ERP Data Quality Improvement Guide

Common Data Quality Issues in Manufacturing ERP

Data quality problems in manufacturing ERP fall into four categories: completeness (missing fields that should be populated), accuracy (incorrect values in critical fields), consistency (same entity represented differently across modules), and timeliness (stale data that no longer reflects reality). The most damaging are BOM errors that affect production, item master inconsistencies that affect purchasing, and customer/vendor duplicates that fragment business relationships.

• BOM accuracy: Average manufacturing site has 3-5% BOM error rate, each costing $500-$5,000 in waste
• Item master duplicates: 8-15% duplicate rate typical, causing fragmented purchasing and inflated inventory
• Customer/vendor duplicates: 5-10% duplication rate, fragmenting spend analysis and credit management
• Stale data: 20-30% of item master records inactive but not flagged, polluting MRP and reporting

Systematic Quality Improvement

Data quality improvement requires both remediation (fixing existing issues) and prevention (stopping new issues from entering). Start with a data quality assessment that quantifies the problem by entity and field. Prioritize remediation based on business impact—BOM accuracy before vendor addresses.

Implement validation rules that prevent invalid data entry. Establish a data governance team with authority to enforce standards and resolve disputes across departments.

• Conduct data quality baseline assessment scoring completeness, accuracy, consistency, and timeliness
• Implement real-time validation rules on critical fields—reject invalid data at entry rather than fixing later
• Establish a data governance committee with cross-functional representation and decision authority
• Schedule quarterly data quality audits with trend reporting to track improvement and catch degradation

AI-Powered Data Quality

Netray's AI agents continuously monitor ERP data quality, detect anomalies as they enter the system, and automatically flag or correct common errors. The agents identify duplicate records using fuzzy matching, detect BOM inconsistencies by comparing against known-good patterns, and alert data stewards to emerging quality issues before they impact operations.