The methods and solutions of operaize enable the autonomous Industry 4.0.
Our focus is on the planning, control and optimization of industrial processes. We use AI-based methods that proactively
identify faults, risks or failures and proactively identify suitable problem-solving strategies to avoid these negative effects. In essence, it is a matter of how problem solutions can be determined from data extremely quickly and implemented effectively.
Today's planning and control procedures rely essentially on static MRP technology developed in the 1950s. This leads to suboptimal performance, lack of coordination between production, maintenance, quality control, resource (energy) consumption
and silo Excel solutions, which can only be compensated with very high planning and control efforts.
Our focus is cognitive operations. By this we mean AI-based optimization algorithms for the autonomous control, planning and optimization of production and supply chain processes. Operational industrial processes in production, supply
chain and logistics are subject to a significantly increasing complexity, which can only be managed with great effort using traditional or previously used applications.
The use of the latest, high-performance AI-based optimization methods results in significant performance, production, market and cost advantages for industrial customers. Necessary resources such as energy, plant capacities and human resources
can be saved significantly.
Our solution includes 5 integrated modules:
Linking of data and preparation as well as
provision of expert knowledge and experience.
Analysis of relevant operational data flows with
regard to patterns and predictability.
Autonomous determination of solutions and
decisions for complex problems.
Self-learning deductions of success rules for
planning and control.
Intuitive human-machine interaction with mutual
With this approach, we imitate and learn from human decision-making behavior and optimize decision-making processes
through autonomously determined optimal decisions and prioritizations of process flows.
Cognitive operations combines different methods and tools of artificial intelligence from machine learning and
Our focus is on reasoning methods from the area of semantic AI and the area of systematic search space restrictions, which
we combine with each other. By using semantic AI methods, we are able to autonomously understand the meaning of problems, correlations and prognoses and their effects, and to use them to gain important insights for problem solving.
When using systematic search space restrictions, different mathematical models from the field of Operations
Research are used to determine all possible problem solutions for problems and then to filter out which of the solution options are optimal and meaningful.
Thanks to the latest scientific findings in mathematics, optimal problem-solving strategies can be determined extremely
quickly. By combining the two reasoning methods, even complex, diffuse problems can be solved very efficiently and effectively.
This is the lever for success in order to be able to make decisions autonomously in complex value chains.