Level of automation (LoA) is increasingly recognized as an important principle in improving manufacturing strategies. However, many automation decisions are made without formally assessing LoA and can be made based on a host of organizational factors, like varied mental models used by managers in decision-making. In this study, respondents (N = 186) were asked to watch five different assembly tasks being completed in an automotive manufacturing environment, and then identify “how automated” or “how manual” they perceived the task to be. Responses were given using a visual analogue scale (VAS) and sliding scale, where possible responses ranged from 0 (totally manual) to 100 (totally automated). The activity explored how and when individuals recognized the automated technologies being employed in each task. The tasks of the videos varied primarily by whether the human played active or passive role in the process. Focus group comments collected as a part of the study show how rating patterns revealed functional systems-level thinking and a focus on cognitive automation in manufacturing. While the video ratings generally followed the LoA framework discussed, slight departures in the rating of each video were found.
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Impacts of the Use of Machine Learning on Work Design
The increased pervasiveness of technological advancements in automation makes it urgent to address the question of how work is changing in response. Focusing on applications of machine learning (ML) to automate information tasks, we draw on a simple framework for identifying the impacts of an automated system on a task that suggests 3 patterns for the use of ML—decision support, blended decision making and complete automation. In this paper, we extend this framework by considering how automation of one task might have implications for interdependent tasks and how automation applies to coordination mechanisms.
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- NSF-PAR ID:
- 10301600
- Date Published:
- Journal Name:
- 8th International Conference on Human-Agent Interaction
- Page Range / eLocation ID:
- 163 to 170
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3406499.3415070
