SIFLM: A Spatio-Relational Intelligence Framework for Enhanced Food Supply Chain Risk Assessment

Han Zhou () and Ling Hu ()
Additional contact information
Han Zhou: Zhejiang University Hangzhou
Ling Hu: Zhejiang center of Food Safety Risk Alerting and Communication Zhejiang Fangyuan Test Group Co. in Administration for Market Regulation, Hangzhou

A chapter in LISS 2024, 2025, pp 624-635 from Springer

Abstract: Abstract Despite the proliferation of food supply chain process data due to digitization strategies, inadequate understanding of the complex spatial relationships within these chains has led to a scarcity of effective assessment models. This deficiency undermines the transition from reactive to proactive management of food safety, resulting in underutilized data resources. Addressing this gap, this paper introduces Spatial Integrated Food Logistics Model(SIFLM), a novel spatio-relational intelligence framework. By incorporating geographic relationships and deploying a relatedness analysis module, SIFLM optimizes the GRU model’s forecasting prowess, enabling the precise identification of critical nodes and prediction of safety vulnerabilities. This advancement equips regulators and industry players with the means to implement proactive strategies, shifting from post-event remediation to anticipatory action. By simulating the dynamic, spatiotemporal characteristics of food supply networks, SIFLM optimizes resource allocation and bolsters food security and sustainability in the face of supply chain informatization and environmental uncertainties.

Keywords: food logistics; risk assessment; spatio-relational intelligence; predictive modeling; resource optimization (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-96-9697-0_48

Ordering information: This item can be ordered from
http://www.springer.com/9789819696970

DOI: 10.1007/978-981-96-9697-0_48

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().