Core Insights - The article presents a systematic solution based on blockchain technology to address vendor data quality issues, which lead to forecasting failures, emphasizing the importance of data integrity and real-time updates [1][14]. Group 1: Vendor Data Quality Issues - The root causes of data distortion include static distortion from incorrect historical pricing records, dynamic lag from unupdated delivery cycle changes, and disconnection between contract terms and order fulfillment data [1]. - Economic quantification indicates that 40% of forecasting failures stem from three types of data pollution [3]. Group 2: Blockchain Clean Data Pipeline Architecture - The architecture employs dual hashing to anchor original data and key field hashes for synchronized storage [2]. - A four-layer data fusion model is designed, consisting of a collection layer (IoT devices/API gateways), a storage layer (consortium blockchain nodes), an analysis layer (AI prediction engine), and an application layer (procurement decision dashboard) [6]. Group 3: Data Cleaning and Verification Mechanisms - The system features an immutable evidence mechanism with smart contracts that automatically verify data logic, isolating failed data for further processing [4]. - Natural Language Processing (NLP) protocols are utilized to automatically extract key fields from unstructured documents, enhancing data accuracy [5]. Group 4: Predictive Optimization Mechanism - The predictive model is restructured with three-factor trusted inputs, leading to significant improvements in forecasting accuracy [7]. - Comparison metrics show that the blockchain clean pipeline improves demand forecast accuracy from 62% to 89%, reduces supplier delivery deviation from ±7 days to ±1.5 days, and lowers unsold inventory ratio from 18% to 5% [9]. Group 5: Risk Control and Credit Assessment - Smart contracts facilitate automatic auditing and dynamic supplier credit assessments, enhancing risk control [9]. - A phased deployment strategy is outlined, focusing on high-value data integration during the pilot phase and expanding to IoT device connections for full-process tracking [9]. Group 6: Implementation Path and Commercial Value - The implementation emphasizes the importance of starting with high-value data to establish quick return benchmarks and designing data contribution incentive mechanisms to accelerate ecosystem development [14]. - The article highlights a triple benefit model, including direct economic gains, compliance value, and brand premium, with examples such as Walmart reducing food recall costs by 90% and Procter & Gamble improving inventory turnover efficiency by 25% [15].