Managing Complexity

The rise of Information and Communication Technology (ICT) in the second half of the 20^th century became the dominant force in economics. Its rise accelerates in the first 15 years of this century at an astonishing speed. The world of ICT right now is in the process of cosmic inflation. In the early universe, quantum fluctuations in a microscopic inflationary agile region became the seed for growing structures in the universe of galactic nebula, galaxies and stars, making the universe transparent. This phenomenon, familiar to physicist and cosmologists, happens right now to ICT. The current observation is that ``things'' of the physical world become intelligent, receive IP addresses and connect to the Internet. The possibilities to create new ICT-based products seem unlimited; however, sponsors must fuel the inflation. Complexity was already an issue when developing software in the early days of ICT. Software development is often done in projects that turn out to be exploratory in the sense that they aim at translating human voices, uttering requirements, into a machine-readable language. Requirements for the software to be build are usually not known at the beginning; the project must uncover them. Developing software without knowing the outcome in advance is a complex undertaking. Predicting the outcome of software projects by proven methods of civil engineering did not work out well. Now, new levels of complexity arise with ICT. Agile approaches are appropriate for software development; however, predicting the outcome of projects still is difficult. New techniques must manage the growing levels of complexity within ICT. Fortunately, mathematics has provided these new techniques. They rely on transfer functions and Eigenwert theory. Its usefulness already has been proven in major search engines of this century. However, this is not the end of the story. This books makes the mathematics of Lean Six Sigma transfer functions available to ICT practitioners. It provides the basic theory, explained with many examples, and even more suggestions, how Six Sigma Transfer Functions help with complex problems.