Business Process Modeling in Industry 4.0 Using Transformation Accelerator Tool

  • Dragan Peraković
  • Marko Periša
  • Ivan Cvitić
  • Petra ZorićEmail author
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


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 to deliver accurate and real-time information to end users. The identification of relevant parameters provides a starting point in the field of modeling ecosystem elements to create a unique integrated system. In the process of designing a unique integrated system, it is important to create new business models for more efficient business within the concept of Industry 4.0. This paper will outline the possibilities of using the Transformation Accelerator tool in the process of creating new business models. Relevant parameters represent the basis for the successful modeling of a business model in an Industry 4.0 environment. The paper also shows the impact of the business transition from traditional (analog culture) to digital business by comparing current business models.


Integrated system Communication system Innovative services Business models TM forum 


  1. 1.
    Oztemel, E., Gursev, S.: Literature review of Industry 4.0 and related technologies. J. Intell. Manuf. (2018).
  2. 2.
    Klitou, D., Conrads, J., Rasmussen, M.: Germany: Industrie 4.0 Fact box for Germany’s Industrie 4.0 policy initiative (2017)Google Scholar
  3. 3.
    Takeda, A., Hatakeyama, Y.: Conversion method for user experience design information and software requirement specification. In: Markus, A. (ed.) Design, User Experience, and Usability: Design Thinking and Methods, pp. 356–364. Springer, Cham (2016). Scholar
  4. 4.
    Guo, Y., Wu, J., Yang, K., Yu, L.: 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 (2017). Scholar
  5. 5.
    Häikiö, J., Koivumäki, T.: Exploring digital service innovation process through value creation. J. Innov. Manag. 4(2), 96–124 (2016)CrossRefGoogle Scholar
  6. 6.
    Bello, O., Zeadally, S., Badra, M.: Network layer inter-operation of Device-to-Device communication technologies in Internet of Things (IoT). Ad Hoc Netw. 57, 52–62 (2017). Scholar
  7. 7.
    Sikder, A.K., Petracca, G., Aksu, H., Jaeger, T., Uluagac, A.S.: A survey on sensor-based threats to Internet-of-Things (IoT) devices and applications. (2018)
  8. 8.
    Rojko, A.: Industry 4.0 concept: background and overview. Int. J. Interact. Mobile Technol. 11(5), 77 (2017). Scholar
  9. 9.
    Sethi, P., Sarangi, S.R.: Internet of Things: architectures, protocols, and applications. J. Electr. Comput. Eng. 2017, 1–25 (2017). Scholar
  10. 10.
    Balog, M., Szilágyi, E., Dupláková, D., Minďaš, M.: Effect verification of external factor to readability of RFID transponder using least square method. Measurement. 94, 233–238 (2016). Scholar
  11. 11.
    Kolarovszki, P.: Research of readability and identification of the items in the postal and logistics environment. Transp. Telecommun. J. 15(3), 196 (2014). Scholar
  12. 12.
    Ren, L., Zhang, L., Tao, F., Zhao, C., Chai, X., Zhao, X.: Cloud manufacturing: from concept to practice. Enterp. Inf. Syst. 9(2), 186–209 (2015). Scholar
  13. 13.
    Immonen, A., Ovaska, E., Kalaoja, J., Pakkala, D.: A service requirements engineering method for a digital service ecosystem. SOCA. 10(2), 151–172 (2016). Scholar
  14. 14.
    Abeywickrama, D.B., Ovaska, E.: A survey of autonomic computing methods in digital service ecosystems. SOCA. 11(1), 1–31 (2017). Scholar
  15. 15.
    Sklyar, A., Kowalkowski, C., Tronvoll, B., Sörhammar, D.: Organizing for digital servitization: a service ecosystem perspective. J. Bus. Res. (2019).
  16. 16.
    Pakkala, D., Spohrer, J.: Digital service: technological agency in service systems. In: Proceedings of the 52nd Hawaii International Conference on System Sciences, vol. 6, pp. 1886–1895 (2019)Google Scholar
  17. 17.
    Barile, S., Lusch, R., Reynoso, J., Saviano, M., Spohrer, J.: Systems, networks, and ecosystems in service research. J. Serv. Manag. 27(4), 652–674 (2016). Scholar
  18. 18.
    Chae, B.(.K.).: A general framework for studying the evolution of the digital innovation ecosystem: the case of big data. Int. J. Inf. Manag. 45, 83–94 (2019). Scholar
  19. 19.
    Mochalov, V.P., Bratchenko, N.Y., Yakovlev, S.V., Gosteva, D.V.: Distributed management system for infocommunication networks based on TM Forum Framework. CEUR Workshop Proc. 2254, 81–93 (2018)Google Scholar
  20. 20.
    Tr3Dent: Digital transformation accelerator. (n.d.)
  21. 21.
    Flatscher, M., Riel, A.: Stakeholder integration for the successful product–process co-design for next-generation manufacturing technologies. CIRP Ann. Manuf. Technol. 65(1), 181–184 (2016). Scholar
  22. 22.
    Thoben, K.-D., Wiesner, S., Wuest, T.: “Industrie 4.0” and smart manufacturing—a review of research issues and application examples. Int. J. Autom. Technol. 11(1), 4–16 (2017). Scholar
  23. 23.
    Stock, T., Seliger, G.: Opportunities of sustainable manufacturing in Industry 4.0. Proc. CIRP. 40, 536–541 (2016). Scholar
  24. 24.
    Zheng, P., Wang, H., Sang, Z., Zhong, R.Y., Liu, Y., Liu, C.: Smart manufacturing systems for Industry 4.0: conceptual framework, scenarios, and future perspectives. Front. Mech. Eng. 13(2), 137–150 (2018). Scholar
  25. 25.
    Raihanian Mashhadi, A., Behdad, S.: Ubiquitous life cycle assessment (U-LCA): a proposed concept for environmental and social impact assessment of Industry 4.0. Manuf. Lett. 15, 93–96 (2018). Scholar
  26. 26.
    Nihtianov, S., Luque, A.: Smart Sensors and MEMS Intelligent Sensing Devices and Microsystems for Industrial Applications, 2nd edn. Woodhead Publishing, Cambridge, UK (2018)Google Scholar
  27. 27.
    Mekki, K., Bajic, E., Chaxel, F., Meyer, F.: 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 (2018). Scholar
  28. 28.
    Sinha, R.S., Wei, Y., Hwang, S.H.: A survey on LPWA technology: LoRa and NB-IoT. ICT Exp. 3(1), 14–21 (2017). Scholar
  29. 29.
    Aernouts, M., Berkvens, R., Van Vlaenderen, K., Weyn, M.: Sigfox and LoRaWAN datasets for fingerprint localization in large urban and rural areas. Data. 3(2), 13 (2018). Scholar
  30. 30.
    Periša, M., Sente, R.E., Cvitić, I., Kolarovszki, P.: 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, Ghent, Belgium (2018). Scholar
  31. 31.
    Peraković, D., Periša, M., Sente, R.E.: Information and communication technologies within Industry 4.0 concept. In: Ivanov, V., et al. (eds.) Advances in Design, Simulation and Manufacturing, pp. 127–134. Springer International Publishing, Cham (2018). Scholar
  32. 32.
    Peraković, D., Periša, M., Zorić, P.: Challenges and issues of ICT in Industry 4.0. In: Advances in Design, Simulation and Manufacturing II, pp. 259–269. Springer, Cham (2020). Scholar
  33. 33.
    Jovović, I., Husnjak, S., Forenbacher, I., Maček, S.: Innovative application of 5G and blockchain technology in Industry 4.0. EAI Endorsed Trans. Ind. Netw. Intell. Syst. 6(18), 157122 (2019). Scholar
  34. 34.
    Cheng, J., Chen, W., Tao, F., Lin, C.L.: Industrial IoT in 5G environment towards smart manufacturing. J. Ind. Inf. Integr. 10(March), 10–19 (2018). Scholar
  35. 35.
    Boyes, H., Hallaq, B., Cunningham, J., Watson, T.: The industrial internet of things (IIoT): an analysis framework. Comput. Ind. 101(June), 1–12 (2018). Scholar
  36. 36.
    Mittal, S., Khan, M.A., Romero, D., Wuest, T.: Smart manufacturing: characteristics, technologies and enabling factors. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 233(5), 1342–1361 (2019). Scholar
  37. 37.
    Cvitić, I., Peraković, D., Kuljanić, T.M.: Availability factors in delivery of information and communication resources to traffic system users. In: Mikulski, J. (ed.) Smart Solutions in Today’s Transport, pp. 28–41. Springer International Publishing, Cham (2017)CrossRefGoogle Scholar
  38. 38.
    Cvitić, I., Peraković, D., Periša, M., Botica, M.: Novel approach for detection of IoT generated DDoS traffic. Wirel. Netw. 1, 1–14 (2019). Scholar
  39. 39.
    Peraković, D., Periša, M., Cvitić, I., Husnjak, S.: Model for detection and classification of DDoS traffic based on artificial neural network. Telfor J. 9(1), 26 (2017). Scholar
  40. 40.
    Peraković, D., Periša, M., Zorić, P.: Identification of the relevant parameters for modeling the ecosystem elements in Industry 4.0. In: Knapcikova, L., Balog, M., Peraković, D., Periša, M. (eds.) 4th EAI International Conference on Management of Manufacturing Systems, p. 260. Springer International Publishing, Cham (2020). Scholar
  41. 41.
    Ibarra, D., Ganzarain, J., Igartua, J.I.: Business model innovation through Industry 4.0: a review. Proc. Manuf. 22, 4–10 (2018). Scholar
  42. 42.
    Mittal, S., Khan, M.A., Romero, D., Wuest, T.: A critical review of smart manufacturing & Industry 4.0 maturity models: implications for small and medium-sized enterprises (SMEs). J. Manuf. Syst. 49(November), 194–214 (2018). Scholar
  43. 43.
    Sjödin, D.R., Parida, V., Leksell, M., Petrovic, A.: 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. Res. Technol. Manag. 61(5), 22–31 (2018). Scholar
  44. 44.
    David, S.: Information Systems Investment Decision Making Using Return of Investment Application of CRUDi Framework with eTOM Business Process Framework in Portuguese Telecommunication Industry. Universidade Nova de Lisboa, Lisbon (2018)Google Scholar
  45. 45.
    TM Forum: TM Forum. (2019)
  46. 46.
    Da Xu, L., Xu, E.L., Li, L.: Industry 4.0: state of the art and future trends, 7543. (2018)
  47. 47.
    Giessbauer, R., Lübben, E., Schrauf, S., Pillsbury, S.: Global Digital Operations Study 2018—How Industry Leaders Build Integrated Operations Ecosystems to Deliver End-to-End Customer Solutions. Strategy & White paper (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Transport and Traffic SciencesUniversity of ZagrebZagrebCroatia

Personalised recommendations