Data-Efficient Traceability Analytics for Food Supply Chains: Integrating Adaptive Monitoring, Edge Intelligence, and Blockchain Records

Authors

  • Xiangyuan Li 1,* https://orcid.org/0009-0004-6935-2001
    • Conceptualization
    • Formal Analysis
    • Methodology
    • Writing – Original Draft Preparation
    • Investigation
    • Data Curation
    • Software
    • Validation
    • Visualization
    • Writing – Review & Editing
    • Supervision
    • Project Administration
  • Lijuan Zhang 2 https://orcid.org/0009-0009-8043-8128
    • Data Curation
    • Investigation
    • Software
    • Visualization
    • Validation
    • Resources
    • Writing – Review & Editing
  • Chengcheng Wu 3 https://orcid.org/0009-0009-9088-4025
    • Validation
    • Writing – Review & Editing
    • Investigation
    • Data Curation
    • Software
    • Resources
    • Visualization
1 Shenhua AI Research Institute, Guangzhou Huashang College, Guangzhou, China 511300
2 School of Artificial Intelligence, Guangzhou Huashang College, Guangzhou, China 511300
3 School of Accounting, Guangzhou Huashang College, Guangzhou, China 511300
Corresponding author: lxy5882@gzhs.edu.cn

DOI:

https://doi.org/10.63646/QYTM7776

Keywords:

Food supply chain; Traceability analytics; Adaptive monitoring; Edge intelligence; Blockchain records; Data governance; Cold-chain logistics; Smart contracts

Abstract

Food supply chains increasingly rely on digital traceability systems to document origin, handling conditions, custody changes, and quality-related events. However, the operational value of traceability is often limited by an imbalance between excessive data capture and insufficient evidential clarity. Continuous Internet-of-Things sensing creates large volumes of temperature, humidity, location, shock, and process data, while blockchain systems are not designed to store high-frequency raw streams directly. This article develops a data-efficient traceability analytics framework that integrates adaptive monitoring, edge intelligence, and blockchain records for food supply chains. The framework separates operational monitoring from audit-oriented recording: edge devices preprocess sensor data, detect contextual risk, and select evidence segments, while blockchain records preserve critical state transitions, cryptographic hashes, compliance events, and access permissions. A simulated cold-chain and processing dataset is used to compare static monitoring, event-only logging, and adaptive edge recording. The results indicate that an adaptive edge strategy can reduce stored data volume by more than 70% relative to static one-minute sampling while maintaining high recall of critical events and improving audit reconstruction. The study contributes to business and data analytics by translating traceability from a passive record-keeping function into an intelligent decision infrastructure. It also provides managerial guidance on data governance, blockchain deployment, platform interoperability, and risk-sensitive monitoring design in food supply chains.

How to Cite

Li, X., Zhang, L., & Wu, C. (2025). Data-Efficient Traceability Analytics for Food Supply Chains: Integrating Adaptive Monitoring, Edge Intelligence, and Blockchain Records. Journal of Business and Data Analytics, 3(4), 62-80. https://doi.org/10.63646/QYTM7776

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