Selecting the Best Strategy for Industry 4.0 Applications with a Case Study

  • Melike ErdoganEmail author
  • Betul Ozkan
  • Ali Karasan
  • Ihsan Kaya
Conference paper
Part of the Lecture Notes in Management and Industrial Engineering book series (LNMIE)


In this paper, we try to find the best strategy for Industry 4.0 implementation. For this aim, we determine the aggregated strategies for applying this concept and criteria that are used to select the best strategy. With the criteria set out in this context, basic strategies should be applied as a priority, considering for example human resources, work organization and design, information systems, and effective use of resources, and the development of new business models and standardization are specified. Since this selection is a process in which many different measures need to be considered, multi-criteria decision-making (MCDM) methods based on AHP-VIKOR methodologies have been applied to find the best strategy. Fuzzy set theory was beneficial for coping with uncertainties in the selection process.


Fuzzy sets Industry 4.0 Multi-criteria decision making Strategy selection 


  1. Barbosa J, Leitão P, Trentesaux D, Colombo AW, Karnouskosk S (2017) Cross benefits from cyber-physical systems and intelligent products for future smart industries. In: IEEE international conference on industrial informatics (INDIN) 7819214, pp 504–509Google Scholar
  2. Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst 28(5):755–768CrossRefGoogle Scholar
  3. Buckley JJ (1985) Fuzzy hierarchical analysis. Fuzzy Sets Syst 17(3):233–247MathSciNetCrossRefzbMATHGoogle Scholar
  4. Chang WY, Wu SJ (2016) Investigated information data of CNC machine tool for established productivity of industry 4.0. In: 2016 5th IIAI international congress on advanced applied informatics (IIAI-AAI). IEEE, pp 1088–1092Google Scholar
  5. Chen JK, Chen IS (2010) Using a novel conjunctive MCDM approach based on DEMATEL, fuzzy ANP, and TOPSIS as an innovation support system for Taiwanese higher education. Expert Syst Appl 37(3):1981–1990CrossRefGoogle Scholar
  6. Fleischmann H, Kohl J, Franke J (2017) Improving maintenance processes with distributed monitoring systems. In: IEEE international conference on industrial informatics (INDIN) 7819189, pp 377–382Google Scholar
  7. Forstner L, Dümmler M (2014) Integrierte Wertschöpfungsnetzwerke-Chancen und Potenziale durch Industrie 4.0. e and i. Elektrotechnik und Informationstechnik 131(7):199–201CrossRefGoogle Scholar
  8. Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabási AL (2007) The human disease network. Proc Natl Acad Sci 104(21):8685–8690CrossRefGoogle Scholar
  9. Gorecky D, Khamis M, Mura K (2017) Introduction and establishment of virtual training in the factory of the future. Int J Comput Integr Manuf 30(1):182–190Google Scholar
  10. Grundstein S, Freitag M, Scholz-Reiter B (2017) A new method for autonomous control of complex job shops—integrating order release, sequencing and capacity control to meet due dates. J Manuf Syst 42:11–28CrossRefGoogle Scholar
  11. Gul M, Celik E, Aydin N, Gumus AT, Guneri AF (2016) A state of the art literature review of VIKOR and its fuzzy extensions on applications. Appl Soft Comput 46:60–89CrossRefGoogle Scholar
  12. Gupta P, Mehlawat MK, Grover N (2016) Intuitionistic fuzzy multi-attribute group decision-making with an application to plant location selection based on a new extended VIKOR method. Inf Sci 370:184–203CrossRefGoogle Scholar
  13. Hsieh TY, Lu ST, Tzeng GH (2004) Fuzzy MCDM approach for planning and design tenders selection in public office buildings. Int J Project Manag 22(7):573–584CrossRefGoogle Scholar
  14. Kahraman C, Süder A, Kaya İ (2014) Fuzzy multicriteria evaluation of health research investments. Technol Econ Dev Econ 20(2):210–226CrossRefGoogle Scholar
  15. Kaya T, Kahraman C (2010) Multicriteria renewable energy planning using an integrated fuzzy VIKOR and AHP methodology: the case of Istanbul. Energy 35(6):2517–2527CrossRefGoogle Scholar
  16. Kaya T, Kahraman C (2011) Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Syst Appl 38(6):6577–6585CrossRefGoogle Scholar
  17. Kaya I, Kahraman C (2014) A comparison of fuzzy multicriteria decision making methods for intelligent building assessment. J Civ Eng Manag 20(1):59–69CrossRefGoogle Scholar
  18. Klein S, Pluim JP, Staring M, Viergever MA (2009) Adaptive stochastic gradient descent optimisation for image registration. Int J Comput Vis 81(3):227CrossRefGoogle Scholar
  19. Kolberg D, Knobloch J, Zühlke D (2016) Towards a lean automation interface for workstations. Int J Prod Res. Google Scholar
  20. Oesterreich T-D, Teuteberg F (2016) Understanding the implications of digitisation and automation in the context of Industry 4.0: a triangulation approach and elements of a research agenda for the construction industry. Comput Ind 83:121–139CrossRefGoogle Scholar
  21. Opricovic S, Tzeng GH (2004) Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 156(2):445–455CrossRefzbMATHGoogle Scholar
  22. Rennung F, Luminosu CT, Draghici A (2016) Service provision in the framework of Industry 4.0. Procedia Soc Behav Sci 221:372–377CrossRefGoogle Scholar
  23. Rezaie K, Ramiyani SS, Shirkouhi SN, Badizadeh A (2014) Evaluating performance of Iranian cement firms using an integrated fuzzy AHP-VIKOR method. Appl Math Model 38:5033–5046MathSciNetCrossRefGoogle Scholar
  24. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New YorkzbMATHGoogle Scholar
  25. Sepulcre M, Gozalvez J, Coll-Perales B (2016) Multipath QoS-driven routing protocol for industrial wireless networks. J Netw Comput Appl 74:121–132CrossRefGoogle Scholar
  26. Tuzkaya G, Gülsün B, Kahraman C, Özgen D (2010) An integrated fuzzy multi-criteria decision making methodology for material handling equipment selection problem and an application. Expert Syst Appl 37(4):2853–2863CrossRefGoogle Scholar
  27. Tzeng GH, Huang JJ (2011) Multiple attribute decision making: methods and applications. CRC pressGoogle Scholar
  28. Veza I. Mladineo M, Gjeldum N (2015) Managing innovative production network of smart factories. IFAC-Papers OnLine 48(3):555–560Google Scholar
  29. Vinodh S, Prasanna M, Prakash NH (2014) Integrated fuzzy AHP–TOPSIS for selecting the best plastic recycling method: a case study. Appl Math Model 38(19):4662–4672CrossRefGoogle Scholar
  30. Wu Y, Chen K, Zeng B, Xu H, Yang Y (2016) Supplier selection in nuclear power industry with extended VIKOR method under linguistic information. Appl Soft Comput 48:444–457CrossRefGoogle Scholar
  31. Zadeh L (1965) Fuzzy sets. Inf Control 8(1965):338–353CrossRefzbMATHGoogle Scholar
  32. Zare M, Pahl C, Rahnama H, Nilashi M, Mardani A, Ibrahim O, Ahmadi H (2016) Multi-criteria decision making approach in E-learning: a systematic review and classification. Appl Soft Comput 45:108–128CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Melike Erdogan
    • 1
    Email author
  • Betul Ozkan
    • 1
  • Ali Karasan
    • 1
  • Ihsan Kaya
    • 1
  1. 1.Industrial EngineeringYıldız Technical UniversityBeşiktaş, İstanbulTurkey

Personalised recommendations