Data in context: How digital transformation can support human reasoning in cyber-physical production systems
This work tackles the problem of insufficient contextualization in cyber-physical production systems for production plant operators, but it is incremental as it reviews existing literature and technologies without introducing new methods.
The paper addresses the lack of context in traditional production plants by reviewing psychological literature to derive functional requirements for digital transformation technologies, which can model system relations, integrate data, and connect present with historical data to support human reasoning.
In traditional production plants, current technologies do not provide sufficient context to support information integration and interpretation. Digital transformation technologies have the potential to support contextualization, but it is unclear how this can be achieved. The present article reviews psychological literature in four areas relevant to contextualization: information sampling, integration, categorization, and causal reasoning. Characteristic biases and limitations of human information processing are discussed. Based on this literature, we derive functional requirements for digital transformation technologies, focusing on the cognitive activities they should support. We then present a selection of technologies that have the potential to foster contextualization. These technologies enable the modelling of system relations, the integration of data from different sources, and the connection of the present situation with historical data. We illustrate how these technologies can support contextual reasoning and highlight challenges that should be addressed when designing human-technology cooperation in cyber-physical production systems.