Actionable Cognitive Twins for Decision Making in Manufacturing
  Actionable Cognitive Twins are the next generation Digital Twins enhanced
with cognitive capabilities through a knowledge graph and artificial
intelligence models that provide insights and decision-making options to the
users. The knowledge graph describes the domain-specific knowledge regarding
entities and interrelationships related to a manufacturing setting. It also
contains information on possible decision-making options that can assist
decision-makers, such as planners or logisticians. In this paper, we propose a
knowledge graph modeling approach to construct actionable cognitive twins for
capturing specific knowledge related to demand forecasting and production
planning in a manufacturing plant. The knowledge graph provides semantic
descriptions and contextualization of the production lines and processes,
including data identification and simulation or artificial intelligence
algorithms and forecasts used to support them. Such semantics provide ground
for inferencing, relating different knowledge types: creative, deductive,
definitional, and inductive. To develop the knowledge graph models for
describing the use case completely, systems thinking approach is proposed to
design and verify the ontology, develop a knowledge graph and build an
actionable cognitive twin. Finally, we evaluate our approach in two use cases
developed for a European original equipment manufacturer related to the
automotive industry as part of the European Horizon 2020 project FACTLOG.
