Smart algorithms in production planning

Flexible planning for the store floor

Together with Industrie Informatik GmbH, RISC Software GmbH has developed a comprehensive planning solution for production planning. The result was a holistic solution, from self-adaptive planning and automatic configuration to the seamless integration of predictive models. This approach enables optimum results to be achieved under a wide range of conditions thanks to individual production processes.

The customer: Industrie Informatik GmbH

As an MES provider, Industrie Informatik GmbH has been supporting manufacturing companies in optimizing their production processes since 1991. Depending on the type of production, a wide range of framework conditions and specifications as well as different problem sizes must be taken into account. The flexible configuration of the MES software allows companies’ individual processes to be mapped precisely. Automatic production planning can then be initiated and adopted on this basis.

The challenge: flexible algorithms for optimizing production planning

The large number of complex framework conditions in connection with the problem variables relevant to practice represents a considerable challenge. To ensure optimal planning for each customer, it must automatically adapt to the customer’s situation. As every company pursues different goals, it is important to coordinate these in order to create a coherent overall plan. Manual user specifications must also be included in the planning.

The solution: modern algorithms, automatic configuration and integration of forecasting models

The project resulted in a holistic solution in which modern scientific methods, including AI and reinforcement learning approaches, were specifically expanded in order to make them usable for practical problems. The complex configuration and adaptation to the respective manufacturing company is considerably simplified by automated optimization of the planning parameters (hyperparameter optimization). In addition, predictive models can be integrated directly into the planning process – i.e. forecasting models that are continuously trained from the real data generated on an ongoing basis.

The customer benefit: better solutions for customers

The developed solution takes the possibilities in planning to the next level with the following features:

• Consideration of various framework conditions
• Optimal solution of problems of practical relevance
• Automatic adaptation to the respective planning situation
• Integration of forecast models into planning
• Automatic configuration through automatic optimization of the planning parameters (hyperparameter optimization)

Additional features can be easily integrated using defined interfaces.