Applying AI in Demand and Inventory Planning

Analyzing Master Data Quality Issues and Validating Product Entries from ERP Sources

Objective

After completing this lesson, you will be able to apply AI-guided validation to detect master data rule violations.

AI for Master Data

How can you trust your plan if your master data is inconsistent? Manually checking every new product entry from your ERP is slow and prone to error. Integrating and Checking Master Data—Practical Example.

This image depicts an AI-enabled approach in SAP IBP for analyzing master data to identify patterns, detect outliers, self-learn rules, and recommend data corrections to improve data quality.

A user wants to validate new product master data as it flows in from ERP. The system’s embedded AI, trained initially on the "best" historical master data, detects typical rule/consistency patterns. When a new, updated spreadsheet is received, the AI scans for mismatches or anomalies. If it spots a value out of bounds, it flags the record for review, allowing the business expert to interactively confirm or override AI suggestions, thus accelerating master data rule setup.

Lesson Summary

  • AI-Powered Data Validation uses embedded AI trained on historical master data to detect patterns, identify outliers, and flag mismatches or anomalies when new data is received, allowing business experts to interactively confirm or override AI suggestions.
  • Automated Corrections and Rule Learning accelerates master data rule setup by recommending data corrections based on detected inconsistencies, while continuously learning rules from validated records to improve future validation accuracy.