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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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.
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
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.
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.
Häikiö, J., & Koivumäki, T. (2016). Exploring digital service innovation process through value creation. Journal of Innovation Management, 4(2), 96–124.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
Tr3Dent. (n.d.). Digital transformation Accelerator. Retrieved from https://www.tr3dent.com/
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.
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.
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.
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.
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.
Nihtianov, S., & Luque, A. (2018). Smart sensors and MEMS intelligent sensing devices and microsystems for industrial applications (2nd ed.). Duxford: Woodhead Publishing.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
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)