Skip to main content
SpringerLink
Log in
Book cover

International Conference on Human-Computer Interaction

HCII 2020: HCI International 2020 – Late Breaking Papers: Interaction, Knowledge and Social Media pp 641–651Cite as

Storytelling with Data in the Context of Industry 4.0: A Power BI-Based Case Study on the Shop Floor

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12427))

Abstract

Industry 4.0 (I4.0) is characterized by cyber physical systems (CFS) and connectivity, paving the way to an end-to-end value chain, using Internet of Things (IoT) platforms supported on a decentralized intelligence in manufacturing processes. In such environments, large amounts of data are produced and there is an urgent need for organizations to take advantage of this data, otherwise its value may be lost. Data needs to be treated to produce consistent and valuable information to support decision-making. In the context of a manufacturing industry, both data analysis and visualization methods can drastically improve understanding of what is being done on the shop floor, enabling easier decision-making, ultimately reducing resources and costs. Visualization and storytelling are powerful ways to take advantage of human visual and cognitive capacities to simplify the business universe. This paper addresses the concept of “Storytelling with Data” and presents an example carried out in the shop floor of a chemical industry company meant to produce a real-time story about the data gathered from one of the manufacturing cells. The result was a streaming dashboard implemented using Microsoft Power BI.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://powerbi.microsoft.com/en-us/blog/microsoft-a-leader-in-gartners-magic-quadrant-for-analytics-and-bi-platforms-for-12-consecutive-years (visited, Jan, 2020).

References

  1. Hill, R., Devitt, J., Anjum, A., Ali, M.: Towards in-transit analytics for industry 4.0. In: Proceedings - IEEE International Conference on Internet Things, IEEE Green Computing and Communications IEEE Cyber, Physical and Social Computing, IEEE Smart Data, pp. 810–817 (2017). https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.124

  2. Arromba, A.R., Teixeira, L., Xambre, A.R.: Information flows improvement in production planning using lean concepts and BPMN an exploratory study in industrial context. In: 14th Iberian Conference on Information Systems and Technologies (CISTI), pp. 206–211 (2019)

    Google Scholar 

  3. Miragliotta, G., Sianesi, A., Convertini, E., Distante, R.: Data driven management in Industry 4.0: a method to measure Data Productivity. IFAC-PapersOnLine 51, 19–24 (2018). https://doi.org/10.1016/j.ifacol.2018.08.228

    Article  Google Scholar 

  4. Chaudhary, P., Hyde, M., Rodger, J.A.: Exploring the benefits of an agile information system. Intell. Inf. Manage. 09, 133–155 (2017). https://doi.org/10.4236/iim.2017.95007

    Article  Google Scholar 

  5. Narayanan, M., Sanil Shanker, K.P.: Data visualization method as the facilitator for business intelligence. Int. J. Eng. Adv. Technol. 8, 2249–8958 (2019). https://doi.org/10.35940/ijeat.f9054.088619

    Article  Google Scholar 

  6. Choi, T.M., Chan, H.K., Yue, X.: Recent development in big data analytics for business operations and risk management. IEEE Trans. Cybern. 47, 81–92 (2017). https://doi.org/10.1109/TCYB.2015.2507599

    Article  Google Scholar 

  7. Pribisalić, M., Jugo, I., Martinčić-Ipšić, S.: Selecting a business intelligence solution that is fit for business requirements. In: 32nd Bled eConference Humanizing Technology for a sustainable Society, pp 443–465 (2019)

    Google Scholar 

  8. Stecyk, A.: Business intelligence systems in SMEs. Eur. J. Serv. Manage. 27, 409–413 (2018)

    Article  Google Scholar 

  9. Chen, S., Li, J., Andrienko, G., et al.: Supporting story synthesis: bridging the gap between visual analytics and storytelling. IEEE Trans. Vis. Comput. Graph. 14, 1077–2626 (2015). https://doi.org/10.1109/TVCG.2018.2889054

    Article  Google Scholar 

  10. Mantravadi, S., Møller, C.: An overview of next-generation manufacturing execution systems: how important is MES for industry 4.0? Procedia Manuf. 30, 588–595 (2019). https://doi.org/10.1016/j.promfg.2019.02.083

    Article  Google Scholar 

  11. Da, X., Xu, E.L., Li, L.: Industry 4.0: state of the art and future trends. Int. J. Prod. Res. 56, 2941–2962 (2018). https://doi.org/10.1080/00207543.2018.1444806

    Article  Google Scholar 

  12. Savastano, M., Amendola, C., Bellini, F., D’Ascenzo, F.: Contextual impacts on industrial processes brought by the digital transformation of manufacturing: a systematic review. Sustainability 11, 891 (2019)

    Article  Google Scholar 

  13. Salierno, G., Cabri, G., Leonardi, L.: Different perspectives of a factory of the future: an overview. In: Proper, H., Stirna, J. (eds.) Advanced Information Systems Engineering Workshops. Lecture Notes in Business Information Processing, pp. 107–119. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20948-3_10

    Chapter  Google Scholar 

  14. Qu, Y., Ming, X., Ni, Y., et al.: An integrated framework of enterprise information systems in smart manufacturing system via business process reengineering. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. (2018). https://doi.org/10.1177/0954405418816846

    Article  Google Scholar 

  15. Tao, F., Zhang, M.: Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access 5, 20418–20427 (2017). https://doi.org/10.1109/ACCESS.2017.2756069

    Article  Google Scholar 

  16. Qi, Q., Tao, F.: Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison. IEEE Access 6, 3585–3593 (2018). https://doi.org/10.1109/ACCESS.2018.2793265

    Article  Google Scholar 

  17. Zhu, Z., Liu, C., Xu, X.: Visualisation of the digital twin data in manufacturing by using augmented reality. Procedia CIRP 81, 898–903 (2019). https://doi.org/10.1016/j.procir.2019.03.223

    Article  Google Scholar 

  18. Schroeder, G., Steinmetz, C., Pereira, C.E., et al.: Visualising the digital twin using web services and augmented reality. In: IEEE International Conference on Industrial Informatics, pp. 522–527 (2016). https://doi.org/10.1109/INDIN.2016.7819217

  19. Uhlemann, T.H.J., Lehmann, C., Steinhilper, R.: The digital twin: realizing the cyber-physical production system for Industry 4.0. Procedia CIRP 61, 335–340 (2017)

    Article  Google Scholar 

  20. Schrefl, M., Neub, T., Schrefl, M., et al.: Modelling knowledge about data analysis modelling knowledge about data analysis in manufacturing processes. IFAC-PapersOnLine 48, 277–282 (2015). https://doi.org/10.1016/j.ifacol.2015.06.094

    Article  Google Scholar 

  21. Bordeleau, F.E., Mosconi, E., de Santa-Eulalia, L.A.: The management of operations Business intelligence and analytics value creation in Industry 4.0 : a multiple case study in manufacturing medium enterprises case study in manufacturing medium enterprises. Prod. Plann. Control 1–13 (2019). https://doi.org/10.1080/09537287.2019.1631458

  22. Raghav, R.S., Pothula, S., Vengattaraman, T., Ponnurangam, D.: A survey of data visualization tools for analyzing large volume of data in big data platform. In: Proceedings of International Conference on Communication, Computing and Electronics Systems, ICCES 2016, pp. 1–6 (2016). https://doi.org/10.1109/CESYS.2016.7889976

  23. Raffoni, A., Visani, F., Bartolini, M., Silvi, R.: Business Performance Analytics: exploring the potential for Performance Management Systems. Prod. Plann. Control 29, 51–67 (2018). https://doi.org/10.1080/09537287.2017.1381887

    Article  Google Scholar 

  24. Poleto, T., De Carvalho, V.D.H., Costa, A.P.C.S.: The full knowledge of big data in the integration of interorganizational information: an approach focused on decision making. Int. J. Decis. Support Syst. Technol. 9, 16–31 (2017). https://doi.org/10.4018/IJDSST.2017010102

    Article  Google Scholar 

  25. Morgan, R., Grossmann, G., Schrefl, M., Stumptner, M.: A model-driven approach for visualisation processes. In: ACM International Conference Proceeding Series (2019). https://doi.org/10.1145/3290688.3290698

  26. Thalmann, S.., Mangler, J., Schreck, T., et al.: Data analytics for industrial process improvement a vision paper. In: Proceeding - 2018 20th IEEE International Conference on Bus Informatics, CBI 2018, vol. 2, pp. 92–96 (2018). https://doi.org/10.1109/CBI.2018.10051

  27. Zhou, F., Lin, X., Liu, C., et al.: A survey of visualization for smart manufacturing. J. Vis. 22, 419–435 (2019). https://doi.org/10.1007/s12650-018-0530-2

    Article  Google Scholar 

  28. Ali, S.M., Gupta, N., Nayak, G.K., Lenka, R.K.: Big data visualization: tools and challenges. In: Proceedings of 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016, pp. 656–660 (2016). https://doi.org/10.1109/IC3I.2016.7918044

  29. Lee, B., Riche, N.H., Isenberg, P., Carpendale, S.: More than telling a story: transforming data into visually shared stories. IEEE Comput. Graph. Appl. 35, 84–90 (2015). https://doi.org/10.1109/MCG.2015.99

    Article  Google Scholar 

  30. Kosara, R., MacKinlay, J.: Storytelling: the next step for visualization. Computer (Long Beach Calif) 46, 44–50 (2013). https://doi.org/10.1109/MC.2013.36

    Article  Google Scholar 

  31. Tong, C., Roberts, R., Laramee, R.S., et al.: Storytelling and visualization: a survey. In: VISIGRAPP 2018 - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 212–224. SciTePress (2018)

    Google Scholar 

  32. Segel, E., Heer, J.: Narrative visualization: telling stories with data. IEEE Trans. Vis. Comput. Graph. 16, 1139–1148 (2010). https://doi.org/10.1109/TVCG.2010.179

    Article  Google Scholar 

  33. Ma, K.-L., Liao, I., Frazier, J., et al.: Scientific storytelling using visualization. IEEE Comput. Graph. Appl. 32, 12–15 (2012)

    Google Scholar 

Download references

Acknowledgments

This research was supported by the Portuguese National Funding Agency for Science, Research and Technology (FCT), within the Institute of Electronics and Informatics Engineering of Aveiro (IEETA), project UIDB/00127/2020.

Author information

Authors and Affiliations

  1. Department of Economics, Management, Industrial Engineering and Tourism, University of Aveiro, 3010-193, Aveiro, Portugal

    Juliana Salvadorinho

  2. Institute of Electronics and Informatics Engineering of Aveiro, Department of Economics, Management, Industrial Engineering and Tourism, University of Aveiro, 3010-193, Aveiro, Portugal

    Leonor Teixeira

  3. Institute of Electronics and Informatics Engineering of Aveiro, Department of Electronics, Telecommunications and Informatics, University of Aveiro, 3010-193, Aveiro, Portugal

    Beatriz Sousa Santos

Authors
  1. Juliana Salvadorinho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leonor Teixeira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Beatriz Sousa Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Leonor Teixeira .

Editor information

Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. University of Central Florida, Orlando, FL, USA

    Gavriel Salvendy

  3. University of West Florida, Pensacola, FL, USA

    June Wei

  4. Tokyo University of Science, Tokyo, Japan

    Sakae Yamamoto

  5. Tokyo City University, Tokyo, Japan

    Hirohiko Mori

  6. Towson University, Towson, MD, USA

    Gabriele Meiselwitz

  7. Missouri University of Science and Technology, Rolla, MO, USA

    Fiona Fui-Hoon Nah

  8. Missouri University of Science and Technology, Rolla, MO, USA

    Keng Siau

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Salvadorinho, J., Teixeira, L., Sousa Santos, B. (2020). Storytelling with Data in the Context of Industry 4.0: A Power BI-Based Case Study on the Shop Floor. In: Stephanidis, C., et al. HCI International 2020 – Late Breaking Papers: Interaction, Knowledge and Social Media. HCII 2020. Lecture Notes in Computer Science(), vol 12427. Springer, Cham. https://doi.org/10.1007/978-3-030-60152-2_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-60152-2_48

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60151-5

  • Online ISBN: 978-3-030-60152-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation