A little bit of history...
The German government pioneered with the start of tearing down the state monopoly for telephony in Germany in 1998. At that time the only company that owned telephony infrastructure in the country was the formerly state owned company Deutsche Telekom (DT). With a total ownership of infrastructure it would have been impossible for other operators to enter the market if not the German government had forced DT to lease transmission to new operators based on state defined price lists.

Today Germany is one of the countries with the most competitive market for telephony in the world. The increasing competition and higher data volumes attracted new transmission providers to the market place. Transmission providers entered with more capacity in the high traffic areas such as the bigger cities and connectivity between them. This led to price drops for transmission in such areas leaving the price levels for low traffic areas, such as the country side, mainly unchanged.

Due to the harder competition the price lists from the government for DT had to be adjusted to maintain DT’s competitive edge as transmission provider.

The Nillsoft idea
Our idea was that even a small price change in any part of an environment causes the most cost effective solution to have a different shape.

Our algorithm was designed to find the lowest total network cost by bringing together price lists, current network capacity and configuration costs which resulted in operative savings summing up to millions of euros per year for Tele2 in Germany.

Not only for the telecom industry
Similar problem statements can be formulated in many other areas such as planning, packaging, resource utilization and logistics. Artificial Intelligence provides the fundamental theories used to find such unnecessary waste.