Towards collective intelligence system architectures for supporting multi-disciplinary engineering of cyber-physical production systems

The engineering process of Cyber-Physical Production Systems (CPPS) involves collaboration of multiple engineering disciplines. Major obstacles arising from these multi-disciplinary engineering processes are heterogeneous representations, weak accumulation and integration of dispersed, local engineering knowledge, and required effective coordination between multi-disciplinary engineering teams across the organization. Further, heterogeneous communication channels lead to increased information sharing effort for individual team members, ill-structured knowledge representation and management, and poor discoverability of business-critical know-how. These challenges can be addressed by Collective Intelligence Systems (CIS) that enhance engineering methods and tools in large, multi-disciplinary projects. CIS help to identify important implicit, hard-to-access dispersed information and engineering knowledge, make it explicit, and promote the awareness and efficient management of this business-critical knowledge. Therefore, this paper presents a research agenda focusing on the systematic and empirically-grounded investigation of needs, basic concepts, principles, and models of CIS software architectures in particular application domains, and outlines expected results.