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Paper Number
2404
Paper Type
Completed
Description
The digitization of the manufacturing domain results in an enormous amount of available data decision-makers are continuously exposed to. Consequently, critical alarm information must be easily comprehensible and suit decision-makers’ preferences to facilitate timely reactions and thus prevent harm to production processes and employees. However, despite its importance, academia and practice show little consensus regarding alarm design in manufacturing dashboards. Against this background, the purpose of our study is twofold. First, we identified 5 factors for visual alarm design with overall 12 design options in a structured literature review. Second, we investigated the effect these design options have on users’ comprehension and preferences in a conjoint study and a best-worst scaling approach with 98 participants with experience working in manufacturing. Our results show that alarm description and their visual integration are the most important factors for alarm design. In this regard, a cluster analysis reveals more nuanced and more stable preferences in more experienced users. Further, we find that color-coding-based content classification entails best performance in recognizing alarms. We contribute to academia and practice by providing actionable insights that may support improving alarm design in manufacturing.
Recommended Citation
Stahmann, Philip; Gravemeier, Laura Sophie; Dittmer, Anke; Janiesch, Christian; and Thomas, Oliver, "User-Centered Visual Design of Alarms in Manufacturing Dashboards: Insights on Comprehensibility and Preferences" (2023). ICIS 2023 Proceedings. 4.
https://aisel.aisnet.org/icis2023/hti/hti/4
User-Centered Visual Design of Alarms in Manufacturing Dashboards: Insights on Comprehensibility and Preferences
The digitization of the manufacturing domain results in an enormous amount of available data decision-makers are continuously exposed to. Consequently, critical alarm information must be easily comprehensible and suit decision-makers’ preferences to facilitate timely reactions and thus prevent harm to production processes and employees. However, despite its importance, academia and practice show little consensus regarding alarm design in manufacturing dashboards. Against this background, the purpose of our study is twofold. First, we identified 5 factors for visual alarm design with overall 12 design options in a structured literature review. Second, we investigated the effect these design options have on users’ comprehension and preferences in a conjoint study and a best-worst scaling approach with 98 participants with experience working in manufacturing. Our results show that alarm description and their visual integration are the most important factors for alarm design. In this regard, a cluster analysis reveals more nuanced and more stable preferences in more experienced users. Further, we find that color-coding-based content classification entails best performance in recognizing alarms. We contribute to academia and practice by providing actionable insights that may support improving alarm design in manufacturing.
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