Multi-Dimensional Production Planning Using a Vertical Data Integration Approach

Due to the continuously and fast increasing complexity of products and production processes, manufacturing companies have to face more and more challenges in order to survive on a competitive market. Thus, in modern planning scenarios of the manufacturing process, the goal is not only to achieve the most efficient low-cost production, but also to take into account the interests of the customer. Especially the increasing impact of the customer on the market leads to rapidly changing boundary conditions and thus different requirements concerning the production process. As a consequence, the production has to be designed more flexible and adaptive to changing circumstances. In order to reach the desired flexibility, the production as well as the communication management within the factory has to be designed on the basis of a modular planning approach. This requires vertical exchange of information through all levels of the company, from the management layers and the Enterprise Resource Planning (ERP) to the automation and shop floor layers, where the aggregated information is needed to optimize the production. The interconnection of these corporate layers can only be achieved through the use of an information model that serves interoperability between these mostly heterogeneous systems. The processing and visualization of ERP data using an integrative information model enables a continuous optimization of the production systematics. Through the information model, cross-linked data structures of the production monitoring and automation can be connected and thus be integrated and consolidated into a consistent data basis. In the current work, such an information model will be introduced and validated by making use of an optimization algorithm that is carried out through the layout planning phase of a factory. The use-case scenario presented aims for serving a flexible and dynamic optimization of the production structure of a manufacturing enterprise. During the optimization, the algorithm takes into account historical data taken from the ERP level of the company as well as time constraints to design multi-dimensional process chains for multiple manufacturing scenarios.