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Paper Type
short
Paper Number
2239
Description
Recent advances in Industry 4.0 could help the manufacturing industry to overcome increasing global competition by automating redundant processes such as the handling of errors. In this paper, we present the development of an escalation management system applying the action design research method. Considering additional business data sources, we design a resulting big-data IT artifact with an integrated analytics engine. To this end, we cooperate with a German manufacturing company to roll-out the system. We show its value as the system significantly reduces the duration of escalations. Subsequently, we formalize our learning in the context of related research and highlight the importance of integrated and automated analytics, especially relating to additional business data sources. Considering additional business data sources, we provide an overview of future research activities such as the extended integration of machine learning methods.
Recommended Citation
Oberdorf, Felix; Stein, Nikolai; Walk, Nicolas; Griebel, Matthias; and Flath, Christoph, "ADR for Big-Data IT Artifact Development: An Escalation Management Example" (2020). ICIS 2020 Proceedings. 15.
https://aisel.aisnet.org/icis2020/is_workplace_fow/is_workplace_fow/15
ADR for Big-Data IT Artifact Development: An Escalation Management Example
Recent advances in Industry 4.0 could help the manufacturing industry to overcome increasing global competition by automating redundant processes such as the handling of errors. In this paper, we present the development of an escalation management system applying the action design research method. Considering additional business data sources, we design a resulting big-data IT artifact with an integrated analytics engine. To this end, we cooperate with a German manufacturing company to roll-out the system. We show its value as the system significantly reduces the duration of escalations. Subsequently, we formalize our learning in the context of related research and highlight the importance of integrated and automated analytics, especially relating to additional business data sources. Considering additional business data sources, we provide an overview of future research activities such as the extended integration of machine learning methods.
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