As food and beverage companies continuously encounter unforeseen risks and hazards in their products due to global supply chain disruptions, ingredient shortages, the effects of climate change on soils, and the increasing instances of economically motivated adulteration, there is a greater need than ever to embrace new technologies. AI has emerged as one of the most innovative technologies, offering many applications to help companies ensure the safety and quality of their products. One such application is the use of AI-powered forecast models, which provide companies with actionable food risk intelligence, highlighting potential future risks in their supply chain.
AI is no longer an experiment but part of everyday workflow
Many food & beverage companies worldwide are now utilizing AI in daily aspects of their work, including for the identification of emerging risks for their ingredients & products in order to adjust their strategy and enhance food safety management systems, and plan more focused lab tests.
How does AI forecasting work?
To demonstrate the applicability of AI & predictive analytics in preventing food safety incidents we created a practical guide that illustrates:
- how forecasting models are developed
- what factors are considered for the model training
- the deployment of multi-factor forecasting
- the applicability of AI forecasting via past outbreak cases