The Relationship Between the Implementation Levels of Industry 4.0 Technologies and Advanced Manufacturing Technologies

• Autorzy: Sari Tuğba

Identyfikatory

DOI

10.24425/mper.2022.142382

• Języki publikacji: EN

Abstrakty

EN

Industry 4.0 is expected to provide high quality and customized products at lower costs by increasing efficiency, and hence create a competitive advantage in the manufacturing industry. As the emergence of Industry 4.0 is deeply rooted in the past industrial revolutions, Advanced Manufacturing Technologies of Industry 3.0 are the precursors of the latest Industry 4.0 technologies. This study aims to contribute to the understanding of technological evolution of manufacturing industry based on the relationship between the usage levels of Advanced Manufacturing Technologies and Industry 4.0 technologies. To this end, a survey was conducted with Turkish manufacturers to assess and compare their manufacturing technology usage levels. The survey data collected from 424 companies was analyzed by machine learning approach. The results of the study reveal that the implementation level of each Industry 4.0 technology is positively associated with the implementation levels of a set of Advanced Manufacturing Technologies.

Słowa kluczowe

EN

advanced manufacturing technologies; AMTs; Industry 4.0; machine learning

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2022
• Tom: Vol. 13, No. 3
• Strony: 52--60
• Opis fizyczny: Bibliogr. 40 poz., tab.

Twórcy

autor

Sari Tuğba

• Konya Food and Agriculture University, Department of Management Information Systems, 42080 Konya, Turkey

Bibliografia

• Agostini L. and Nosella A. (2020), The adoption of Industry 4.0 technologies in SMEs: results of an international study. Management Decision, No. 4, Vol. 58, pp. 625–643. DOI: 10.1108/MD-09-2018-0973.
• BCG (Boston Consulting Group) (2021), Embracing Industry 4.0 rediscovering growth, from https://www.bcg.com/capabilities/operations/embracingIndustry-4.0-rediscovering-growth.aspx, accessed March 10, 2021.
• Boyer K.K., Leong G.K., Ward P.T. and Krajewski L.J. (1997), Unlocking the potential of advanced manufacturing technologies, Journal of operations management, Vol. 15, No. 4, pp. 331–347. DOI: 10.1016/S0272-6963(97)00009-0.
• Ceruti A., Marzocca P., Liverani A., and Bil C. (2019), Maintenance in Aeronautics in an Industry 4.0 Context: The Role of Augmented Reality and Additive Manufacturing, Journal of Computational Design and Engineering, No. 4, Vol. 6, pp. 516–526. DOI: 10.1016/j.jcde.2019.02.001.
• Choi T.M., Wallace S.W., and Wang Y. (2018), Big data analytics in operations management, Production and Operations Management, No. 10, Vol. 27, pp. 1868-1883. DOI: 10.1111/poms.12838.
• Dassisti M., Giovannini A., Merla P., Chimienti M., and Panetto H. (2019), An approach to support Industry 4.0 adoption in SMEs using a core-metamodel, Annual Reviews in control, No. 47, pp. 266–274. DOI: 10. 1016/j.arcontrol.2018.11.001.
• Davies R., Coole T., and Smith A. (2017), Review of socio-technical considerations to ensure successful implementation of Industry 4.0, Procedia Manufacturing, Vol. 11, pp. 1288–1295. DOI: 10.1016/ j.promfg.2017.07.256.
• Dalenogare L.S., Benitez G.B., Ayala N.F., and Frank A.G. (2018), The expected contribution of Industry 4.0 technologies for industrial performance, International Journal of Production Economics, Vol. 204, pp. 383–394. DOI: 10.1016/j.ijpe.2018.08.019.
• Deloitte (2020), The Fourth Industrial Revolution At the intersection of readiness and responsibility. https://www2.deloitte.com/content/dam/Deloitte/de/Documents/human-capital/Deloitte_Review_26_Fourth_Industrial_Revolution.pdf, accessed August, 26, 2021.
• Diez-Olivan A., Del Ser J., Galar D., and Sierra B. (2019), Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0, Information Fusion, No. 50, pp. 92–111. DOI: 10.1016/j.inffus.2018.10.005.
• Europarl (2016), European Parliament, Economic and Scientific Policy: Industry 4.0, from http://www.euro parl.europa.eu/RegData/etudes/STUD/2016/570007/IPOL_STU(2016)570007_EN.pdf, accessed March 15, 2020.
• Ferreira W., Armellini F., and Santa-Eulalia L.A. (2020), Simulation in industry 4.0: A state-of-the-art review, Computers & Industrial Engineering, No. 149. DOI: 10.1016/j.cie.2020.106868.
• Guo N. and Leu M. C. (2013), Additive manufacturing: technology, applications and research needs, Frontiers of mechanical engineering, No. 3, Vol. 8, pp. 215–243. DOI: 10.1007/s11465-013-0248-8.
• Hand D.J. (2007), Principles of Data Mining, DrugSafety, No. 30, pp. 621–622. DOI: 10.2165/00002018-200730070-00010.
• Jonsson P. (2000), An empirical taxonomy of advanced manufacturing technology, International Journal of Operations & Production Management, No. 20, Vol. 12, pp. 1446–1474. DOI: 10.1108/014435700103 53103.
• Kamble S.S., Gunasekaran A., and Sharma R. (2018), Analysis of the driving and dependence power of barriers to adopt industry 4.0 in Indian manufacturing industry, Computers in Industry, No. 101, pp. 107– 119. DOI: 10.1016/j.compind.2018.06.004.
• Khanchanapong T., Prajogo D., Sohal A.S., Cooper B.K., Yeung A.C., and Cheng T.C.E. (2014), The unique and complementary effects of manufacturing technologies and lean practices on manufacturing operational performance, International Journal of Production Economics, No. 153, pp. 191–203. DOI: 10.1016/j.ijpe.2014.02.021.
• Lee J., Bagheri B., and Kao H.A. (2015), A cyberphysical systems architecture for industry 4.0-based manufacturing systems, Manufacturing letters, No. 3, pp. 18–23. DOI: 10.1016/j.mfglet.2014.12.001.
• Lu Y. (2017), Industry 4.0: A survey on technologies, applications and open research issues, Journal of industrial information integration, No. 6, pp. 1–10. DOI: 10.1016/j.jii.2017.04.005.
• Luthra S. and Mangla S.K. (2018), Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies, Process Safety and Environmental Protection, No. 117, pp. 168–179. DOI: 10.1016/j.psep.2018.04.018.
• Machadoa C.G., Winrotha M., Carlssonb D., Almströma P., Centerholtb V., and Hallin M. (2019), Industry 4.0 readiness in manufacturing companies: challenges and enablers towards increased digitalization, Procedia Cirp, No. 81, pp. 1113–1118. DOI: 10.1016/j.procir.2019.03.262.
• Maghazei O. and Netland T. (2017, September), Implementation of industry 4.0 technologies: what can we learn from the past? In IFIP International Conference on Advances in Production Management Systems (pp. 135–142). Springer, Cham.
• Meredith J. (1987), The strategic advantages of new manufacturing technologies for small firms, Strategic Management Journal, No. 3, Vol. 8, pp. 249–258. DOI: 10.1016/j.ijpe.2014.02.021.
• Mitchell T.M. (1999), Machine learning and data mining, Communications of the ACM, No. 11, Vol. 42, pp. 30– 36. DOI: 10.1145/319382.319388.
• Müller J.M., Buliga O. and Voigt K.I. (2018), Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0, Technological Forecasting and Social Change, No. 132, pp. 2–17. DOI: 10.1016/j.techfore.2017.12.019.
• Nakayama R.S., de Mesquita Spínola M., and Silva J.R. (2020), Towards I4. 0: a comprehensive analysis of evolution from I3.0, Computers & Industrial Engineering, No. 144, 106453. DOI: 10.1016/j.cie.2020. 106453.
• Newbold P., Carlson W.L., and Thorne B. (2013), Statistics for business and economics. Boston, MA:
• Pearson. Qin J., Liu Y., and Grosvenor R. (2016), A categorical framework of manufacturing for industry 4.0 and beyond, Procedia CIRP, No. 52, pp. 173–178. DOI: 10.1016/j.procir.2016.08.005.
• Raschka S. (2015), Python machine learning, Packt Publishing Ltd.
• Rao T.R., Mitra P., Bhatt R., and Goswami A. (2018), The big data system, components, tools, and technologies: a survey, Knowledge and Information Systems, No. 60, pp. 1–81. DOI: 10.1007/s10115-018-1248-0.
• Raymond L. (2005), Operations management and advanced manufacturing technologies in SMEs, Journal of Manufacturing Technology Management, No. 8, Vol. 16. DOI: 10.1108/17410380510627898.
• Schröder C. (2016), The challenges of industry 4.0 for small and medium-sized enterprises. Friedrich-EbertStiftung: Bonn, Germany. https://library.fes.de/pdffiles/wiso/12683.pdf, accessed March 27, 2020.
• Sohal A.S., Sarros J., Schroder R., and O’neill P. (2006), Adoption framework for advanced manufacturing technologies, International Journal of Production Research, No. 24, Vol. 44, pp. 5225–5246. DOI: 10.1080/00207540600558320.
• Sun H. (2000), Current and future patterns of using advanced manufacturing technologies, Technovation, No. 11, Vol. 20, pp. 631–641. DOI: 10.1016/ S0166-4972(00)00007-9.
• TUSIAD (Turkish Industry and Business Association) (2016), Industry 4.0 in Turkey as an imperative for global competitiveness – An emerging market perspective, from https://tusiad.org/en/reports/item/9011-Industry-40-in-turkey-as-an-imperative-for-globalcompetitiveness, accessed September 17, 2020.
• Udo G.J. and Ehie I.C. (1996), Advanced manufacturing technologies, International Journal of Operations & Production Management, No. 12, Vol. 16. DOI: 10.1108/01443579610151733.
• Xia F., Yang L.T., Wang L., and Vinel A. (2012), Internet of things, International Journal of Communication Systems, No. 9, Vol. 25. DOI: 10.1002/dac.2417.
• Xu L.D., Xu E.L., and Li L. (2018), Industry 4.0: state of the art and future trend, International Journal of Production Research, No. 8, Vol. 56, pp. 2941–2962. DOI: 10.1080/00207543.2018.1444806.
• Yin Y., Stecke K.E. and Li D. (2018), The evolution of production systems from Industry 2.0 through Industry 4.0, International Journal of Production Research, No. 1-2, Vol. 56, pp. 848–861. DOI: 10.1080/00207543.2017.1403664.
• Zhong R.Y., Xu X., Klotz E., and Newman S.T. (2017), Intelligent manufacturing in the context of industry 4.0: a review, Engineering, No. 5, Vol. 3, pp. 616–630. DOI: 10.1016/J.ENG.2017.05.015.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)