Detailed Case Study

Background & Context

During SAP EWM implementation projects, one of the most critical challenges is ensuring data quality before migration. Industry research shows that "Neglecting Data Quality and Underestimating Migration Challenges" is the #8 reason SAP implementations fail, and "Poor Master Data Quality" is specifically listed as mistake #2 in EWM implementations.

Traditional approaches involve manual data validation, which is time-consuming, error-prone, and doesn't scale. This project addresses the need for automated, repeatable data quality validation that understands SAP EWM data structures and business rules.

Challenge Analysis

The primary challenges addressed:

• Multiple Data Sources: Warehouse data comes from legacy WMS systems, ERPs, spreadsheets, and external feeds, each with different formats and quality levels
• Complex Validation Rules: SAP EWM has specific requirements for master data (materials, storage bins, handling units) that must be validated
• Referential Integrity: Transactions must reference valid materials and locations
• Data Format Standardization: Dates, units, and codes must be standardized before SAP import
• Scalability: Solution must handle thousands of records efficiently

Solution Architecture

The pipeline follows a modular architecture:

┌─────────────────┐
│  Data Sources   │
│  (CSV Files)    │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Ingestion      │
│  Layer          │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Validation     │
│  (Great Exp.)   │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Transformation  │
│  Layer          │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  SAP-Ready     │
│  Output Files   │
└─────────────────┘

Key Components:

1. Ingestion Layer: Loads CSV files, handles encoding issues, and schema changes
2. Validation Engine: Uses Great Expectations to validate against business rules
3. Transformation Layer: Standardizes formats, deduplicates, and enriches data
4. Reporting Module: Generates HTML reports with quality scores and recommendations

Implementation Details

Data Sources

The pipeline processes three core data sources:

• materials.csv: SKU master data (ID, description, weight, volume, unit)
• locations.csv: Bin/location master (warehouse, storage type, bin)
• transactions.csv: Inbound/outbound warehouse movements

Validation Rules

Implemented validation rules include:

• Schema validation (mandatory columns, data types)
• Business rule validation (unique material IDs, valid bin patterns)
• Referential integrity (transactions reference valid materials/locations)
• Data quality checks (positive quantities, valid date ranges)

Code Example: Validation Suite

import great_expectations as ge
import pandas as pd

def validate_materials(df):
    """Validate materials master data against SAP EWM requirements."""
    ge_df = ge.from_pandas(df)
    
    # Material IDs must be unique and non-null
    ge_df.expect_column_values_to_be_unique('MATERIAL_ID')
    ge_df.expect_column_values_to_not_be_null('MATERIAL_ID')
    
    # Weight and volume must be positive
    ge_df.expect_column_values_to_be_between('WEIGHT', min_value=0, strictly=True)
    ge_df.expect_column_values_to_be_between('VOLUME', min_value=0, strictly=True)
    
    # Plant code must match expected format
    ge_df.expect_column_values_to_match_regex('PLANT', r'^[A-Z0-9]{4}$')
    
    return ge_df.validate()

Results & Metrics

The pipeline delivers measurable results:

Lessons Learned

• Early Validation is Critical: Catching data quality issues before SAP migration saves significant time and cost
• SAP Structure Understanding: Understanding EWM data structures is essential for effective validation
• Automation Scales: Automated validation handles large datasets that manual processes cannot
• SQL Skills Transfer: SQL proficiency directly transfers to SAP Open SQL for performance optimization