Graph Databases for Group Decision Making in Industry: A Comprehensive Literature Review

Virtual manufacturing, simulation, and optimization provide a wealth of knowledge about the possibilities of future production systems to support decision makers. However, this knowledge usually remains with a handful of domain experts, is hard to share even within the same team and cannot be easily used to support further optimization activities. At the same time, simulations can benefit from the incorporation of linked data from real factories once a process is running, to improve the accuracy of the simulation and hence the accuracy of optimization results. Graph databases provide a possible approach to storing and managing this form of interrelated heterogeneous data, with powerful querying capabilities that can identify important or interesting patterns that might otherwise remain hidden. This paper provides a structured literature review of the current directions of research within this broad but interconnected set of topics, with a special focus on how graphs can support finding knowledge within Virtual Factories, used by larger teams for industrial planning and optimization. Papers have been selected from a wide number of research databases and reviewed from the perspectives of the industries they focus on, the technologies they focus on and the approach they have taken. We conclude that current research focuses on one or two aspects of this problem but does not address all at once, despite the potential benefits of the combination.