Towards a domain-specific information architecture enabling the investigation and optimization of flexible production systems by utilizing artificial intelligence

The industrial domain is undergoing a major transformation, pushed forward by emerging technologies originating from research or industry. The resulting trend, better known by the term Industry 4.0, advances automation within these manufacturing companies by providing ubiquitous interconnection. This enables the integration of technologies mainly used in the Industrial Internet of Things (IIoT), Cyber-physical Systems (CPS) or Big Data with the goal to optimize production processes or facilitate intelligent decision-making. While those processes are progressively supported by methodologies coming from the area of artificial intelligence (AI) like machine learning algorithms, sustainable and consistent storing of production data becomes increasingly important. Concluding, production systems engineering and information engineering are correlating with each other, as the respective results could be used to the advantage of the respective other discipline. In order to address these issues while developing such flexible production systems, the Reference Architecture Model Industrie 4.0 (RAMI 4.0) has been introduced. However, practical applications are lacking as this standard is mainly described in theory, which makes it difficult to actually apply this framework. Thus, the main goal of this paper is to specify a detailed architecture description of the Information Layer to ensure the practical application of RAMI 4.0, which allows stakeholders to utilize model-based Systems Engineering (MBSE) for developing data aspects of industrial systems on the one hand and enable Information Engineering on the other hand. Supported by the concept of the Zachman Framework, the resulting architecture is applied and validated with the help of a real-world case study.