This article delves into the innovative application of advanced OCR (Optical Character Recognition) technology-based supply chain data collection solutions in the fast-moving consumer goods (FMCG) industry. Through a practical case study of China's leading dairy company, the Yili Group, this article demonstrates how AI-driven OCR technology overcomes the challenge of recognizing packaging marks in complex environments, improving order processing efficiency by over 80%. It provides a key digital solution for ensuring product quality traceability and supply chain transparency.

User Background

Yili was founded in June 1993. It is a dairy product production and processing enterprise headquartered in Hohhot City, Inner Mongolia Autonomous Region. Its product line includes dairy products such as Yili Ambrosia Milk and Yili Satin Milk.

Challenges

With the continuous expansion of its business scale, Yili faces a core challenge in its supply chain management: to ensure end-to-end traceability, the laser-printed QR codes on its dairy product packaging serve as the unique identifier for all inbound and outbound records. However, differences in printing equipment, processes, and packaging background colors across different production factories result in uneven print quality and contrast of the QR codes, making them difficult to read stably with traditional recognition equipment. Manually transcribing batch numbers is not only inefficient and error-prone but also affects the timeliness and accuracy of supply chain data.

Before deploying a professional OCR recognition solution, Yili faced the following specific operational bottlenecks in the data collection process of its supply chain:

• Low manual operation efficiency and high error rate: Reliance on manual transcription and entry is a labor-intensive, time-consuming, and highly error-prone task. Verifying handwritten records is also tedious and effort-intensive.
• Poor data collection stability, affecting process continuity: Frequent QR code recognition failures caused by differences in packaging background colors and fluctuations in print quality repeatedly interrupted the automated inbound/outbound processes, requiring manual intervention. This became an obstacle to improving operational efficiency.
• Limited supply chain transparency and traceability reliability: Instability and delays in the basic data collection process directly affected the accuracy of inventory data in upstream systems, thereby posing challenges to the full-process traceability of product quality and rapid, collaborative supply chain decision-making.

By deploying an AI-driven data collection solution integrated with Seuic's proprietary OCR algorithm, Yili achieved significant efficiency improvements in its supply chain management:

• Leap in operational efficiency: Replaced traditional paper-based processes and manual entry with real-time, accurate electronic information flow, improving the overall efficiency of order processing and related business by over 80%.
• Achieved high-precision data collection and synchronization: Leveraging powerful dynamic performance and automatic scheduling capabilities, significantly improved the response speed of data processing and OCR recognition, ensuring real-time and accurate synchronization of data across all supply chain links.
• Guaranteed end-to-end traceability: Fundamentally solved the problem of data gaps caused by unidentifiable marks, ensuring that every single product can be accurately tracked. This strongly supports the product quality management system and consumer confidence.

The solution adopted by Yili is an intelligent data collection solution that deeply integrates advanced hardware with AI algorithms:

• Deployment of high-performance data collection hardware: Utilized RFID readers equipped with Seuic's high-performance scanning engines, providing a stable and clear hardware foundation for image capture in complex environments.
• Application of proprietary AI OCR recognition algorithm: The core lies in utilizing Seuic's proprietary OCR algorithm and technology. Through post-processing techniques and deep learning models, this solution effectively addresses recognition challenges caused by printing background interference, character adhesion, and multiple detection frames.
• Achieving precise recognition in complex scenarios: The OCR algorithm, specially trained and optimized, is capable of adapting to the color, contrast, and quality variations of laser-printed QR codes on different packaging backgrounds from Yili's various factories. It has achieved high-precision, highly robust recognition of printed characters under various complex conditions, thereby completing rapid and accurate data entry and collection, completely replacing error-prone manual operations.