Data Quality Challenges and Governance Frameworks for AI Implementation in Supply Chain Management

This research investigates data quality challenges and governance frameworks critical for effective artificial intelligence implementation in supply chain management contexts. The study employs a mixed-methods approach integrating systematic literature review, case study analysis, and expert interviews to identify prevalent data quality issues affecting supply chain AI applications. The investigation reveals six primary data quality challenges: temporal inconsistency, cross-organizational heterogeneity, semantic variability, granularity misalignment, update frequency disparity, and provenance ambiguity. Quantitative analysis demonstrates non-linear degradation relationships between data quality metrics and AI model performance, with accuracy reductions of 15-20% resulting from 5% data quality deterioration. The research establishes that data quality requirements escalate non-linearly with supply chain complexity, requiring exponentially more sophisticated governance approaches in multi-tier environments. A comprehensive maturity assessment model provides structured implementation guidelines with quantitative benchmarks for resource allocation across evolutionary stages. The conceptual framework extends existing data quality theories by establishing supply chain-specific requirements and quantifiable relationships between governance maturity and AI performance metrics. The findings enable supply chain practitioners to prioritize governance initiatives based on organizational maturity levels while providing a foundation for evaluating implementation success through standardized metrics aligned with strategic objectives.