Cognitive Challenges of Changeability: Multi‐Level Flexibility for Operating a Modular Chemical Plant

Müller, Romy; Urbas, Leon

doi:10.1002/cite.201700029

Cited by 5 publications

(4 citation statements)

References 90 publications

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“…It also must be considered that the introduction of cyber-physical production systems (CPPS) will pose its own challenges for operator work (Hirsch-Kreinsen, 2014;Müller, 2019;Müller & Urbas, 2017). One exemplary challenge is that the requirement of being 'in control' becomes more abstract (Woods & Branlat, 2010).…”

Section: Psychological Literature Does Not Always Adequately Address ...mentioning

confidence: 99%

“…This focus differs from a large body of Human Factors literature, which argues that technological innovation creates new problems for humans. For instance, the psychological literature on automation effects has engaged in the latter reasoning for decades (e.g., Parasuraman & Manzey, 2010;Parasuraman & Riley, 1997;Parasuraman & Wickens, 2008), and recently this work has been extended to novel digital transformation technologies (e.g., Endsley, 2017;Müller, 2019;Müller & Urbas, 2017;Woods & Branlat, 2010). Although this perspective has revealed many important insights, the present article takes the opposite approach, considering technology as an enabler rather than a problem source, and asking how to use it in a way that benefits human cognitive activities.…”

Section: Introductionmentioning

confidence: 99%

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Data in Context: How Digital Transformation Can Support Human Reasoning in Cyber-Physical Production Systems

Müller

Kessler

Humphrey³

et al. 2021

Future Internet

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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.

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Section: Psychological Literature Does Not Always Adequately Address ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data in Context: How Digital Transformation Can Support Human Reasoning in Cyber-Physical Production Systems

Müller

Kessler

Humphrey³

et al. 2021

Future Internet

Self Cite

View full text Add to dashboard Cite

show abstract

“…In industrial contexts, the technical innovation of modularity introduces this issue into the daily work of operators, as modular plants enable a flexible reconfiguration of their physical setup [ 7 ]. Accordingly, operators can choose between local and global changes [ 8 ]. They can either modify process parameters like temperature or pressure within the narrow ranges of the current module (Adapt) or reconfigure the plant and use another module that enables production at more suitable parameter ranges, for instance, because it has a bigger reactor (Exchange).…”

Section: Introductionmentioning

confidence: 99%

Adapt/Exchange Decisions Depend on Structural and Surface Features: Effects of Solution Costs and Presentation Format

Müller

2024

Behavioral Sciences

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Problem solvers often need to choose between adapting a current solution and exchanging it for a new one. However, previous studies have not considered how such decisions might depend on structural and surface features of the task. Therefore, the present study investigated the interplay between the costs of the two solutions (a structural feature) and the format in which this information is presented (a surface feature). In a computer-based modular plant scenario, participants chose between modifying process parameters (Adapt) and reconfiguring the module setup (Exchange). The solution costs were presented either as graphs depicting parameter relations, separate numbers for each parameter, or integrated numbers for each solution. It was hypothesised that graphs induce satisficing (i.e., basing decisions only on Adapt), whereas the numeric formats foster a comparison of the solutions (i.e., basing decisions on the Adapt/Exchange ratio). The hypothesised effects were restricted to situations with medium Adapt costs. A second experiment replicated these findings while adjusting the scale of the numeric formats. In conclusion, Adapt/Exchange decisions are shaped by an interaction of structural and surface features of the task. These findings contribute to a more detailed understanding of the influences on decision strategies in complex scenarios that require a balance between stability and flexibility.

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“…In different work domains, the task of operators is to keep processes within operational limits and intervene during faulty behavior (Müller and Oehm 2019;Wang et al 2015). To do so in the flexible environment of cyber-physical production systems, operators need to adjust their decision making to the current production context (Hirsch-Kreinsen 2014; Müller and Urbas 2017). This also requires them to learn about different production contexts and transfer this knowledge between contexts (Müller 2019).…”

Section: Introductionmentioning

confidence: 99%

Motivational assistance system design for industrial production: from motivation theories to design strategies

Dostert

Müller

2020

Cogn Tech Work

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Industrial production is still widely sustained by human operators. However, the design of human-machine interaction often does not foster the motivation to learn more about their machine or system. This may decrease operators' ability to flexibly adjust their decision making and problem-solving skills to the current production context. Motivation to learn could be attained by a motivating socio-technical design of assistance systems, but suitable and context-specific design strategies are lacking. In the present study, a systematic literature review of motivation theories in education, at the workplace, and in system design was carried out. The resulting 16 theories were integrated into a conceptual model of motivating assistance system design in industrial production. In this model, learning motivation results from the satisfaction of the needs for autonomy, competence, and relatedness, which in turn is mediated through the design of the system (including interface, task, and behavior). Moreover, this process is subject to moderating influences from job characteristics, personal variables, and factors concerning the respective work domain. Strategies for motivational design are derived from the model, and an example from the discrete processing industry is used to illustrate how the model could be applied to design assistance systems in this domain. Finally, the procedures for theory selection and model development are discussed, theoretical and practical implications are derived, and alternative strategies of instilling motivation are considered.

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Cognitive Challenges of Changeability: Multi‐Level Flexibility for Operating a Modular Chemical Plant

Cited by 5 publications

References 90 publications

Data in Context: How Digital Transformation Can Support Human Reasoning in Cyber-Physical Production Systems

Data in Context: How Digital Transformation Can Support Human Reasoning in Cyber-Physical Production Systems

Adapt/Exchange Decisions Depend on Structural and Surface Features: Effects of Solution Costs and Presentation Format

Motivational assistance system design for industrial production: from motivation theories to design strategies

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