A Few Selected Cases
Success Case Supply Chains Pharmaceutical Wholesaler
Success Case Predictive Maintenance Buildings
Success Case Predictive Maintenance Bio Fuel
Success Case Predictive Maintenance Gas Turbine
Predictive Maintenance (PdM) has developed to a key success factor for operating plants and production processes alike. PdM is a key in reducing costs through avoiding production downtimes. Beyond, it is not only possible to avoid downtimes, but maintenance can be planned with more foresight and it lays out a foundation to quickly implement smoother machine operation. A higher process quality translates directly into a measurable success.
The data collected from industrial processes reflect the behavior of all involved components and plant parts. By applying machine learning and artificial intelligence to this data, a mathematical reflection of the actual processes is created. This digital clone is capable of reveal steps to improve operations.
Warehouses play a central role within supply chains and often form a network of closely interacting exchange points. Stocks can have a limited shelf live or even be part of a higher-level material cycle. In order to find the most cost-effective stock level across an entire network, millions of demands must be predicted with spatial resolution. Countless additional restrictions have to be met too. A combination of classical optimization methods and artificial intelligence identifies our best options.
More and more goods are transported from one location to another. Be it by truck, train or plane. Depending on the mode of transportation, different challenges arise. The best possible pricing while maintaining a high degree of flexibility can bring great advantages in exceedingly competitive markets. Where real-time pricing is needed, an agreement between transport company and shipper must be reached within fractions of a second and a task only achievable through AI.
Increasing decentralization is one of the major challenges for building, operating and maintaining infrastructure. Consumers and producers of energy are closely intertwined in networks. To supply the cities of the future with energy reliably and in a resource-saving manner, power grids and buildings must become more intelligent. Mathematical methods – such as artificial intelligence – help to solve these problems. These build upon data generated by electricity meters, lighting, air conditioning and heating systems, as well as combined heat and power plants and transformer stations.