Industry Pain Points

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  Mismatch between Flexible Market Demand and Traditional Supply Chain Systems

  Agricultural enterprises offer diversified product categories, each with unique requirements for production, transportation, warehousing, and sales environments. Traditional supply chain structures are cumbersome and unable to accommodate flexible service changes at the sales end.

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  Inability to Monitor Production Environments and Difficulty in Applying Historical Experience

  Difficulties in IoT data collection make it impossible to monitor data during production/cultivation and sales processes. This hinders the transformation of historical data and experience into methodologies to guide production.

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  Low Level of Informatization

  Difficulties in IoT data collection prevent data monitoring during production/cultivation and sales processes, impeding the transformation of historical data and experience into methodologies to guide production. (Note: This point is somewhat redundant with the previous one; consider merging or emphasizing different aspects if needed.)

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  Challenges in Food Safety Management and Control

  Agricultural products involve consumer safety issues, necessitating full-process traceability from production to sales, strict regulatory and management systems, and comprehensive monitoring of execution throughout the entire process.

Business Objectives

Leveraging IoT, big data, cloud computing, and other technologies, we aim to build an enterprise digital platform that integrates F2B2B (Farm-to-Business-to-Business), supply chain management, logistics, quality traceability, and smart park operations, thereby forming a new agricultural business model. By relying on data integration innovation, we will establish a new agricultural product supply chain system centered around digital order-driven operations, encompassing all product categories and the entire supply chain, and providing fully online integrated services. This will facilitate the transformation of agricultural product circulation towards digitization, supply chain optimization, and service-orientation. Our goals include addressing food safety issues in wholesale markets and enhancing urban safety supply levels and regulatory capabilities.

Solution Architecture

Customer Value

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  Digitalization Capacity Building for Internal Operations

  Through the construction of a big data platform, we will gradually establish an integrated full-supply chain system and a centralized warehouse, supply, and distribution system. This will complete the foundational setup for our enterprise's digitalization capabilities, establishing abilities for unified data storage, data aggregation, data processing, unified management of data assets, data operational analysis, and data sharing services. These will provide foundational support for business digitalization.

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  Data-Driven Innovation in Business Models

  By leveraging the big data platform to form a one-stop data collection center, we will build standardized full linkages for production, processing, and distribution. This will enable online-offline integration, precise matching of supply and demand, order consolidation, demand integration, precise services, and collaborative distribution. It will serve the incubation and transformation towards smarter and more modern business models.

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  Traceability in Food Safety Processes

  By collecting data from the IoT platform through the big data platform, we will connect the entire process of agricultural product production, processing, environment, logistics, and O2O marketing. This will realize digital monitoring of the entire product lifecycle from production to sales, enabling corporate regulation and auditing, consumer traceability inquiries, and repeat purchases.

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  Real-Time Monitoring of Product Production Environment

  Through real-time data collection, modeling, and analysis of production environment data, we can achieve regulation of product production/growth status, tracking and supervision of production/planting execution plans, and provide environmental guidance for production/planting based on historical data analysis.