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
Behind the categorization and data quality work across Siemens’ P2P and SAP landscape is the same architecture we now call an Enterprise Data Space: governed Semantic Digital Twins of materials, spend and suppliers, kept accurate and connected across two SRM systems and six divisions.
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.
