Skip to main content

Identification of the Relevant Parameters for Modeling the Ecosystem Elements in Industry 4.0

  • Conference paper
  • First Online:
4th EAI International Conference on Management of Manufacturing Systems

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

The development of information and communication technologies leads to more efficient logistics and production processes through the implementation of the Industry 4.0 concept. For this purpose, it is important to establish all elements of the ecosystem with the aim of delivering accurate and real-time information to end users. Today’s scientific research literature does not provide enough insight into the field of modeling unique integrated Industry 4.0 ecosystem with the aim of delivering the required services. The aim of this research is to identify the relevant parameters required for modeling ecosystem elements within the Industry 4.0 concept. The identification of relevant parameters provides a starting point in the field of modeling ecosystem elements for the purpose of creating unique integrated system. In the process of designing a unique integrated system, it is important to create new business models for the purpose of more efficient business within the concept of Industry 4.0. The article also shows the impact of business transition from traditional to digital business by comparing current business models.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

References

  1. Oztemel, E., & Gursev, S. (2018). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 1–56. https://doi.org/10.1007/s10845-018-1433-8.

    Article  Google Scholar 

  2. Klitou, D., Conrads, J., & Rasmussen, M. (2017). Germany: Industrie 4.0 fact box for Germany’s Industrie 4.0 policy initiative. Retrieved from https://ec.europa.eu/growth/tools-databases/dem/monitor/sites/default/files/DTM_Industrie%204.0.pdf

  3. Takeda, A., & Hatakeyama, Y. (2016). Conversion method for user experience design information and software requirement specification. In A. Markus (Ed.), Design, user experience, and usability: Design thinking and methods (pp. 356–364). Cham: Springer. https://doi.org/10.1007/978-3-319-40409-7_34.

    Chapter  Google Scholar 

  4. Guo, Y., Wu, J., Yang, K., & Yu, L. (2017). Research on requirement elicitation model of high-end equipment based on requirement classification under Internet and big data environment. In Advances in computer science research (Vol. 71, pp. 685–692). doi:https://doi.org/10.2991/icmmita-16.2016.127.

  5. Häikiö, J., & Koivumäki, T. (2016). Exploring digital service innovation process through value creation. Journal of Innovation Management, 4(2), 96–124.

    Article  Google Scholar 

  6. Bello, O., Zeadally, S., & Badra, M. (2017). Network layer inter-operation of Device-to-Device communication technologies in Internet of Things (IoT). Ad Hoc Networks, 57, 52–62. https://doi.org/10.1016/j.adhoc.2016.06.010.

    Article  Google Scholar 

  7. Sikder, A. K., Petracca, G., Aksu, H., Jaeger, T., & Uluagac, A. S. (2018). A survey on sensor-based threats to Internet-of-Things (IoT) devices and applications. Retrieved from http://arxiv.org/abs/1802.02041

  8. Rojko, A. (2017). Industry 4.0 concept: Background and overview. International Journal of Interactive Mobile Technologies, 11(5), 77. https://doi.org/10.3991/ijim.v11i5.7072.

    Article  Google Scholar 

  9. Sethi, P., & Sarangi, S. R. (2017). Internet of things: Architectures, protocols, and applications. Journal of Electrical and Computer Engineering, 2017, 1–25. https://doi.org/10.1155/2017/9324035.

    Article  Google Scholar 

  10. Balog, M., Szilágyi, E., Dupláková, D., & Minďaš, M. (2016). Effect verification of external factor to readability of RFID transponder using least square method. Measurement, 94, 233–238. https://doi.org/10.1016/j.measurement.2016.07.088.

    Article  Google Scholar 

  11. Kolarovszki, P. (2014). Research of readability and identification of the items in the postal and logistics environment. Transport and Telecommunication Journal, 15(3), 196. https://doi.org/10.2478/ttj-2014-0017.

    Article  Google Scholar 

  12. Ren, L., Zhang, L., Tao, F., Zhao, C., Chai, X., & Zhao, X. (2015). Cloud manufacturing: From concept to practice. Enterprise Information Systems, 9(2), 186–209. https://doi.org/10.1080/17517575.2013.839055.

    Article  Google Scholar 

  13. Immonen, A., Ovaska, E., Kalaoja, J., & Pakkala, D. (2016). A service requirements engineering method for a digital service ecosystem. Service Oriented Computing and Applications, 10(2), 151–172. https://doi.org/10.1007/s11761-015-0175-0.

    Article  Google Scholar 

  14. Abeywickrama, D. B., & Ovaska, E. (2017). A survey of autonomic computing methods in digital service ecosystems. Service Oriented Computing and Applications, 11(1), 1–31. https://doi.org/10.1007/s11761-016-0203-8.

    Article  Google Scholar 

  15. Sklyar, A., Kowalkowski, C., Tronvoll, B., & Sörhammar, D. (2019). Organizing for digital servitization: A service ecosystem perspective. Journal of Business Research, 104, 450–460. https://doi.org/10.1016/j.jbusres.2019.02.012.

    Article  Google Scholar 

  16. Pakkala, D., & Spohrer, J. (2019). Digital service: Technological agency in service systems. In Proceedings of the 52nd Hawaii International Conference on System Sciences (Vol. 6, pp. 1886–1895).

    Google Scholar 

  17. Barile, S., Lusch, R., Reynoso, J., Saviano, M., & Spohrer, J. (2016). Systems, networks, and ecosystems in service research. Journal of Service Management, 27(4), 652–674. https://doi.org/10.1108/JOSM-09-2015-0268.

    Article  Google Scholar 

  18. Chae, B. K. (2019). A General framework for studying the evolution of the digital innovation ecosystem: The case of big data. International Journal of Information Management, 45, 83–94. https://doi.org/10.1016/j.ijinfomgt.2018.10.023.

    Article  Google Scholar 

  19. Tr3Dent. (n.d.). Digital transformation Accelerator. Retrieved from https://www.tr3dent.com/

  20. Flatscher, M., & Riel, A. (2016). Stakeholder integration for the successful product–process co-design for next-generation manufacturing technologies. CIRP Annals – Manufacturing Technology, 65(1), 181–184. https://doi.org/10.1016/j.cirp.2016.04.055.

    Article  Google Scholar 

  21. Thoben, K.-D., Wiesner, S., & Wuest, T. (2017). “Industrie 4.0” and smart manufacturing – A review of research issues and application examples. International Journal of Automation Technology, 11(1), 4–16. https://doi.org/10.20965/ijat.2017.p0004.

    Article  Google Scholar 

  22. Stock, T., & Seliger, G. (2016). Opportunities of sustainable manufacturing in Industry 4.0. Procedia CIRP, 40, 536–541. https://doi.org/10.1016/j.procir.2016.01.129.

    Article  Google Scholar 

  23. Zheng, P., Wang, H., Sang, Z., Zhong, R. Y., Liu, Y., Liu, C., & Xu, X. (2018). Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Frontiers of Mechanical Engineering, 13(2), 137–150. https://doi.org/10.1007/s11465-018-0499-5.

    Article  Google Scholar 

  24. Raihanian Mashhadi, A., & Behdad, S. (2018). Ubiquitous Life Cycle Assessment (U-LCA): A proposed concept for environmental and social impact assessment of industry 4.0. Manufacturing Letters, 15, 93–96. https://doi.org/10.1016/j.mfglet.2017.12.012.

    Article  Google Scholar 

  25. Nihtianov, S., & Luque, A. (2018). Smart sensors and MEMS intelligent sensing devices and microsystems for industrial applications (2nd ed.). Duxford: Woodhead Publishing.

    Google Scholar 

  26. Mekki, K., Bajic, E., Chaxel, F., & Meyer, F. (2018). Overview of cellular LPWAN technologies for IoT deployment: Sigfox, LoRaWAN, and NB-IoT. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018, (March) (pp. 197–202). doi:https://doi.org/10.1109/PERCOMW.2018.8480255.

  27. Sinha, R. S., Wei, Y., & Hwang, S. H. (2017). A survey on LPWA technology: LoRa and NB-IoT. ICT Express, 3(1), 14–21. https://doi.org/10.1016/j.icte.2017.03.004.

    Article  Google Scholar 

  28. Aernouts, M., Berkvens, R., Van Vlaenderen, K., & Weyn, M. (2018). Sigfox and LoRaWAN datasets for fingerprint localization in large urban and rural areas. Data, 3(2), 13. https://doi.org/10.3390/data3020013.

    Article  Google Scholar 

  29. Periša, M., Sente, R. E., Cvitić, I., & Kolarovszki, P. (2018). Application of innovative smart wearable device in Industry 4.0. In Proceedings of the 3rd EAI International Conference on Management of Manufacturing Systems (pp. 1–10). EAI. doi:https://doi.org/10.4108/eai.6-11-2018.2279105.

  30. Peraković, D., Periša, M., & Sente, R. E. (2019). Information and communication technologies within industry 4.0 concept. In V. Ivanov, Y. Rong, J. Trojanowska, J. Venus, A. Liaposhchenko, J. Zajac, & D. Perakovic (Eds.), Advances in design, simulation and manufacturing (pp. 127–134). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-93587-4_14.

    Chapter  Google Scholar 

  31. Peraković, D., Periša, M., & Zorić, P. (2020). Challenges and issues of ICT in Industry 4.0. In Advances in design, simulation and manufacturing II (pp. 259–269). Cham: Springer. https://doi.org/10.1007/978-3-030-22365-6_26.

    Chapter  Google Scholar 

  32. Jovović, I., Husnjak, S., Forenbacher, I., & Maček, S. (2019). Innovative application of 5G and blockchain technology in Industry 4.0. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 6(18), 157122. https://doi.org/10.4108/eai.28-3-2019.157122.

    Article  Google Scholar 

  33. Cheng, J., Chen, W., Tao, F., & Lin, C. L. (2018). Industrial IoT in 5G environment towards smart manufacturing. Journal of Industrial Information Integration, 10(March), 10–19. https://doi.org/10.1016/j.jii.2018.04.001.

    Article  Google Scholar 

  34. Boyes, H., Hallaq, B., Cunningham, J., & Watson, T. (2018). The industrial internet of things (IIoT): An analysis framework. Computers in Industry, 101(June), 1–12. https://doi.org/10.1016/j.compind.2018.04.015.

    Article  Google Scholar 

  35. Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2019). Smart manufacturing: Characteristics, technologies and enabling factors. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 233(5), 1342–1361. https://doi.org/10.1177/0954405417736547.

    Article  Google Scholar 

  36. Cvitić, I., Peraković, D., & Kuljanić, T. M. (2017). Availability factors in delivery of information and communication resources to traffic system users. In J. Mikulski (Ed.), Smart solutions in today’s transport (pp. 28–41). Springer International Publishing, Cham, Switzerland.

    Google Scholar 

  37. Cvitić, I., Peraković, D., Periša, M., & Botica, M. (2019). Novel approach for detection of IoT generated DDoS traffic. Wireless Networks, 1, 1–14. https://doi.org/10.1007/s11276-019-02043-1.

    Article  Google Scholar 

  38. Perakovic, D., Perisa, M., Cvitic, I., & Husnjak, S. (2017). Model for detection and classification of DDoS traffic based on artificial neural network. Telfor Journal, 9(1), 26–31. https://doi.org/10.5937/telfor1701026P.

    Article  Google Scholar 

  39. Ibarra, D., Ganzarain, J., & Igartua, J. I. (2018). Business model innovation through Industry 4.0: A review. Procedia Manufacturing, 22, 4–10. https://doi.org/10.1016/j.promfg.2018.03.002.

    Article  Google Scholar 

  40. Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2018). A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs). Journal of Manufacturing Systems, 49(November), 194–214. https://doi.org/10.1016/j.jmsy.2018.10.005.

    Article  Google Scholar 

  41. Sjödin, D. R., Parida, V., Leksell, M., & Petrovic, A. (2018). Smart factory implementation and process innovation: A preliminary maturity model for leveraging digitalization in manufacturing moving to smart factories presents specific challenges that can be addressed through a structured approach focused on people. Research Technology Management, 61(5), 22–31. https://doi.org/10.1080/08956308.2018.1471277.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dragan Perakovic .

Editor information

Editors and Affiliations

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

Perakovic, D., Perisa, M., Cvitic, I., Zoric, P. (2020). Identification of the Relevant Parameters for Modeling the Ecosystem Elements in Industry 4.0. In: Knapcikova, L., Balog, M., Perakovic, D., Perisa, M. (eds) 4th EAI International Conference on Management of Manufacturing Systems. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-34272-2_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-34272-2_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34271-5

  • Online ISBN: 978-3-030-34272-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics