Episode 5: How to integrate predictions in your work flows

Predictive analytics, over time, they gain more recognition as at the same time their widespread use, by companies is done systematically. An Industry Innovation Group on Predictive Analytics for Food Integrity from over 26 companies of different types and sizes is mentioning the tremendous value in enhancing the predictive capabilities of food risk assessment and prevention.

Giannis Stoitsis, CTO and partner at Agroknow (Coodinator partner of the BigDataGrapes project), presents the way to integrate predictions into an operation mode, so AI can lead to an impotant chnage in your company. In addition, this video focuses on how we can update and automate predictions, ensuring continuous integration with systems and external data for more accurate decision making.

The discussion about the applications that artificial intelligence could have in the food industry is never-ending. Companies have started paying closer attention as they are interested in how AI can lead to an important change in the way that they are working. Moreover, companies oversee the untapped potential in the way that AI-powered prediction machines can help them anticipate and mitigate food risks.

However, it’s not that easy to integrate predictions into an operation mode. Companies need to rethink the current workflows and adapt to the nature of their-targeted food supply chain.

In order to solve this problem, two components come to pave the way for the integration of predictions into the workflows. Both updated prediction models and automation are analyzed in this video, providing best practices of using them in order to create predictions that can be crucial for decision making.

In our next video we will focus on what these predictions can tell us about the Chocolate industry. Stay tuned for our next video!

These short video series are a part of the BigDataGrapes project. The project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 780751.