Optimizing complicated processes in the food industry with the help of AI

Artificial intelligence can be used in production control and planning to optimize and automate the manufacturing process. This can involve various types of AI technologies, such as machine learning or predictive analytics, which automate and optimize complex decisions.

In the food sector, planning and production processes in particular are characterized by high fluctuations in supply and offtake, as well as by varying qualities of natural products.

Here, AI systems can help plan production processes by making predictions about demand and the need for materials and resources. This can help avoid bottlenecks in production and prevent overproduction or overstocking. In the current project REIF it is shown that the reduction of food waste can be optimized with the help of AI in the entire production process – from cultivation/breeding to products in the grocery store. In this area, AI applications can generate surprising planning proposals that will guarantee significantly better utilization of all resources.

Another use case is the prediction of bottlenecks or disruptions in the production process itself. Here, machine learning algorithms can be used to analyze historical data and make predictions about future events. Based on these predictions, appropriate measures can then be taken to avoid bottlenecks or respond quickly to disruptions. For example, ML models can help increase production efficiency by predicting machine downtime and scheduling maintenance to minimize production downtime.