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

  • Soujanya MantravadiEmail author
  • Andreas Dyrøy Jansson
  • Charles Møller
Conference paper
  • 281 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12034)

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.

Keywords

AI applications Manufacturing Chatbot 

Notes

Acknowledgment

This research work is partially funded by the Manufacturing Academy of Denmark. The authors would like to thank the informants from the companies for sharing their knowledge.

References

  1. 1.
    MESA White Paper #01: The Benefits of MES: A Report from the Field. Presented at the (1997)Google Scholar
  2. 2.
    Scholten, B.: The Road to Integration. ISA (2007)Google Scholar
  3. 3.
    Sauer, O.: Information technology for the factory of the future - State of the art and need for action. Procedia CIRP 25, 293–296 (2014).  https://doi.org/10.1016/j.procir.2014.10.041CrossRefGoogle Scholar
  4. 4.
    Hermann, M., Pentek, T., Otto, B.: Design principles for industrie 4.0 scenarios. In: Proceedings of the Annual Hawaii International Conference on System Sciences, March 2016, pp. 3928–3937 (2016)Google Scholar
  5. 5.
    Gorecky, D., Schmitt, M., Loskyll, M., Zühlke, D.: Human-machine-interaction in the industry 4.0 era. In: Proceedings - 2014 12th IEEE International Conference on Industrial Informatics, INDIN 2014, pp. 289–294 (2014)Google Scholar
  6. 6.
    Monostori, L., Váncza, J., Kumara, S.R.T.: Agent-based systems for manufacturing. CIRP Ann. - Manuf. Technol. 55, 697–720 (2006).  https://doi.org/10.1016/j.cirp.2006.10.004CrossRefGoogle Scholar
  7. 7.
    Hatvany, J., Nemes, L.: Intelligent manufacturing systems—a tentative forecast. IFAC Proc. 11, 895–899 (1978).  https://doi.org/10.1016/S1474-6670(17)66031-2CrossRefGoogle Scholar
  8. 8.
    Maedche, A., Morana, S., Schacht, S., Werth, D., Krumeich, J.: Advanced user assistance systems. Bus. Inf. Syst. Eng. 58, 367–370 (2016).  https://doi.org/10.1007/s12599-016-0444-2CrossRefGoogle Scholar
  9. 9.
    Stoeckli, E., Uebernickel, F., Brenner, W.: Exploring affordances of slack integrations and their actualization within enterprises - towards an understanding of how chatbots create value. In: Proceedings of the 51st Hawaii International Conference on Systems Science, pp. 2016–2025 (2018).  https://doi.org/10.24251/hicss.2018.255
  10. 10.
    Kletti, J.: Manufacturing Execution Systems – MES. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-49744-8CrossRefGoogle Scholar
  11. 11.
    Mantravadi, S., Li, C., Møller, C.: Multi-agent manufacturing execution system (MES): concept, architecture & ML algorithm for a smart factory case. In: Scitepress, pp. 477–482 (2019).  https://doi.org/10.5220/0007768904770482
  12. 12.
    ISA101: Human-Machine Interfaces. https://www.isa.org/
  13. 13.
    Albus, J.S.: An intelligent systems architecture for manufacturing. In: International Conference Intelligent Systems: A Semiotic Perspective, pp. 20–23 (1996)Google Scholar
  14. 14.
    Younus, M., Hu, L., Yuqing, F., Yong, C.P.: Manufacturing execution system for a subsidiary of aerospace manufacturing industry. In: Proceedings - 2009 International Conference on Computer and Automation Engineering, ICCAE 2009, pp. 208–212 (2009).  https://doi.org/10.1109/ICCAE.2009.12
  15. 15.
    Meyer von Wolff, R., Hobert, S., Schumann, M.: How may I help you? – State of the art and open research questions for chatbots at the digital workplace. In: Proceedings of the Hawaii International Conference on Systems Science, vol. 6, pp. 95–104 (2019)Google Scholar
  16. 16.
    Autor, D.H., Levy, F., Murnane, R.J.: The skill content of recent technological change: an empirical exploration. Q. J. Econ. 118, 1279–1333 (2003)CrossRefGoogle Scholar
  17. 17.
    Augello, A., Pilato, G., Machi, A., Gaglio, S.: An approach to enhance chatbot semantic power and maintainability: experiences within the FRASI project. In: Proceedings - IEEE 6th International Conference Semantic Computing, ICSC 2012, pp. 186–193 (2012).  https://doi.org/10.1109/ICSC.2012.26
  18. 18.
    Demartini, M., Tonelli, F., Damiani, L., Revetria, R., Cassettari, L.: Digitalization of manufacturing execution systems: the core technology for realizing future smart factories. In: Proceedings of the Summer School Francesco Turco, September 2017, pp. 326–333 (2017)Google Scholar
  19. 19.
    Madsen, O., Møller, C.: The AAU smart production laboratory for teaching and research in emerging digital manufacturing technologies. Procedia Manuf. 9, 106–112 (2017)CrossRefGoogle Scholar
  20. 20.
    Marietto, M., et al.: Artificial Intelligence Markup Language: a brief tutorial. Int. J. Comput. Sci. Eng. Surv. (IJCSES) 4(3), 1–20 (2013)CrossRefGoogle Scholar
  21. 21.
    Bussmann, S., McFarlane, D.: Rationales for holonic manufacturing control. In: Proceedings of the 2nd Intelligent Agent Based Framework for Manufacturing Systems, pp. 1–8 (1999)Google Scholar
  22. 22.
    Wu, Y., Wang, G., Li, W., Li, Z.: Automatic chatbot knowledge acquisition from online forum via rough set and ensemble learning. In: Proceedings - 2008 IFIP International Conference on Network and Parallel Computing, NPC 2008, pp. 242–246 (2008).  https://doi.org/10.1109/NPC.2008.24
  23. 23.
    Karnouskos, S., Baecker, O., De Souza, L.M.S., Spieß, P.: Integration of SOA-ready networked embedded devices in enterprise systems via a cross-layered web service infrastructure. In: IEEE International Conference on Emerging Technologies Factory Automation ETFA, pp. 293–300 (2007)Google Scholar
  24. 24.
    Miranda, J., et al.: From the Internet of Things to the Internet of People. IEEE Internet Comput. 19, 40–47 (2015)CrossRefGoogle Scholar
  25. 25.
    Reshmi, S., Balakrishnan, K.: Implementation of an inquisitive chatbot for database supported knowledge bases. Sadhana - Indian Acad. Sci. 41, 1173–1178 (2016)Google Scholar
  26. 26.
    Carayannopoulos, S.: Using chatbots to aid transition. Int. J. Inf. Learn. Technol. 35, 118–129 (2018)CrossRefGoogle Scholar
  27. 27.
    Lestarini, D., Raflesia, S.P., Surendro, K.: A conceptual framework of engaged digital workplace diffusion. In: Proceeding 2015 9th International Conference on Telecommunication System, Services, and Application, TSSA 2015 (2016)Google Scholar
  28. 28.
    Levy, F.: Computers and populism: artificial intelligence, jobs, and politics in the near term. Oxford Rev. Econ. Policy. 34, 393–417 (2018).  https://doi.org/10.1093/oxrep/gry004CrossRefGoogle Scholar
  29. 29.
  30. 30.
    Kimoto, T., Asakawa, K., Yoda, M., Takeoka, M.: Stock market prediction system with modular neural networks, vol. 1, pp. 1–6 (2002).  https://doi.org/10.1109/ijcnn.1990.137535
  31. 31.
    Sheth, A., et al.: A sensor network based landslide prediction system. In: SenSys, pp. 280–281 (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Materials and ProductionAalborg UniversityAalborgDenmark
  2. 2.Department of Computer Science and Computational EngineeringUiT – Arctic University of NorwayNarvikNorway

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