Modeling Approach for Situational Event-handling within Production Planning and Control Based on Complex Event Processing

Abstract

Nowadays industrial production environments are complex, volatile, and driven by uncertainties. Manufacturing companies are striving for flexibility and adaptability to cope with these challenges and remain competitive. Market requirements such as shortened product life cycles, increasing number of variants, and customized products lead to complexity in manufacturing systems. Possible approaches to cope with such challenges can be found in the field of ‘Industrie 4.0’. In particular, decision-making and real-time reaction systems are one way to handle the complexity. To cope with this complexity, digitalization like the vision of ‘Industrie 4.0’ can offer different solutions. However, digitalization leads to an increase of the amount of data describing the status of products and resources within an industrial production environment. In order to achieve a near real time monitoring and control of production and logistics processes, intelligent processing and analyzing of the acquired data is necessary. As a result of this development, so called “complex event processing” (CEP) is essential for analyzing extensive data streams in real-time. In order to derive the rules for a CEP engine, an event model has to be described to visualize the relations, constraints and abstraction levels of production processes. The main focus within this paper is a modeling approach for the situational handling of events within production planning and control. The requirements of the modeling method are focused on the use case of a mass production for carbon-fiber-reinforced plastic CFRP components.