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 presents a selection of the psychological literature in four areas relevant to contextualization: information sampling, information 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–machine cooperation in cyber-physical production systems.
Highlights
One reason for this is that current technologies do not sufficiently support their implementation [10]. This is expected to change in cyber-physical production systems (CPPS)
We ask how contextualization can improve human–machine cooperation: How can digital transformation technologies support the context-dependent selection and integration of data from different sources, and thereby support categorization and causal reasoning? We focus on supervisory control tasks that require operators to monitor highly automated processes and intervene if necessary [16]
Contextualization is a major challenge in the process industries and discrete processing industries
Summary
In the process industries and discrete processing industries, operators’ process monitoring and process control activities can be characterized as problem solving [1,2,3]. It is possible to derive sufficient information from only a few indicators This requires operators to know which ones to check in what situations, because the importance of data depends on the current production context. The Human Factors literature has suggested interface concepts to address some of the challenges, e.g., [6,7,8,9], these concepts cannot solve the contextualization problem in real plants. One reason for this is that current technologies do not sufficiently support their implementation [10]. This is expected to change in cyber-physical production systems (CPPS)
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