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This paper investigates new roles of stakeholders in the context of the migration of circular value creation based on a collaborative model of logistic systems in an industrial environment. The evolving nature of supply networks and the underlying information systems as well as advances in technology drive the application of artificial intelligence (AI) in this context. The paper thus aims to harmonize and optimize the combination of classical methods with those of AI in hybrid planning and development systems. For this purpose, the connection of the design of the roles with the logistic processes in terms of a unified modeling is established. The design of the roles enables the generation of smart solutions in logistics. Aspects of data acquisition, data preparation, data storage, data analysis and data quality are considered to make qualified statements on the maturity level of AI support for logistics systems.

Supply chain management consists of complex processes. They enlarge permanent the basis of data and information. Moreover there exists complex knowledge of individuals and within the organization. Both information and knowledge must be saved and processed. For the realization methods of the knowledge management are suitable. In this paper a global Supply chain in the automotive industry will be analyzed under the aspect of the knowledge processing. Theoretical and practical fundamentals of the integration of knowledge-based components are represented. The first steps of developing a knowledge base and the consequences for Enterprise Information Systems are introduced.

Supply Chain is determined as a network of corporation of several organisations achieving a contribution to several activities in performance processes for the increase in value. It is a mighty method for improving the efficiency in the Automotive Industry. But there are complex problems only solvable by integration of the Knowledge Management and its special tools. The application of special concepts and tools is presented by two examples.

Total quality is becoming increasingly important for competitiveness. In order to achieve high quality, the requirements must be continuously compared with the results achieved in the process. This is done by means of measurement parameters and comparative values. The acquisition of the data requires appropriate measurement methods. The measurement methods and procedures have to be constantly developed in order to measure more precisely and to generate an even higher quality. Thus, the achieved product quality can be determined absolutely and relatively. If deviations from the planned quality parameters occur, the operator will be able to intervene immediately. The presented procedure is one of the noncontact (optical) measurement methods using CMMs, 3D scanners and 3D cameras. It is a combination of stereo photography and photogrammetry.

As a result of new challenges such as flexible, adaptable manufacturing systems and customized products, the complexity of new technologies for digital transformation and Industry 4.0 [36] is increasing. In this context, logistics and additive manufacturing are seen as two out of nine future technologies for modern production industries. For future developments it is crucial to find effective and flexible approaches for the connection of heterogeneous systems and, above all, data structures. A promising approach is concerned with the interoperable connection of these complex structures. The first approach to be described in the paper deals with the smart interoperable connection of systems in the domain of logistics.