Supplier Risk Assessment—A Quantitative Tool for the Identification of Reliable Suppliers to Enhance Food Safety Across the Supply Chain

Foods, Journal Year: 2025, Volume and Issue: 14(8), P. 1437 - 1437

Published: April 21, 2025

Food safety is a global issue that can be enhanced by collaboration with reliable suppliers. Given the complexities of international supply chains, identifying suppliers often challenging and resource-intensive. Integrating artificial intelligence (AI) offers valuable opportunity to improve efficiency in this process. The aim present study was develop quantitative supplier assessment scheme for implementation an AI-supported database. framework developed incorporates different indicators, including hazard risk, incident category level, vulnerability commodity, audit performance, logistic performance index, gross domestic product (GDP) growth, GDP per capita. Each indicator evaluated according its own distinct assessment. Ultimately, sub-assessments are integrated into calculation supplier’s overall risk score. Hereby, it possible set individual weightings each indicator. Manual testing using exemplary selected yielded promising results, indicating next steps involve It concluded such effective method identification A future challenge will establish incentives make data freely available, as these restricted cannot considered

Language: Английский

Optimisation for Sustainable Supply Chain of Aviation Fuel, Green Diesel, and Gasoline from Microalgae Cultivated in Sugarcane Vinasse

Processes, Journal Year: 2025, Volume and Issue: 13(5), P. 1326 - 1326

Published: April 26, 2025

The development of new technologies for the production renewable energy is fundamental to reducing greenhouse gas emissions. Therefore, search generation methods that are environmentally responsible, socially rational, and economically viable gaining momentum in order mitigate carbon footprint. aviation sector responsible a significant fraction emissions; this reason, decarbonisation must be investigated using biorefinery models. This study presents mixed-integer linear programming (MILP) model optimising design configuration supply chain different states Brazil sustainable fuel (SAF) green diesel gasoline, microalgae cultivated sugarcane vinasse as raw material. technology hydrothermal liquefaction was assessed terms its capacity convert without need energy-intensive drying step. MILP developed LINGO v.20 software library physical economic process We consider selection processes based on object total minimum cost, with optimal plant scaling regional design, including an assessment resources final product distribution. A case implemented Brazil, considering regions country local demands fuels. São Paulo most profitable state, cash flow 1071.09 IRR 36.19%, far outperforming rest. Transport emissions alone represent between 0.6 8.6% generated by model. costs materials, mainly hydrogen (57%) electricity (27%) main evaluated cost (MUS$/TJ biofuel) range 0.009–0.011. Finally, changes have greatest impact

Language: Английский