AI can optimize processes: Use cases and examples

Business and production processes can be optimized to an unprecedented degree with the help of AI. Unfortunately, improving processes using AI is often still associated with abstract ideas:

In discussions with new customers, we often notice that the practical deployment options, possible AI scenarios and the actual optimization potential of AI-based solution finding are not fully clear.

Here we give you insights into our work and some of the applications that we conceptualize, develop and put into production at CompanyMind.

In production processes in food industry, a popular application scenario is visual quality inspection and assurance with the help of an AI system. Here, camera-based systems supplement humans as quality inspectors or take over this task completely. Our products ArtificialVet®, HookTracing® as well as MeatVision and BoxInspector® are based on this technology.

They all optimize processes to reduce errors, objectify and standardize results, and significantly increase the speed of the inspection and assurance process. Our product LivestockGuardian supplements visual inspection with sensory inspection components, enabling optimized anomaly detection in barn processes.

When optimizing demand, capacity and production planning, AI applications can generate novel planning proposals that guarantee significantly better utilization of all resources.

If AI is used to optimize production planning and control, knowledge previously tied to individuals can flow into the AI system, making the knowledge more widely and quickly usable.

One of our current research projects shows 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 (research). Another project proves that optimized control of process and building cooling in German food production is also possible using machine learning:

Our AI-based solution, once productized, will contribute to climate change adaptation by realizing the reduction of energy demand in the context of process and building cooling – both through intelligent control systems and due to the higher share of renewable energy.

Sales planning is a popular application area for applied AI: the use of machine learning enables planning that automatically and continuously adapts to market changes, integrates cannibalization effects, and recognizes product life cycles. This promises a higher forecasting quality than classical statistical forecasting models.

Predictive maintenance optimizes the life of a given production machine so that the machine can be used to its maximum potential before it fails on a previously known date, thanks to the time span calculated by artificial intelligence.

This means that more can be produced because there are fewer unplanned shutdowns. Production costs are also lower in this scenario because there are no fatal failures and they can be planned. Specifically, this means that your company can reduce costs by performing maintenance AI-based.