Supplier Risk Assessment—A Quantitative Tool for the Identification of Reliable Suppliers to Enhance Food Safety Across the Supply Chain
Sina Röhrs, Sascha Rohn, Yvonne Pfeifer, Anna Romanova

TL;DR
This paper introduces a quantitative AI-based tool to assess supplier reliability and improve food safety across global supply chains.
Contribution
A novel supplier risk assessment framework integrating multiple indicators for AI-based supplier evaluation is developed.
Findings
A supplier risk score can be calculated using weighted indicators like hazard risk and audit performance.
Manual testing of the framework showed promising results for identifying reliable suppliers.
Implementation into an AI-supported database is the next step for practical use.
Abstract
Food safety is a global issue that can be enhanced by collaboration with reliable suppliers. Given the complexities of international supply chains, identifying reliable suppliers is often challenging and resource-intensive. Integrating artificial intelligence (AI) offers a valuable opportunity to improve efficiency in this process. The aim of the present study was to develop a quantitative supplier assessment scheme for implementation in an AI-supported database. The framework developed incorporates different indicators, including the hazard risk, incident category level, vulnerability of a commodity, audit performance, logistic performance index, gross domestic product (GDP) growth, and GDP per capita. Each indicator is evaluated according to its own distinct assessment. Ultimately, the sub-assessments are integrated into the calculation of a supplier’s overall risk score. Hereby, it…
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Taxonomy
TopicsFood Safety and Hygiene · Supply Chain Resilience and Risk Management · Food Supply Chain Traceability
