2025/08/01 by Röhrs, Sina, Rohn, Sascha, Pfeifer, Yvonne +1
#000 Informatik #300 Sozialwissenschaften::330 Wirtschaft::338 Produktion #600 Technik #Informationswissenschaft #Medizin #Systeme::006 Spezielle Computerverfahren #Wissen #allgemeine Werke::000 Informatik #angewandte Wissenschaften::650 Management #artificial intelligence #food safety #supplier risk assessment #Öffentlichkeitsarbeit::658 Allgemeines Management
paper · doi:10.14279/depositonce-23887
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 is possible to set individual weightings for each indicator. Manual testing using an exemplary selected supplier yielded promising results, indicating that the next steps involve implementation into an AI-supported database. It can be concluded that such an assessment framework can be an effective method for the identification of reliable suppliers. A future challenge will be to establish incentives to make audit data freely available, as these are often restricted and cannot be considered in the supplier risk assessment.