a5c-ai Master Data Quality Manager
Overview
The Master Data Quality Manager provides supply chain master data quality monitoring, validation, and improvement capabilities. It ensures data accuracy across item, supplier, location, and BOM master data to support reliable supply chain operations and analytics.
Capabilities
- Item Master Data Validation: Product data completeness and accuracy
- Supplier Master Data Cleansing: Vendor data quality improvement
- Location/Plant Data Verification: Facility data accuracy
- BOM Accuracy Checking: Bill of materials validation
- Lead Time Validation: Lead time data accuracy assessment
- Data Completeness Scoring: Missing data identification
- Duplicate Detection: Redundant record identification
- Data Quality Trending: Quality metric tracking over time
Input Schema
data_quality_request:
data_domains:
item_master: boolean
supplier_master: boolean
location_master: boolean
bom_master: boolean
lead_time: boolean
validation_rules:
completeness_rules: array
accuracy_rules: array
consistency_rules: array
timeliness_rules: array
data_sources:
erp_system: string
extract_files: array
quality_thresholds:
critical_fields: object
acceptable_error_rate: float
Output Schema
data_quality_output:
quality_scorecard:
overall_score: float
by_domain: object
item_master:
completeness: float
accuracy: float
consistency: float
timeliness: float
supplier_master:
completeness: float
accuracy: float
consistency: float
timeliness: float
location_master:
completeness: float
accuracy: float
bom_master:
completeness: float
accuracy: float
lead_time:
accuracy: float
issues_identified:
critical: array
high: array
medium: array
low: array
duplicate_analysis:
potential_duplicates: array
merge_recommendations: array
completeness_report:
missing_fields: array
missing_by_domain: object
data_cleansing_actions:
recommended_fixes: array
automated_corrections: array
manual_review_required: array
trend_analysis:
quality_over_time: object
improvement_areas: array
degradation_alerts: array
Usage
Comprehensive Data Quality Assessment
Input: Master data extracts, validation rules
Process: Validate against quality rules
Output: Data quality scorecard with issues
Duplicate Detection and Resolution
Input: Supplier or item master data
Process: Identify potential duplicates
Output: Duplicate report with merge recommendations
Lead Time Data Validation
Input: Lead time master, historical receipt data
Process: Compare stated vs. actual lead times
Output: Lead time accuracy report
Integration Points
- ERP Systems: Master data extraction
- MDM Platforms: Master data management integration
- Data Quality Tools: Profiling and cleansing platforms
- Tools/Libraries: Data quality frameworks, MDM platforms
Process Dependencies
- All supply chain processes (cross-cutting)
- Demand Forecasting and Planning
- Inventory Optimization and Segmentation
Best Practices
- Define clear data ownership
- Establish data quality metrics and targets
- Implement preventive data quality controls
- Schedule regular data quality reviews
- Automate data quality monitoring
- Address root causes, not just symptoms