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SWOT Analysis for Industry 4.0

Yıl 2024, Cilt: 8 Sayı: 3, 863 - 877, 29.09.2024
https://doi.org/10.30586/pek.1435359

Öz

SWOT Analysis for Industry 4.0 serves as a strategic framework that assesses the strengths, weaknesses, opportunities, and threats associated with the adoption and implementation of Industry 4.0. This transformative concept represents the fourth industrial revolution, characterized by the seamless integration of information technology (IT) systems with physical systems, resulting in cyber-physical systems. The overarching goal of Industry 4.0 is to enhance industries by making them more intelligent, dynamic, and flexible through real-time data sharing and the interconnection of physical objects. By comprehending the SWOT factors, industrial practitioners can strategically implement Industry 4.0, leveraging its strengths and opportunities while proactively addressing weaknesses and mitigating potential threats. Industry 4.0 is advanced in many sectors and business areas. In this study, a literature review has been conducted on Industry 4.0 and its main components, including Cyber-Physical Systems, the Internet of Things, Smart Factories, and Big Data. The results of the literature review indicate that a SWOT analysis is used to assess certain features of Industry 4.0 during its implementation. The strengths, weaknesses, opportunities, and threats related to Industry 4.0 are identified. By considering these four groups of factors, industrial practitioners can understand how to implement Industry 4.0. Additionally, industrial practitioners can make strategic decisions that mitigate the impacts of the threats and weaknesses brought by Industry 4.0 by leveraging its strengths and opportunities.

Kaynakça

  • Aceto, G., Persico, V., & Pescape, A. (2019). A survey on information and communication Technologies for industry4.0: state-of-the art, taxonomies, perspectives, and challenges. IEEE Communications Surveys andTutorials, vol. 21 no. 4, pp. 3467-3501
  • Akta¸ S. F., Çeken, C., & Erdemli, Y. E. (2016). Nesnelerin interneti teknolojisinin biyomedikal alanındaki uygulamaları. Düzce Üniv. Bilim ve Teknol. Derg., 4, 37–54.
  • Aitzhan, N. Z. & Svetinovic, D. (2018). Security and privacy in decentralized energy trading through multi-signatures, blockchain and anonymous messaging streams, IEEE Trans. Dependable Secure Comput., vol. 15, no. 5, pp. 840–852.
  • Ammar, M., Haleem, A., Javaid, M., Walia, R., Bahl, S. (2012). Improving material quality management and manufacturing organisations system through Industry 4.0 technologies Mater. Today: Proceedings, 45 (6), pp. 5089-5096
  • Avventuroso, G., Foresti, R., & Morosini, E. F. (2017). Production Paradigms for Additive Manufacturing Systems: a Simulation-based Analysis.
  • Barnatt, C. (2013). 3D printing: The Next Industrial Revolution. In ABD: Create Space Independent Publishing Platform; CreateSpace Independent Publishing Platform: Scotts Valley, CA, USA, p. 11120.
  • Batalha, M. O., & Rachid, A. (2008). Estratégia e organizações. In M. O. Batalha & A. Rachid, Introduction in production engineering. Rio de Janeiro: Elseiver
  • Brettel, M., Friederichsen, N., Keller, M., Rosenberg, M. (2014). How virtualization, decentralization and network building change the manufacturing landscape: An industry 4.0 perspective. Int. J. Sci. Eng. Technol, 8, 37–44.
  • Bojana, N., Jelena, I., Nikola, S., Branislav, S., & Aleksandar, R. (2017). Predictive Manufacturing Systems In Industry 4.0: Trends, Benefits And Challenges. International Symposium on Intelligent Manufacturing and Automation.
  • Bumblauskas, D., Nold, H., Bumblauskas, P., & Igou, A. (2017). Big data analytics: transforming data to action. Business Process Management Journal, 23(3), 703–720. https://doi.org/10.1108/bpmj-03-2016-0056
  • Campos, J., Sharma, P., Gabiria, U. G., Jantunen, E., & Baglee, D. (2017). A Big Data Analytical Architecture for the Asset Management. Procedia CIRP, 64, 369–374. https://doi.org/10.1016/j.procir.2017.03.019
  • Campos, J., Sharma, P., Jantunen, E., Baglee, D., & Fumagalli, L. (2017). Business Performance Measurements in Asset Management with the Support of Big Data Technologies. Management Systems in Production Engineering, 25(3), 143–149. https://doi.org/10.1515/mspe-2017-0021
  • Carvalho, N., Chaim, O., Cazarini, E., & Gerolamo, M. (2018). Manufacturing in the fourth industrial revolution: A positive prospect in sustainable manufacturing. Procedia Manufacturing, 21, 671-678
  • Cross, T. (2018). Human obsolescence, science and technology. In The Economist: The World in 2018; The Economist Newspaper Limited: London, UK, 2018. 114. Shaywitz, S. A new and complete science-based program for reading problems at any level. Overcoming Dyslexia 2005, 28, 575.
  • Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics, 204, 383–394.
  • Davies, R., Coole, T., & Smith, A. (2017). Review of socio-technical considerations to ensure successful implementation of Industry 4.0. Procedia Manufacturing, 11, 1288-1295. http://dx.doi.org/10.1016/j.promfg.2017.07.256
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  • Fahmideh, M., & Beydoun, G. (2019). Big data analytics architecture design—An application in manufacturing systems. Computers & Industrial Engineering, 128, 948–963. https://doi.org/10.1016/j.cie.2018.08.004
  • 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. https://doi.org/10.1016/j.ijpe.2019.01.004
  • Gao, J., Koronios, A., & Selle, S. (2015). Towards a process view on critical success factors in big data analytics projects. In: 21st Americas Conference on Information Systems (AMCIS 2015). Fajardo, Puerto Rico, 13-15 August 2015.
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Endüstri 4.0 için SWOTAnalizi

Yıl 2024, Cilt: 8 Sayı: 3, 863 - 877, 29.09.2024
https://doi.org/10.30586/pek.1435359

Öz

Endüstri 4.0 için SWOT Analizi, Endüstri 4.0'ın benimsenmesi ve uygulanmasıyla ilişkili güçlü yönleri, zayıf yönleri, fırsatları ve tehditleri değerlendiren stratejik bir çerçeve görevi görür. Bu dönüştürücü konsept, bilgi teknolojisi (IT) sistemlerinin fiziksel sistemlerle kusursuz entegrasyonuyla karakterize edilen ve siber-fiziksel sistemlerle sonuçlanan dördüncü sanayi devrimini temsil ediyor. Endüstri 4.0'ın genel hedefi, gerçek zamanlı veri paylaşımı ve fiziksel nesnelerin birbirine bağlanması yoluyla endüstrileri daha akıllı, dinamik ve esnek hale getirerek geliştirmektir. Endüstriyel uygulayıcılar, SWOT faktörlerini anlayarak Endüstri 4.0'ı stratejik olarak uygulayabilir, güçlü yönlerinden ve fırsatlarından yararlanabilir, zayıf yönlerini proaktif olarak ele alabilir ve potansiyel tehditleri azaltabilir. Endüstri 4.0 birçok sektörde ve iş alanında gelişmiş durumdadır. Son zamanlarda yeni iş modelleri sağlayan, süreçlerin etkinliğini ve verimliliğini artıran bu trend bazı teknolojileri etkin olarak kullanmaktadır. Bu çalışmada Endüstri 4.0 ve ana bileşenleri olan Siber-Fiziksel Sistemler, Nesnelerin İnterneti, Akıllı Fabrikalar ve Büyük Veri üzerine literatür taraması yapılmıştır. Literatör taraması sonuçunda, endüstri 4.0'ın uygulanmasında SWOT analizi kullanılarak endüstri 4.0'ın bazı özellikleri değerlendirilmektedir. Endüstri 4.0 ile ilgili güçlü yönleri, zayıf yönleri, fırsatları ve tehditleri tanımlanmaktadır. Bu dört faktör grubunun dikkate alınmasıyla endüstriyel uygulayıcılar Endüstri 4.0'ın nasıl uygulanacağını anlayabilirler. Ayrıca endüstriyel uygulayıcılar, endüstri 4.0'ın sunduğu güçlü yanları/fırsatları kullanarak endüstri 4.0'ın getirdiği tehditlerin/zayıflıkların etkisini azaltacak stratejik kararlar alabilirler.

Kaynakça

  • Aceto, G., Persico, V., & Pescape, A. (2019). A survey on information and communication Technologies for industry4.0: state-of-the art, taxonomies, perspectives, and challenges. IEEE Communications Surveys andTutorials, vol. 21 no. 4, pp. 3467-3501
  • Akta¸ S. F., Çeken, C., & Erdemli, Y. E. (2016). Nesnelerin interneti teknolojisinin biyomedikal alanındaki uygulamaları. Düzce Üniv. Bilim ve Teknol. Derg., 4, 37–54.
  • Aitzhan, N. Z. & Svetinovic, D. (2018). Security and privacy in decentralized energy trading through multi-signatures, blockchain and anonymous messaging streams, IEEE Trans. Dependable Secure Comput., vol. 15, no. 5, pp. 840–852.
  • Ammar, M., Haleem, A., Javaid, M., Walia, R., Bahl, S. (2012). Improving material quality management and manufacturing organisations system through Industry 4.0 technologies Mater. Today: Proceedings, 45 (6), pp. 5089-5096
  • Avventuroso, G., Foresti, R., & Morosini, E. F. (2017). Production Paradigms for Additive Manufacturing Systems: a Simulation-based Analysis.
  • Barnatt, C. (2013). 3D printing: The Next Industrial Revolution. In ABD: Create Space Independent Publishing Platform; CreateSpace Independent Publishing Platform: Scotts Valley, CA, USA, p. 11120.
  • Batalha, M. O., & Rachid, A. (2008). Estratégia e organizações. In M. O. Batalha & A. Rachid, Introduction in production engineering. Rio de Janeiro: Elseiver
  • Brettel, M., Friederichsen, N., Keller, M., Rosenberg, M. (2014). How virtualization, decentralization and network building change the manufacturing landscape: An industry 4.0 perspective. Int. J. Sci. Eng. Technol, 8, 37–44.
  • Bojana, N., Jelena, I., Nikola, S., Branislav, S., & Aleksandar, R. (2017). Predictive Manufacturing Systems In Industry 4.0: Trends, Benefits And Challenges. International Symposium on Intelligent Manufacturing and Automation.
  • Bumblauskas, D., Nold, H., Bumblauskas, P., & Igou, A. (2017). Big data analytics: transforming data to action. Business Process Management Journal, 23(3), 703–720. https://doi.org/10.1108/bpmj-03-2016-0056
  • Campos, J., Sharma, P., Gabiria, U. G., Jantunen, E., & Baglee, D. (2017). A Big Data Analytical Architecture for the Asset Management. Procedia CIRP, 64, 369–374. https://doi.org/10.1016/j.procir.2017.03.019
  • Campos, J., Sharma, P., Jantunen, E., Baglee, D., & Fumagalli, L. (2017). Business Performance Measurements in Asset Management with the Support of Big Data Technologies. Management Systems in Production Engineering, 25(3), 143–149. https://doi.org/10.1515/mspe-2017-0021
  • Carvalho, N., Chaim, O., Cazarini, E., & Gerolamo, M. (2018). Manufacturing in the fourth industrial revolution: A positive prospect in sustainable manufacturing. Procedia Manufacturing, 21, 671-678
  • Cross, T. (2018). Human obsolescence, science and technology. In The Economist: The World in 2018; The Economist Newspaper Limited: London, UK, 2018. 114. Shaywitz, S. A new and complete science-based program for reading problems at any level. Overcoming Dyslexia 2005, 28, 575.
  • Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics, 204, 383–394.
  • Davies, R., Coole, T., & Smith, A. (2017). Review of socio-technical considerations to ensure successful implementation of Industry 4.0. Procedia Manufacturing, 11, 1288-1295. http://dx.doi.org/10.1016/j.promfg.2017.07.256
  • Dillon, T., Wu, C., Chang, E. (2010). Cloud Computing: Issues and Challenges. In IEEE International Conference on Advanced Information Networking and Applications, 27–33.
  • Fahmideh, M., & Beydoun, G. (2019). Big data analytics architecture design—An application in manufacturing systems. Computers & Industrial Engineering, 128, 948–963. https://doi.org/10.1016/j.cie.2018.08.004
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  • Gao, J., Koronios, A., & Selle, S. (2015). Towards a process view on critical success factors in big data analytics projects. In: 21st Americas Conference on Information Systems (AMCIS 2015). Fajardo, Puerto Rico, 13-15 August 2015.
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  • Moktadir, M. A., Ali, S. M., Paul, S. K., & Shukla, N. (2019). Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh. Computers & Industrial Engineering, 128, 1063–1075. https://doi.org/10.1016/j.cie.2018.04.013
  • Mourtzis, D., Vlachou, E., & Milas, N. (2016). Industrial Big Data as a Result of IoT Adoption in Manufacturing. Procedia CIRP, 55, 290–295. https://doi.org/10.1016/j.procir.2016.07.038
  • Mousannif, H., Sabah, H., Douiji, Y., & Oulad S. Y. (2016). Big data projects: just jump right in! International Journal of Pervasive Computing and Communications, 12(2), 260–288. https://doi.org/10.1108/ijpcc-04-2016-0023
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  • Raj, R., Lee, I., & Stankovic, J. (2010), Cyber-Physical Systems: The Next Computing Revolution. Design Automation Conference (DAC), pp. 731–736.
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Toplam 92 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgi Sistemleri Organizasyonu ve Yönetimi
Bölüm Makaleler
Yazarlar

Begumhan Turgut 0000-0002-7594-9128

Erken Görünüm Tarihi 25 Eylül 2024
Yayımlanma Tarihi 29 Eylül 2024
Gönderilme Tarihi 11 Şubat 2024
Kabul Tarihi 18 Eylül 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 8 Sayı: 3

Kaynak Göster

APA Turgut, B. (2024). Endüstri 4.0 için SWOTAnalizi. Politik Ekonomik Kuram, 8(3), 863-877. https://doi.org/10.30586/pek.1435359

Bu eser Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı ile lisanslanmıştır.