Data in context: How digital transformation can support human reasoning in cyber-physical production systems

TL;DR

This paper explores how digital transformation can enhance human reasoning in cyber-physical production systems by supporting contextualization through modeling, data integration, and historical data connection.

Contribution

It reviews psychological insights and derives functional requirements for digital technologies to improve contextual reasoning in industrial settings.

Findings

Identification of key cognitive activities for contextualization

Presentation of technologies supporting data integration and modeling

Discussion of design challenges for human-technology cooperation

Abstract

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.