Intelligent workforce planning ensures networking of all influencing factors relevant for workforce planning in a specific case. The prerequisite is the digital availability of the relevant factors. With the help of a combination ("integration") of the factors, transparency is first created about the available variables in order to then realize the best possible and up-to-date deployment of employees on a daily basis using mathematical methods, e.g. forecasting or optimization, independent of time and location. At the same time, the analytics approach enables the automation of a wide range of processes such as task distribution and corrections in the event of absences and the billing of work actually performed according to individual factors such as salaries, holiday regulations and more.

Route optimization is an elementary component of logistics. Plan faster and better, use the fleet more efficiently and produce fewer CO2 emissions: Which carrier delivers to which area? Which is the shortest of the possible delivery routes? How much merchandise can a messenger deliver in one working day? These questions point to one of the most mathematically difficult types of problems: Just visiting 20 delivery points, including consideration of other factors such as vehicle types with varying amounts of space, working and opening hours, traffic conditions, possible stops for reloading, yields more possibilities than a human can calculate through in a lifetime of work. AI-powered computer-calculated mathematical optimization helps to make complex decisions and make purposeful trade-offs. To build an adequate and efficient route optimization, the quality and timeliness of the data in each use case is essential. Understanding the data situation as well as the logistical processes and procedures on site, in the shipping department and at the customer's site have top priority. Our optimization approach is based on the use of mathematical algorithms for the inclusion of individual and industry-specific requirements, thus ensuring the networking and condensation of all truly relevant data in the planning context.

"The right objects must be available to customers in the right quantity at the right time with the right information at the right cost in the right place with the right quality." - this guiding principle shows the continuous pressure to make decisions between sometimes conflicting framework parameters. Only through the transparent, quality-assured, comprehensible provision of all information in a central location can decision-making be fact-based. Via technologies such as data integration and a central data pool, the first goal is achieved: centralizing the information and putting it into a form that is suitable for decision-making in the first place. Initially, this may continue to be done manually, but this lays the foundation for further intelligent support: Using mathematical methods, e.g., the automatic determination of dependencies, forecasts and optimization, decisions can be made faster and better.