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Real-time Analytics through Industrial Internet of Things: Lessons Learned from Data-driven Industry
University West, Department of Engineering Science, Division of Production Systems. (PTW iAIL LINA)ORCID iD: 0000-0003-0086-9067
University West, School of Business, Economics and IT, Divison of Informatics. (LINA iAIL)ORCID iD: 0000-0002-6101-3054
Reykjavik University, School of Computer Science (ISL).ORCID iD: 0000-0002-4563-0001
2021 (English)In: Digital Innovation and Entrepreneurship (Amcis 2021), Association for Information Systems, 2021, article id 172685Conference paper, Published paper (Refereed)
Abstract [en]

Industrial Internet of Things (IIoT) and the increasing role of real-time analytics (RTA) data are currently transforming industry and shop floor work. Manufacturing industry needs to adapt accordingly and implement systems solutions for rich data analysis to achieve increased business value. However, a data-driven implementation of RTA applications, often launched as “Plug&Play” solutions, often lacks both insights into shop floor work and the alignment to user perspectives. This paper focuses both on the technical implementation and the deployment of RTA applications from a design-in-use perspective and therefore we argue for congruence between a data-driven and a user-driven approach. The main findings reveal how configuration and implementation of RTA applications interplay with users’ work operations that further extends current IIoT layered models by aligning architectural levels with user and business levels. The main contribution is presented as lessons learned to inform sustainable and innovative implementation for increased business value for data-driven industry.

Place, publisher, year, edition, pages
Association for Information Systems, 2021. article id 172685
Keywords [en]
Industrial internet of things; IIoT; Layered-modular architecture; Real time analytics; Data-driven industry
National Category
Information Systems, Social aspects
Research subject
Work Integrated Learning; Production Technology
Identifiers
URN: urn:nbn:se:hv:diva-17116ISI: 000672599802015Scopus ID: 2-s2.0-85118641335ISBN: 9781733632584 (electronic)OAI: oai:DiVA.org:hv-17116DiVA, id: diva2:1622133
Conference
27th Annual Americas Conference on Information Systems (AMCIS), ELECTR NETWORK, AUG 09-13, 2021
Note

The study was carried out within the AHIL-project, Artificial and Human Intelligence through Learning, funded by the Swedish Knowledge Foundation and University West

Available from: 2021-12-21 Created: 2021-12-21 Last updated: 2023-06-02Bibliographically approved

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fulltext(312 kB)521 downloads
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Hattinger, MonikaLundh Snis, UlrikaIslind, Anna Sigridur

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