The Haniel subsidiary XXL Horeca, together with Mergerspot, was able to set up a fully automated market analysis in real time and thus increase competition observation by 200%, which would never have been possible manually due to the fast pace and mass of data.

At Evonik, innoSEP has been able to improve the accuracy of weekly predictive order planning for silanes using artificial intelligence (AI) in a pilot project from 60 to 85 percent, so that customer needs can be better served in the future and production surpluses avoided.

As part of the Ruhr Data Hub, Evonik has now developed an AI together with the Berlin startup Industrial Analytics, which, based on the previous test results, is developing new and individual formulas for different needs. The expectation can be made faster and better.

Startup Accelogress has developed a platform for analyzing the production and use of energy, which on the one hand helps municipal utilities to optimize their network planning and, on the other hand, Klimaviertel residents to live CO2-neutral. The CO2 emissions of the houses can be reduced by another 25 percent and climate neutrality increased to 75 percent. For Herne, the project is the entry into a smart city concept.

Thanks to adiutaByte's data-based route planning, Herne disposal can reduce the number of kilometers spent on bulky waste collection by up to 30 per cent and cut citizens' waiting times by two-thirds.

Early recognition of buildings in need of maintenance and modernization leads to optimization in the planning of contracts for construction workers and communication with tenants.

Optimization of trading activities by reliable forecasting of the natural gas price for the day ahead trading leads to increased efficiency in storage operation, balancing group management and portfolio management.

Solution enables early recognition and prediction of tenants who are likely to terminate their tenancy agreement. This means that cancellations can be averted by follow-up offers, which reduces the loss of rental income. In addition, resources of tenant changes are planned more efficiently.

Reduce costs by decreasing the number of control flights required and automating anomaly detection. Greater reliability in identifying deviations minimizes the risk of daybreaks - increased efficiency.

Automated early detection of faulty sensor data and semi-automated correction recommendations lead to a 50% reduction in manual effort for data correction.