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Üretim İşletmelerinin Endüstri 4.0 Entegrasyonunun Veri Zarflama Analizi ile Değerlendirilmesi

Year 2021, Volume: 36 Issue: 3, 637 - 647, 30.09.2021
https://doi.org/10.21605/cukurovaumfd.1005323

Abstract

İnternetin, sistemlerin fiziksel ve siber entegrasyonuyla dönüşümü dördüncü sanayi devrimi olarak anılan Endüstri 4.0 (I4.0) kavramını ortaya çıkarmıştır. Birçok teknolojinin bir araya gelmesi bulut teknolojisi, dijitalleşme, büyük veri ve nesnelerin interneti gibi kavramlar ile yeni bir üretim modeli oluşmaktadır. I4.0 tabanlı bir üretim modeline geçmek isteyen bir işletme için önemli bir süreç ve teknoloji hazırlığı ve altyapı ihtiyacı oluşmaktadır. Bu nedenle, işletmeler dönüşüm için öncelikle bu modeli içeren yönetim şekli, süreç ve teknolojilerine olan uyumu sağlamak zorundadır. Bu çalışmada, farklı üretim firmalarında gerçekleştirilen yüz yüze görüşmeler sonucunda elde edilen verilerle işletmelerin I4.0 entegrasyon
yeteneği analiz edilmiştir. Veriler, veri zarflama analizi ile değerlendirilmiş ve işletmelerin I4.0 uyum yeteneği göreceli olarak saptanmıştır. İşletmeler değerlendirilirken bilgi teknolojileri, araştırma-geliştirme faaliyetleri, müşteri ilişkileri, finansman, kalite yönetimi, planlama, maliyet yönetimi vb. birçok farklı başlık altında analiz edilerek detaylı bir değerlendirme yapılması sağlanmıştır. Yapılan değerlendirme sonucunda incelenen 24 imalat işletmesinin 13’ü dönüşüm için etkin olduğu sonucuna ulaşılmıştır.

References

  • 1. Lee, J., Bagheri, B., Kao, H.A., 2015. A Cyber-physical Systems Architecture for Industry 4.0- Based Manufacturing Systems. Manufacturing Letters, 3, 18-23.
  • 2. Frank, A.G., Dalenogare, L.S., Ayala, N.F., 2019. Industry 4.0 Technologies: Implementation Patterns in Manufacturing Companies. International Journal of Production Economics, 210, 15-26.
  • 3. Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., Vasilakos, A.V., 2017. A Manufacturing Big Data Solution for Active Preventive Maintenance. IEEE Transactions on Industrial Informatics, 13(4), 2039-2047.
  • 4. McFarlane, D., Sarma, S., Chirn, J.L., Wong, C., Ashton, K., 2003. Auto ID Systems and Intelligent Manufacturing Control. Engineering Applications of Artificial Intelligence, 16(4), 365-376.
  • 5. Zhong, R.Y., Xu, X., Klotz, E., Newman, S.T., 2017. Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering, 3(5), 616-630.
  • 6. Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., Harnisch, M., 2015. Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries. Boston Consulting Group, 9(1), 54-89.
  • 7. Schumacher, A., Erol, S., Sihn, W., 2016. A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises, Procedia Cirp, 52, 161-166.
  • 8. Khan, A., Turowski, K., 2016. A Survey of Current Challenges in Manufacturing Industry and Preparation for Industry 4.0. In Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’16), Springer, Cham, 15-26.
  • 9. Crnjac, M., Veža, I., Banduka, N., 2017. From Concept to the Introduction of Industry 4.0. International Journal of Industrial Engineering and Management, 8(1), 21-30.
  • 10. Strandhagen, J.W., Alfnes, E., Strandhagen, J.O., Vallandingham, L.R., 2017. The Fit of Industry 4.0 Applications in Manufacturing Logistics: A Multiple Case Study. Advances in Manufacturing, 5(4), 344-358.
  • 11. Siew, L.W., Fai, L.K., Hoe, L.W., 2018. An Optimal Control on the Efficiency of Technology Companies in Malaysia with Data Envelopment Analysis Model. Journal of Telecommunication. Electronic and Computer Engineering (JTEC), 10(1), 107-111.
  • 12. Fazlollahi, A., Franke, U., 2018. Measuring the Impact of Enterprise Integration on Firm Performance Using Data Envelopment Analysis. International Journal of Production Economics, 200, 119-129.
  • 13. Ghobakhloo, M., 2018. The Future of Manufacturing Industry: A Strategic Roadmap Toward Industry 4.0. Journal of Manufacturing Technology Management.
  • 14. Machado, C.G., Winroth, M., Carlsson, D., Almström, P., Centerholt, V., Hallin, M., 2019. Industry 4.0 Readiness in Manufacturing Companies: Challenges and Enablers Towards Increased Digitalization. Procedia Cirp, 81, 1113-1118.
  • 15.Castelo-Branco, I., Cruz-Jesus, F., Oliveira, T., 2019. Assessing Industry 4.0 Readiness in Manufacturing: Evidence for the European Union. Computers in Industry, 107, 22-32.
  • 16. Dalmarco, G., Ramalho, F.R., Barros, A.C., Soares, A.L., 2019. Providing Industry 4.0 Technologies: The Case of a Production Technology Cluster. The Journal of High Technology Management Research, 30(2), 100355.
  • 17. Szalavetz, A., 2019. Industry 4.0 and Capability Development in Manufacturing Subsidiaries. Technological Forecasting and Social Change, 145, 384-395.
  • 18.Rosin, F., Forget, P., Lamouri, S., Pellerin, R., 2020. Impacts of Industry 4.0 Technologies on Lean Principles. International Journal of Production Research, 58(6), 1644-1661.
  • 19. Zheng, T., Ardolino, M., Bacchetti, A., Perona, M., 2020. The Applications of Industry 4.0 Technologies in Manufacturing Context: A Systematic Literature Review. International Journal of Production Research, 1-33.
  • 20. Machado, C.G., Winroth, M.P., Ribeiro da Silva, E.H.D., 2020. Sustainable Manufacturing in Industry 4.0: an Emerging Research Agenda. International Journal of Production Research, 58(5), 1462-1484.
  • 21.Culot, G., Orzes, G., Sartor, M., Nassimbeni, G., 2020. The Future of Manufacturing: a Delphi-based Scenario Analysis on Industry 4.0. Technological Forecasting and Social Change, 157, 120092.
  • 22.Raj, A., Dwivedi, G., Sharma, A., de Sousa Jabbour, A.B.L., Rajak, S., 2020. Barriers to the Adoption of Industry 4.0 Technologies in the Manufacturing Sector: An Inter-country Comparative Perspective. International Journal of Production Economics, 224, 107546.
  • 23.Bag, S., Gupta, S., Kumar, S., 2021. Industry 4.0 Adoption and 10R Advance Manufacturing Capabilities for Sustainable Development. International Journal of Production Economics, 231, 107844.
  • 24. Eken, M.H., Kale, S., 2011. Measuring Bank Branch Performance Using Data Envelopment Analysis: The Case of Turkish Bank Branches. African Journal of Business Management, 5(3), 889-901.
  • 25.Banker, R.D., 1992. Estimation of Returns to Scale Using Data Envolopment Analysis. European Journal of Operational Research, Vol. 62.
  • 26. Timor, M., 2001. Yöneylem Araştırması ve İşletmecilik Uygulamaları. İstanbul Üniversitesi İşletme Fakültesi Yayınları, İstanbul.
  • 27. Dinçer, E., 2008. Veri Zarflama Analizi’nde Malmquist Endeksiyle Toplam Faktör Verimliliği Değişiminin İncelenmesi ve İMKB Üzerine Bir Uygulama. Marmara Üniversitesi İİBF Dergisi, 15(2), 825-846.
  • 28. Kaya, Y., Doğan E., 2005. Dezenflasyon Sürecinde Türk Bankacılık Sektöründe Etkinliğin Gelişimi. BDDK, ARD Çalışma Raporları.
  • 29. Aslankaraoğlu, N., 2006. Veri Zarflama Analizi ve Temel Bileşenler Analizi ile AB Ülkelerinin Sıralaması. Gazi Üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, Ankara, 142.
  • 30.Çavmak, Ş., 2017. Sağlık Hizmetlerinde Veri Zarflama Analizi ve Modelleri. Sağlık Yönetimi Dergisi, 1(1), 35-47.
  • 31. Özden, Ü., 2008. Veri Zarflama Analizi (VZA) ile Türkiye’deki Vakıf Üniversitelerinin Etkinliğinin Ölçülmesi. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 37(2), 167-185.
  • 32. Norman, M., Stoker, B., 1991. Data Envelopment Analysis: The Assessment of Performance, John Wiley & Sons, Inc.

Evaluation of Industry 4.0 Integration of Manufacturing Enterprises with Data Envelopment Analysis

Year 2021, Volume: 36 Issue: 3, 637 - 647, 30.09.2021
https://doi.org/10.21605/cukurovaumfd.1005323

Abstract

The internet and the transformation of physical and cyber systems’ integration have created Industry 4.0 (I4.0), referred to as the fourth industrial revolution. Combining many technologies forms a new production model with concepts such as cloud technology, digitalization, big data, and the internet of things. A necessary process and technology preparation, and infrastructure need arise for an enterprise that wants to switch to an I4.0-based production model. For this reason, businesses must first ensure compliance with the management style, processes, and technologies that include this model for transformation. In this study, the I4.0 integration capability of the enterprises was analyzed with the data
obtained from face-to-face interviews conducted in different manufacturing companies. The data were evaluated with the Data Envelopment Analysis technique, and the I4.0 compliance capability of the enterprises was determined relatively. A detailed evaluation has been achieved by analyzing various aspects such as information technologies, R&D activities, customer relations, financing, quality management, planning, and cost management. As a result of the evaluation, it was concluded that 13 of the 24 manufacturing enterprises examined were effective for transformation.

References

  • 1. Lee, J., Bagheri, B., Kao, H.A., 2015. A Cyber-physical Systems Architecture for Industry 4.0- Based Manufacturing Systems. Manufacturing Letters, 3, 18-23.
  • 2. Frank, A.G., Dalenogare, L.S., Ayala, N.F., 2019. Industry 4.0 Technologies: Implementation Patterns in Manufacturing Companies. International Journal of Production Economics, 210, 15-26.
  • 3. Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., Vasilakos, A.V., 2017. A Manufacturing Big Data Solution for Active Preventive Maintenance. IEEE Transactions on Industrial Informatics, 13(4), 2039-2047.
  • 4. McFarlane, D., Sarma, S., Chirn, J.L., Wong, C., Ashton, K., 2003. Auto ID Systems and Intelligent Manufacturing Control. Engineering Applications of Artificial Intelligence, 16(4), 365-376.
  • 5. Zhong, R.Y., Xu, X., Klotz, E., Newman, S.T., 2017. Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering, 3(5), 616-630.
  • 6. Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., Harnisch, M., 2015. Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries. Boston Consulting Group, 9(1), 54-89.
  • 7. Schumacher, A., Erol, S., Sihn, W., 2016. A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises, Procedia Cirp, 52, 161-166.
  • 8. Khan, A., Turowski, K., 2016. A Survey of Current Challenges in Manufacturing Industry and Preparation for Industry 4.0. In Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’16), Springer, Cham, 15-26.
  • 9. Crnjac, M., Veža, I., Banduka, N., 2017. From Concept to the Introduction of Industry 4.0. International Journal of Industrial Engineering and Management, 8(1), 21-30.
  • 10. Strandhagen, J.W., Alfnes, E., Strandhagen, J.O., Vallandingham, L.R., 2017. The Fit of Industry 4.0 Applications in Manufacturing Logistics: A Multiple Case Study. Advances in Manufacturing, 5(4), 344-358.
  • 11. Siew, L.W., Fai, L.K., Hoe, L.W., 2018. An Optimal Control on the Efficiency of Technology Companies in Malaysia with Data Envelopment Analysis Model. Journal of Telecommunication. Electronic and Computer Engineering (JTEC), 10(1), 107-111.
  • 12. Fazlollahi, A., Franke, U., 2018. Measuring the Impact of Enterprise Integration on Firm Performance Using Data Envelopment Analysis. International Journal of Production Economics, 200, 119-129.
  • 13. Ghobakhloo, M., 2018. The Future of Manufacturing Industry: A Strategic Roadmap Toward Industry 4.0. Journal of Manufacturing Technology Management.
  • 14. Machado, C.G., Winroth, M., Carlsson, D., Almström, P., Centerholt, V., Hallin, M., 2019. Industry 4.0 Readiness in Manufacturing Companies: Challenges and Enablers Towards Increased Digitalization. Procedia Cirp, 81, 1113-1118.
  • 15.Castelo-Branco, I., Cruz-Jesus, F., Oliveira, T., 2019. Assessing Industry 4.0 Readiness in Manufacturing: Evidence for the European Union. Computers in Industry, 107, 22-32.
  • 16. Dalmarco, G., Ramalho, F.R., Barros, A.C., Soares, A.L., 2019. Providing Industry 4.0 Technologies: The Case of a Production Technology Cluster. The Journal of High Technology Management Research, 30(2), 100355.
  • 17. Szalavetz, A., 2019. Industry 4.0 and Capability Development in Manufacturing Subsidiaries. Technological Forecasting and Social Change, 145, 384-395.
  • 18.Rosin, F., Forget, P., Lamouri, S., Pellerin, R., 2020. Impacts of Industry 4.0 Technologies on Lean Principles. International Journal of Production Research, 58(6), 1644-1661.
  • 19. Zheng, T., Ardolino, M., Bacchetti, A., Perona, M., 2020. The Applications of Industry 4.0 Technologies in Manufacturing Context: A Systematic Literature Review. International Journal of Production Research, 1-33.
  • 20. Machado, C.G., Winroth, M.P., Ribeiro da Silva, E.H.D., 2020. Sustainable Manufacturing in Industry 4.0: an Emerging Research Agenda. International Journal of Production Research, 58(5), 1462-1484.
  • 21.Culot, G., Orzes, G., Sartor, M., Nassimbeni, G., 2020. The Future of Manufacturing: a Delphi-based Scenario Analysis on Industry 4.0. Technological Forecasting and Social Change, 157, 120092.
  • 22.Raj, A., Dwivedi, G., Sharma, A., de Sousa Jabbour, A.B.L., Rajak, S., 2020. Barriers to the Adoption of Industry 4.0 Technologies in the Manufacturing Sector: An Inter-country Comparative Perspective. International Journal of Production Economics, 224, 107546.
  • 23.Bag, S., Gupta, S., Kumar, S., 2021. Industry 4.0 Adoption and 10R Advance Manufacturing Capabilities for Sustainable Development. International Journal of Production Economics, 231, 107844.
  • 24. Eken, M.H., Kale, S., 2011. Measuring Bank Branch Performance Using Data Envelopment Analysis: The Case of Turkish Bank Branches. African Journal of Business Management, 5(3), 889-901.
  • 25.Banker, R.D., 1992. Estimation of Returns to Scale Using Data Envolopment Analysis. European Journal of Operational Research, Vol. 62.
  • 26. Timor, M., 2001. Yöneylem Araştırması ve İşletmecilik Uygulamaları. İstanbul Üniversitesi İşletme Fakültesi Yayınları, İstanbul.
  • 27. Dinçer, E., 2008. Veri Zarflama Analizi’nde Malmquist Endeksiyle Toplam Faktör Verimliliği Değişiminin İncelenmesi ve İMKB Üzerine Bir Uygulama. Marmara Üniversitesi İİBF Dergisi, 15(2), 825-846.
  • 28. Kaya, Y., Doğan E., 2005. Dezenflasyon Sürecinde Türk Bankacılık Sektöründe Etkinliğin Gelişimi. BDDK, ARD Çalışma Raporları.
  • 29. Aslankaraoğlu, N., 2006. Veri Zarflama Analizi ve Temel Bileşenler Analizi ile AB Ülkelerinin Sıralaması. Gazi Üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, Ankara, 142.
  • 30.Çavmak, Ş., 2017. Sağlık Hizmetlerinde Veri Zarflama Analizi ve Modelleri. Sağlık Yönetimi Dergisi, 1(1), 35-47.
  • 31. Özden, Ü., 2008. Veri Zarflama Analizi (VZA) ile Türkiye’deki Vakıf Üniversitelerinin Etkinliğinin Ölçülmesi. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 37(2), 167-185.
  • 32. Norman, M., Stoker, B., 1991. Data Envelopment Analysis: The Assessment of Performance, John Wiley & Sons, Inc.
There are 32 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Adem Erik This is me 0000-0001-6840-0586

Yusuf Kuvvetli This is me 0000-0002-9817-1371

Publication Date September 30, 2021
Published in Issue Year 2021 Volume: 36 Issue: 3

Cite

APA Erik, A., & Kuvvetli, Y. (2021). Üretim İşletmelerinin Endüstri 4.0 Entegrasyonunun Veri Zarflama Analizi ile Değerlendirilmesi. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 36(3), 637-647. https://doi.org/10.21605/cukurovaumfd.1005323