Using Industry 4.0 Concept – Digital Twin – to Improve the Efficiency of Leather Cutting in Automotive Industry

Miroslava Horváthová, Roman Lacko, Zuzana Hajduová


Purpose: The aim of this study is to propose alternatives of increasing the efficiency of material selection and processing in the selected company and reduce costs and leather sustainability as a result.

Methodology/Approach: In this case study, an automotive company processing a natural leather material that enters the process of a large-scale production was explored. For this purpose, the internal documents of the firm selected including its internal database and know-how of its employees were used. The ways of improving the efficiency of the material processing were proposed and tested in a digital environment. In the proposed solutions, Industry 4.0 principles were implemented.

Findings: By the use of Digital twin and other Industry 4.0 principles and solutions in the process of material selection and processing in the company selected, the increased efficiency and cost savings were achieved.

Research Limitation/implication: The solutions proposed in this paper were based on exploration of the chosen data set of the selected company. For the future research, testing of the given proposals in other companies should be conducted.

Originality/Value of paper: Although there is an increasing number of publications describing the concept Industry 4.0, the research providing evidence of its benefits for business entities is still scarce. This paper offers such a research in the enterprise selected.


cloud computing; Internet of Things; efficiency; big data; Industry 4.0; sustainability

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Ayani, M., Ganebäck, M. and Ng, A.H.C., 2018. Digital Twin: Applying emulation for machine reconditioning. Procedia CIRP, [e-journal] 72, pp.243-248.

Bonilla, S., Silva, H., Terra da Silva, M., Franco Gonçalves, R. and Sacomano, J., 2018. Industry 4.0 and Sustainability Implications: A Scenario-Based Analysis of the Impacts and Challenges. Sustainability, [e-journal] 10(10), p.3740.

Botkina, D., Hedlind, M., Olsson, B., Henser, J. and Lundholm, T., 2018. Digital Twin of a Cutting Tool. Procedia CIRP, [e-journal] 72, pp.215-218.

De Sousa Jabbour, A.B.L., Jabbour, C.J.C., Foropon, C. and Godinho Filho, M., 2018. When titans meet – Can industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors. Technol. Forecast. Soc. Change, [e-journal] 132, pp.18-25.

Grieco, A., Pacella, M. and Blaco, M., 2017. Image Based Quality Control of Free-form Profiles in Automatic Cutting Processes. Procedia CIRP, [e-journal] 62, pp.405-410.

Hu, L., Nguyen, N.-T., Tao, W., Leu, M.C., Liu, X.F., Shahriar, M.R. and Al Sunny, S.M.N., 2018. Modeling of Cloud-Based Digital Twins for Smart Manufacturing with MT Connect. Procedia Manufacturing, [e-journal] 26, pp.1193-1203.

Kamble, S.S., Gunasekaran, A. and Gawankar, S.A., 2018. Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, [e-journal] 117, pp.408-425.

Kritzinger, W., Karner, M., Traar, G., Henjes, J. and Sihn, W., 2018. Digital Twin in manufacturing: A categorical literature review and classification. IFAC PapersOnLine, [e-journal] 51(11), pp.1016-1022.

Kunath, M. and Winkler, H., 2018. Integrating the Digital Twin of the manufacturing system into a decision support system for improving the order management process. Procedia CIRP, [e-journal] 72, pp.225-231.

Leng, J., Zhang, H., Yan, D., Liu, Q., Chen, X. and Zhang, D., 2019. Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop. Journal of Ambient Intelligence and Humanized Computing, [e-journal] 10(3), pp.1155-1166.

Liu, J., Zhou, H., Tian, G., Liu, X. and Jing, X., 2019. Digital twin-based process reuse and evaluation approach for smart process planning. The International Journal of Advanced Manufacturing Technology, [e-journal] 100(5-8), pp.1619-1634.

Moreno, A., Velez, G., Ardanza, A., Barandiaran, I., de Infante, Á.R. and Chopitea, R., 2017. Virtualisation process of a sheet metal punching machine within the Industry 4.0 vision. International Journal on Interactive Design and Manufacturing (IJIDeM), [e-journal] 11(2), pp.365-373.

Müller, J.M., Kiel, D. and Voigt, K.-I., 2018. What Drives the Implementation of Industry 4.0? The Role of Opportunities and Challenges in the Context of Sustainability. Sustainability, [e-journal] 10(1), p.247.

Nagy, J., Oláh, J., Erdei, E., Máté, D. and Popp, J., 2018. The Role and Impact of Industry 4.0 and the Internet of Things on the Business Strategy of the Value Chain—The Case of Hungary. Sustainability, [e-journal] 10(10), p.3491.

Negri, E., Fumagalli, L. and Macchi, M., 2017. A Review of the Roles of Digital Twin in CPS-based Production Systems. Procedia Manufacturing, [e-journal] 11, pp.939-948.

Oztemel, E. and Gursev, S., 2018. Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing.

Padovano, A., Longo, F., Nicoletti, L. and Mirabelli, G., 2018. A Digital Twin based Service Oriented Application for a 4.0 Knowledge Navigation in the Smart Factory. IFAC PapersOnLine, [e-journal] 51(11), pp.631-636.

Piccarozzi, M., Aquilani, B., Gatti, C., 2018. Industry 4.0 in Management Studies: A Systematic Literature Review. Sustainability, [e-journal] 10(10), p.3821.

Pringle, T., Barwood, M. and Rahimifard, S., 2016. The Challenges in Achieving a Circular Economy within Leather Recycling. Procedia CIRP, [e-journal] 48, pp.544-549.

Roblek, V., Meško, M. and Krapež, A., 2016. A Complex View of Industry 4.0. SAGE Open, [e-journal] April-June, pp.1-11.

Saucedo-Martínez, J.A., Pérez-Lara, M., Marmolejo-Saucedo, J.A., Salais-Fierro, T.E. and Vasant, P., 2018. Industry 4.0 framework for management and operations: a review. Journal of Ambient Intelligence and Humanized Computing, [e-journal] 9(3), pp.789-801.

Schneider, P., 2018. Managerial challenges of Industry 4.0: an empirically backed research agenda for a nascent field. Rev. Manag. Sci., [e-journal] 12(3), pp.803-848.

Sivarajah, U., Kamal, M.M., Irani, Z. and Weerakkody, V., 2017. Critical analysis of Big Data challenges and analytical methods. J. Bus. Res., [e-journal] 70, pp.263-286.

Stepanov, A., Manninen, M., Pärnänen, I., Hirvimäki, M. and Salminen, A., 2015. Laser Cutting of Leather: Tool for Industry or Designers?. Phys. Procedia, [e-journal] 78, pp.157-162.

Tamás, P. and Illés, B., 2016. Process Improvement Trends for Manufacturing Systems in Industry 4.0. Academic Journal of Manufacturing Engineering, 14(4), pp.119-125.

Thames, L. and Schaefer, D., 2016. Software-defined Cloud Manufacturing for Industry 4.0. Procedia CIRP, [e-journal] 52, pp.12-17.

Uhlemann, T.H.-J., Lehmann, C. and Steinhilper, R., 2017. The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0. Procedia CIRP, [e-journal] 61, pp.335-340.

Uhlemann, T.H.-J., Schock, C., Lehmann, C., Freiberger, S. and Steinhilper, R., 2017. The Digital Twin: Demonstrating the Potential of Real Time Data Acquisition in Production Systems. Procedia Manuf., [e-journal] 9, pp.113-120.

Wan, J. and Xia, M., 2017. Cloud-Assisted Cyber-Physical Systems for the Implementation of Industry 4.0. Mob. Netw. Appl., [e-journal] 22(6), pp.1157-1158.

Wolf, G., Meier, S. and Lin, A., 2013/2014. Leather and Sustainability: From Contradiction to Value Creation. [pdf] Liverpool: Limited. Available at: < > [Accessed 10 May 2019].

Zgodavová, K., Bober, P., Sütöová, A. and Lengyelová, K., 2019. Supporting sustainable entrepreneurship in injection molding of plastic parts by optimizing material consumption. Przemysł Chemiczny : Chemical Industry, 98(3), pp.399-407.

Zhang, H., Zhang, G. and Yan, Q., 2018. Digital twin-driven cyber-physical production system towards smart shop-floor. J. Ambient Intell. Humaniz. Comput., pp.1-15.

Zhong, R.Y., Xu, X., Klotz, E. and Newman, S.T., 2017. Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering, [e-journal] 3(5), pp.616-630.



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Copyright (c) 2019 Miroslava Horváthová, Roman Lacko, Zuzana Hajduová

ISSN 1335-1745 (print)
ISSN 1338-984X (online)
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