Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
The use of machine learning in digitized production increases potentials for production automation. A milestone on the path to autonomous production is real-time anomaly detection. However, increasing complexity of production makes autonomous decisions difficult to understand for humans as central stakeholders. In this paper, we create a dashboard that incorporates elements from knowledge-based systems, requirements for real-time anomaly detection, and design guidelines for dashboards. Using design science research, the dashboard is designed, implemented and comprehensively evaluated with 98 participants. After the second design science iteration, the dashboard is approved in terms of usefulness and ease of use. Our research primarily contributes to practice, as our implementation constitutes a starting point for designing the interface between humans and autonomous production. We also contribute to academia as the dashboard is an instantiation in the research field of interface design for knowledge-based systems, which can be further developed in future research.
Recommended Citation
Stahmann, Philip, "A Prototypical Dashboard for Knowledge-Based Expert Systems used for Real-Time Anomaly Handling in Smart Manufacturing" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 4.
https://aisel.aisnet.org/hicss-56/os/data_analytics/4
A Prototypical Dashboard for Knowledge-Based Expert Systems used for Real-Time Anomaly Handling in Smart Manufacturing
Online
The use of machine learning in digitized production increases potentials for production automation. A milestone on the path to autonomous production is real-time anomaly detection. However, increasing complexity of production makes autonomous decisions difficult to understand for humans as central stakeholders. In this paper, we create a dashboard that incorporates elements from knowledge-based systems, requirements for real-time anomaly detection, and design guidelines for dashboards. Using design science research, the dashboard is designed, implemented and comprehensively evaluated with 98 participants. After the second design science iteration, the dashboard is approved in terms of usefulness and ease of use. Our research primarily contributes to practice, as our implementation constitutes a starting point for designing the interface between humans and autonomous production. We also contribute to academia as the dashboard is an instantiation in the research field of interface design for knowledge-based systems, which can be further developed in future research.
https://aisel.aisnet.org/hicss-56/os/data_analytics/4