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User-Friendly MES Interfaces: Recommendations for an AI-Based Chatbot Assistance in Industry 4.0 Shop Floors

Permanent link
https://hdl.handle.net/10037/20748
DOI
https://doi.org/10.1007/978-3-030-42058-1_16
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Date
2020-03-04
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Author
Mantravadi, Soujanya; Jansson, Andreas Dyrøy; Møller, Charles
Abstract
The purpose of this paper is to study an Industry 4.0 scenario of ‘technical assistance’ and use manufacturing execution systems (MES) to address the need for easy information extraction on the shop floor. We identify specific requirements for a user-friendly MES interface to develop (and test) an approach for technical assistance and introduce a chatbot with a prediction system as an interface layer for MES. The chatbot is aimed at production coordination by assisting the shop floor workforce and learn from their inputs, thus acting as an intelligent assistant. We programmed a prototype chatbot as a proof of concept, where the new interface layer provided live updates related to production in natural language and added predictive power to MES. The results indicate that the chatbot interface for MES is beneficial to the shop floor workforce and provides easy information extraction, compared to the traditional search techniques. The paper contributes to the manufacturing information systems field and demonstrates a human-AI collaboration system in a factory. In particular, this paper recommends the manner in which MES based technical assistance systems can be developed for the purpose of easy information retrieval.
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Det står preprint på artikkelen, men medforfatter har sjekket og fått bekreftet at den er fagfellevurdert../skm, 26.3.2021
Publisher
Springer Nature
Citation
Mantravadi, S.: Jansson, A.D.; Møller, C. (2020) User-Friendly MES Interfaces: Recommendations for an AI-Based Chatbot Assistance in Industry 4.0 Shop Floors. Lecture Notes in Computer Science (LNCS),12034
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  • Artikler, rapporter og annet (datateknologi og beregningsorienterte ingeniørfag) [171]
Copyright 2020 Springer Nature

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