and 0.5M free-text PO lines
- 1,700 ESN categories across three levels, with a new release every two years
- 19 supported languages (English, French, Italian, German, Spanish, Portuguese, Dutch, Danish, Chinese, Russian)
Challenges
Challenges & Issues
Errors in ESN categorization, affecting:
- Free-text orders
- Static catalog and punch-out purchase requisitions
- Material master data in SAP
Inefficient purchasing processes due to incorrect ESN categorization
Unreliable reporting in Siemens BW (SCM CoRe)
The Solution
Solution Implemented
AI-powered automation to enhance data quality in three key areas:
Indirect Spend Data Quality (Worldwide)
- 90% accuracy certified for purchase requisitions globally, including China
- Migration from Siemens OneSRM to Siemens MyMall WPS in 2020
- Automated categorization of static catalogs and supervised categorization for free-text requests
- Solution live since 2018
Direct Materials & Spend Data Quality
- >90% accuracy certified
- ESN categorization for Siemens Energy Management (400K items)
- Continuous spend data categorization for Siemens Smart Infrastructure
- Categorization automation embedded in SAP, with deployment in progress for other divisions
Supplier Master Data Quality (Worldwide)
- 60% increase in categorization of new suppliers via the Siemens Vendor Portal (Pega)
- Global roll-out started with Siemens Smart Infrastructure & Siemens Energy
The Results
Impact & Benefits
>90% data accuracy to minimize purchasing errors
Scalable, SAP-integrated automation for P2P and spend management
Improved spend visibility and control through standardized categorization
Increased operational efficiency by eliminating manual errors and delays
Conclusion: Siemens achieved structured data governance, reducing errors and improving procurement efficiency through Creactives’ AI-powered solutions.
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Case studies
Creactives has use cases in many industries, but with a common pain: Large and complex multilingual datasets.
