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SWOT Analysis for Industry 4.0

Yıl 2024, Cilt: 8 Sayı: 3, 863 - 877
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
  • 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
  • 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.
  • García, S. G., & García, M. G. (2019). Industry 4.0 implications in production and maintenance management: An overview. Procedia Manufacturing, 41, 415–422. https://doi.org/10.1016/j.promfg.2019.09.027
  • Ghobakhloo, M. (2018). The future of manufacturing industry: a strategic roadmap toward Industry 4.0. Journal of Manufacturing Technology Management, 29(6), 910-936.
  • Givehchi, O., Henning, T., & Juergen, J. (2013). Conference Program. In Conference on Emerging Technologies & Factory Automation (ETFA), 1–37.
  • Golzer, Cato, & Amberg, M. (2015). Data processing requirements of industry 4.0-use cases for big data applications. In: ECIS 2015 Research-in-Progress Papers, Munster Germany, 26-29 May, 61.
  • Hamzeh, R., Zhong, R., & Xu, X. W. (2018). A Survey Study on Industry 4.0 for New Zealand Manufacturing. Procedia Manufacturing, 26, 49–57. https://doi.org/10.1016/j.promfg.2018.07.007
  • Hämäläinen, E., & Inkinen, T. (2019). Industrial applications of big data in disruptive innovations supporting environmental reporting. Journal of Industrial Information Integration, 16, 100105. https://doi.org/10.1016/j.jii.2019.100105
  • Harrisson, J. P. (2010). Strategic planning and SWOT analysis. In J. P. Harrison, Essentials ofstrategic planning in healthcare. Chicago: Health Administration Press.
  • Hathaliya, J., Sharma, P., Tanwar. S., & Gupta, R. (2019). Blockchain-based remote patient monitoring in healthcare 4.0. IEEE 9th International Conference On Advanced Computing (IACC), pp. 87-91.
  • Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23-34. http://dx.doi.org/10.1016/j.compind.2017.04.002.
  • Huxtable, J., & Schaefer, D. (2016). On servitization of the manufacturing industry in the UK. Procedia CIRP, 52, 46-51. http://dx.doi.org/10.1016/j.procir.2016.07.042.
  • Jukić, N., Sharma, A., Nestorov, S., & Jukić, B. (2015). Augmenting Data Warehouses with Big Data. Information Systems Management, 32(3), 200–209. https://doi.org/10.1080/10580530.2015.1044338
  • Kagermann, H., Helbig, J., Schuh, G., & Wahlster, W. (2013). Securing the future of German manufacturing industry. Recommendations for implementing the strategic initiative Industrie 4.0. Final report of the Industrie 4.0 Working Group München: Acatech.
  • Kamble, S. K., Gunasekaran, A., & 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, 117, 408-425. http://dx.doi.org/10.1016/j.p
  • Kamble, S., Gunasekaran, A., & Dhone, N.C. (2020), Industry 4.0 and leanmanufacturing practices for sustainable organisational performance in Indian manufacturing companies. International Journal of Production Research, vol. 58, no.5, pp. 1319-1337.
  • Kneissl, W. (2013). 3D printing 2014-2025: Technologies, markets, players. In ABD: ID Tech Ex; IDTechEX: Boston, MA, USA, p. 4.
  • Kopetz, H. (2011). Internet of things. In Real-Time Systems; Springer: New York, NY, USA, pp. 307–323.
  • Lakshmi, B. & Raghunandhan, G. (2011). A conceptual overview of data mining. In: National conference on innovations in emerging technology, Erode India, 17-18 February, 27-35.
  • Landriscina, F. (2013). Simulation and Learning a Model-Centered Approach; Springer: New York, NY, USA.
  • Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239-242.
  • Lee, J. (2013). Industry 4.0 in Big Data Environment. German Harting Magazine, pp.8–10.
  • 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.
  • Lee, J., Lapira, E., Bagheri, B., & Kao, H. A. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1(1), pp.38–41. Available at: http://linkinghub.elsevier.com/retrieve/pii/S22138463130001 14 [Accessed July 14, 2014].
  • Lee, G. M., Crespi, N., Choi, J.K., & Boussard, M. (2013). Internet of things. In Evolution of Telecommunication Services; Springer: Berlin/Heidelberg, Germany, pp. 257–282.
  • Lee, J., Ardakani, H. D., Yang, S., & Bagheri, B. (2015). Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance & Service Innovation. Procedia CIRP, 38, 3 7. https://doi.org/10.1016/j.procir.2015.08.026
  • Liang, X., Zhao, J., Shetty, S & Li, D. (2017). Towards data assurance and resilience in IoT using blockchain, in Proc. IEEE Mil. Commun. Conf., Baltimore, MD, USA, pp. 261–266.
  • Li, Z., Kang, J., Yu, R., Ye, D., Deng, Q. & Zhang, Y. (2018). Consortium blockchain for secure energy trading in industrial Internet of Things, IEEE Trans. Ind. Inform., vol. 14, no. 8, pp. 3690–3700.
  • Longo, F., Nicoletti, L., & Padovano, A. (2017). Smart operators in Industry 4.0: A human centred approach to enhance operators' capabilities and competencies within the new smart factory context. Computers & Industrial Engineering, 113, 144–159.
  • Masood, T., & Egger, J. (2019). Augmented reality in support of Industry 4.0: Implementation challenges and success factors. Robotics and Computer Integrated Manufacturing, 58, 181–195. https://doi.org/10.1016/j.rcim.2019.02.003.
  • McKinsey, C. (2016). Public Sector Information Technology. Chicago: McKinsey & Company.
  • Mladen, A.V. (2008). Cloud computing-issues, research and implementations. Journal of Computing and Information Technology, 16(4):235-246.
  • Miller, D. (2018). Blockchain and the Internet of Things in the industrial sector, IT Professional, vol. 20, no. 3, pp. 15–18.
  • Müller, J. M., & Voigt, K.-I. (2018). The Impact of Industry 4.0 on Supply Chains in Engineer-to-Order Industries - An Exploratory Case Study. IFAC-Papers On Line, 51(11), 122–127. https://doi.org/10.1016/j.ifacol.2018.08.245.
  • Müller, J. M. (2019). Assessing the barriers to Industry 4.0 implementation from a workers’ perspective. IFAC Papers On Line, 52(13), 2189–2194. https://doi.org/10.1016/j.ifacol.2019.11.530
  • 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
  • Nübler, I. (2016, November). New technologies: A jobless future or a golden age of job creation? Retrieved May 19, 2018, from http://www.ilo.org/wcmsp5/groups/public/---dgreports/--inst/documents/publication/wcms_544189.pdf
  • Raj, R., Lee, I., & Stankovic, J. (2010), Cyber-Physical Systems: The Next Computing Revolution. Design Automation Conference (DAC), pp. 731–736.
  • Rajkumar, R.R ., I. Lee, I ., Sha, L & J. Stankovic, J. 2010, Cyber-physical systems: The next computing revolution, in Proceedings of the 47th Design Automation Conference, pp. 731–736.
  • Rajpurohit, L., & Arvid, V. K. (2016). Industrie 4.0: An overview. International Journal of Advance Engineering and Research Development, 3, 535–541
  • Reddy, B. R. (2018). A Comprehensive Literature Review on Data Analytics in IIoT (Industrial Internet of Things). HELIX, 8(1), 2757–2764. https://doi.org/10.29042/2018-2757-2764
  • Rehman, M. H., Yaqoob, I., Salah, K., Imran, M., Jayaraman, P. P., & Perera, C. (2019). The role of big data analytics in industrial Internet of Things. Future Generation Computer Systems, 99, 247–259. https://doi.org/10.1016/j.future.2019.04.020
  • Industry 4.0 in Management Studies: A Systematic Literature Review
  • Piccarozzi, M., Aquilani,B., & Gatti, C. (2018).Industry 4.0 in Management Studies: A Systematic Literature Review, 1-24
  • Sarma, S., Dutt, N., Gupta, P., Venkatasubramanian, N & Nicolau, A. (2015). Cyberphysical-system-on-chip (cpsoc): A self-aware mpsoc paradigm with cross-layer virtual sensing and actuation, in Design, Automation Test in Europe Conference Exhibition, pp. 625–628
  • Schwab, K. (2016). The fourth industrial revolution Geneva: World Economic Forum.
  • Scott, J., Gupta, N., Wember, C., & Newsom, S., & Caffrey, T. (2012). Additive Manufacturing: Status and Opportunities, 1-29.
  • Shafiq, S. I., Sanin, C., Szczerbicki, E., & Toro, C. (2015). Virtual Engineering Object / Virtual Engineering Process: A specialized form of Cyber Physical System for Industrie 4.0. Procedia Computer Science, 60, 1146-1155. https://doi.org/10.1016/j.procs.2015.08.166
  • Shamim, S. E. A. (2016). Management Approaches for Industry 4.0 – A human resources management perspective. IEEE Congress on Evolutionary Computation (CEC), https://www.researchgate.net/publication/311251654, pp. 5309-5314.
  • Shank, P. 2016. The Fourth Industrial Revolution: What Happens With Employment? https://www.td.org/Publications/Blogs/Learning-Executive Blog/2016/05/The-Fourth Industrial-Revolution-What-Happens-withEmployment. Accessed 10-9-2019.
  • Shava, E., & Hofisi, C. (2017). Challenges and opportunities for public administration in the Fourth Industrial Revolution. African Journal of Public Affairs, 9(9), 203-215.
  • Strupczewski, L. (2015, January 10). Big Data or Big Confusion? Retrieved May 29, 2018, from https://www.business2community.com/big-data/big-data-bigconfusion-01118253
  • Sreedharan, V. R. & Unnikrishnan, A. (2017). Moving towards industry 4.0: a systematic review. International Journal of Pure and
  • Applied Mathematics, 117(20), 929-936.
  • Sung, T. K. (2018). Industry 4.0: A Korea perspective. Technological Forecasting and Social Change, 132, 40–45. https://doi.org/10.1016/j.techfore.2017.11.005
  • Teslya, N & Ryabchikov, I. (2017). Blockchain-based platform architecture for industrial IoT, in Proc. 21st Conf. of Open Innovations Assoc., Helsinki, Finland, pp. 321–329.
  • Thompson, A. A., Strickland, A. J. & Gamble, J. E. (2007). Crafting and Executing Strategy Concepts and Cases, (15th Edition), USA: McGraw-Hill/Irwin
  • Xu, L. D., & Duan, L. (2018). Big data for cyber physical systems in industry 4.0: a survey. Enterprise Information Systems, 13(2), 148–169. https://doi.org/10.1080/17517575.2018.1442934
  • Walwei, U. (2016, September). Digitalization and structural labour market problems: The case of Germany. Retrieved May 19, 2018, from http://www.ilo.org/wcmsp5/groups/public/--dgreports/---inst/documents/publication/wcms_522355.pdf
  • Wang, X., Ong, S. K., & Nee, A. Y. C. (2016). A comprehensive survey of augmented reality assembly research. Advances in Manufacturing, 4, 1–22.
  • Wang et al. (2017, November 28). Industry 4.0: A way from mass customization to mass personalization production. Retrieved June 16, 2018, from https://link.springer.com/article/10.1007/s 40436-017-0204-7
  • WCED, S. W. S. (1987). World commission on environment and development. Our common future, 17, 1-91.
  • Witkowski, K. (2017). Internet of things, big data, Industry 4.0 – innovative solutions in logistics and supply chains management. Procedia Engineering, 182, 763-769
  • World Economic Forum (WEF). (2016). Five Million Jobs by 2020: The Real Challenge of the Fourth Industrial Revolution. Available at https://www.weforum.org/press/2016/01/five-million-jobs-by-2020-the-real-challenge-of-the-fourth-industrial-revolution. Accessed 15-9-2019.
  • Yang, G., Xie, L., Mantsalo, M., Zhou, X., Pang, Z., Xu, L., Kao-Walter, S., Chen, Q., & Zheng, L. (2014). A Health-IoT Platform Based on the Integration of Intelligent Packaging, Unobtrusive Bio-Sensor and Intelligent Medicine Box. IEEE Transactions on Industrial informatics, 10, 2180–2191.
  • Yin, S., & Kaynak, O. (2015). Big Data for Modern Industry: Challenges and Trends, Proc. of IEEE, vol. 103, no. 2, pp. 143–146.
  • Ylijoki, O., & Porras, J. (2018). A recipe for big data value creation. Business Process Management Journal, 25(5), 1085–1100. https://doi.org/10.1108/bpmj-03-2018-0082
  • Verl, A. Robotics & Industrie 4.0. (2017). In IFR-International Federation of Robotics. Available online: https://scholar.archive.org/work/q26zts5rhjd5jhfbjzv6dhi62u/access/wayback/https://ifr.org/img/uploads/Presentation_Industry_i4.0_Rob_Alexander_VERL_29_9_16.pdf (accessed on 7 February 2022).
  • Zhang, Y., Ma, S., Yang, H., Lv, J., & Liu, Y. (2018). A big data driven analytical framework for energyintensive manufacturing industries. Journal of Cleaner Production, 197, 57–72.
  • https://doi.org/10.1016/j.jclepro.2018.06.170
  • Zheng, T., Ardolino, M., Bacchetti, A. & Perona, M. (2021). The applications of Industry4.0 technologies inmanufacturing context: asystematic literatüre review. International Journal of Production Research, vol. 59, no.6, pp.1922-1954.
  • Zhou, C., Damiano, N., Whisner, B., & Reyes, M. D. (2017). Industrial Internet of Things: (IIoT) applications in underground coal mines. Mining Engineering, 69(12):50-56.

Endüstri 4.0 için SWOTAnalizi

Yıl 2024, Cilt: 8 Sayı: 3, 863 - 877
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
  • 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.
  • García, S. G., & García, M. G. (2019). Industry 4.0 implications in production and maintenance management: An overview. Procedia Manufacturing, 41, 415–422. https://doi.org/10.1016/j.promfg.2019.09.027
  • Ghobakhloo, M. (2018). The future of manufacturing industry: a strategic roadmap toward Industry 4.0. Journal of Manufacturing Technology Management, 29(6), 910-936.
  • Givehchi, O., Henning, T., & Juergen, J. (2013). Conference Program. In Conference on Emerging Technologies & Factory Automation (ETFA), 1–37.
  • Golzer, Cato, & Amberg, M. (2015). Data processing requirements of industry 4.0-use cases for big data applications. In: ECIS 2015 Research-in-Progress Papers, Munster Germany, 26-29 May, 61.
  • Hamzeh, R., Zhong, R., & Xu, X. W. (2018). A Survey Study on Industry 4.0 for New Zealand Manufacturing. Procedia Manufacturing, 26, 49–57. https://doi.org/10.1016/j.promfg.2018.07.007
  • Hämäläinen, E., & Inkinen, T. (2019). Industrial applications of big data in disruptive innovations supporting environmental reporting. Journal of Industrial Information Integration, 16, 100105. https://doi.org/10.1016/j.jii.2019.100105
  • Harrisson, J. P. (2010). Strategic planning and SWOT analysis. In J. P. Harrison, Essentials ofstrategic planning in healthcare. Chicago: Health Administration Press.
  • Hathaliya, J., Sharma, P., Tanwar. S., & Gupta, R. (2019). Blockchain-based remote patient monitoring in healthcare 4.0. IEEE 9th International Conference On Advanced Computing (IACC), pp. 87-91.
  • Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23-34. http://dx.doi.org/10.1016/j.compind.2017.04.002.
  • Huxtable, J., & Schaefer, D. (2016). On servitization of the manufacturing industry in the UK. Procedia CIRP, 52, 46-51. http://dx.doi.org/10.1016/j.procir.2016.07.042.
  • Jukić, N., Sharma, A., Nestorov, S., & Jukić, B. (2015). Augmenting Data Warehouses with Big Data. Information Systems Management, 32(3), 200–209. https://doi.org/10.1080/10580530.2015.1044338
  • Kagermann, H., Helbig, J., Schuh, G., & Wahlster, W. (2013). Securing the future of German manufacturing industry. Recommendations for implementing the strategic initiative Industrie 4.0. Final report of the Industrie 4.0 Working Group München: Acatech.
  • Kamble, S. K., Gunasekaran, A., & 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, 117, 408-425. http://dx.doi.org/10.1016/j.p
  • Kamble, S., Gunasekaran, A., & Dhone, N.C. (2020), Industry 4.0 and leanmanufacturing practices for sustainable organisational performance in Indian manufacturing companies. International Journal of Production Research, vol. 58, no.5, pp. 1319-1337.
  • Kneissl, W. (2013). 3D printing 2014-2025: Technologies, markets, players. In ABD: ID Tech Ex; IDTechEX: Boston, MA, USA, p. 4.
  • Kopetz, H. (2011). Internet of things. In Real-Time Systems; Springer: New York, NY, USA, pp. 307–323.
  • Lakshmi, B. & Raghunandhan, G. (2011). A conceptual overview of data mining. In: National conference on innovations in emerging technology, Erode India, 17-18 February, 27-35.
  • Landriscina, F. (2013). Simulation and Learning a Model-Centered Approach; Springer: New York, NY, USA.
  • Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239-242.
  • Lee, J. (2013). Industry 4.0 in Big Data Environment. German Harting Magazine, pp.8–10.
  • 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.
  • Lee, J., Lapira, E., Bagheri, B., & Kao, H. A. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1(1), pp.38–41. Available at: http://linkinghub.elsevier.com/retrieve/pii/S22138463130001 14 [Accessed July 14, 2014].
  • Lee, G. M., Crespi, N., Choi, J.K., & Boussard, M. (2013). Internet of things. In Evolution of Telecommunication Services; Springer: Berlin/Heidelberg, Germany, pp. 257–282.
  • Lee, J., Ardakani, H. D., Yang, S., & Bagheri, B. (2015). Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance & Service Innovation. Procedia CIRP, 38, 3 7. https://doi.org/10.1016/j.procir.2015.08.026
  • Liang, X., Zhao, J., Shetty, S & Li, D. (2017). Towards data assurance and resilience in IoT using blockchain, in Proc. IEEE Mil. Commun. Conf., Baltimore, MD, USA, pp. 261–266.
  • Li, Z., Kang, J., Yu, R., Ye, D., Deng, Q. & Zhang, Y. (2018). Consortium blockchain for secure energy trading in industrial Internet of Things, IEEE Trans. Ind. Inform., vol. 14, no. 8, pp. 3690–3700.
  • Longo, F., Nicoletti, L., & Padovano, A. (2017). Smart operators in Industry 4.0: A human centred approach to enhance operators' capabilities and competencies within the new smart factory context. Computers & Industrial Engineering, 113, 144–159.
  • Masood, T., & Egger, J. (2019). Augmented reality in support of Industry 4.0: Implementation challenges and success factors. Robotics and Computer Integrated Manufacturing, 58, 181–195. https://doi.org/10.1016/j.rcim.2019.02.003.
  • McKinsey, C. (2016). Public Sector Information Technology. Chicago: McKinsey & Company.
  • Mladen, A.V. (2008). Cloud computing-issues, research and implementations. Journal of Computing and Information Technology, 16(4):235-246.
  • Miller, D. (2018). Blockchain and the Internet of Things in the industrial sector, IT Professional, vol. 20, no. 3, pp. 15–18.
  • Müller, J. M., & Voigt, K.-I. (2018). The Impact of Industry 4.0 on Supply Chains in Engineer-to-Order Industries - An Exploratory Case Study. IFAC-Papers On Line, 51(11), 122–127. https://doi.org/10.1016/j.ifacol.2018.08.245.
  • Müller, J. M. (2019). Assessing the barriers to Industry 4.0 implementation from a workers’ perspective. IFAC Papers On Line, 52(13), 2189–2194. https://doi.org/10.1016/j.ifacol.2019.11.530
  • 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
  • Nübler, I. (2016, November). New technologies: A jobless future or a golden age of job creation? Retrieved May 19, 2018, from http://www.ilo.org/wcmsp5/groups/public/---dgreports/--inst/documents/publication/wcms_544189.pdf
  • Raj, R., Lee, I., & Stankovic, J. (2010), Cyber-Physical Systems: The Next Computing Revolution. Design Automation Conference (DAC), pp. 731–736.
  • Rajkumar, R.R ., I. Lee, I ., Sha, L & J. Stankovic, J. 2010, Cyber-physical systems: The next computing revolution, in Proceedings of the 47th Design Automation Conference, pp. 731–736.
  • Rajpurohit, L., & Arvid, V. K. (2016). Industrie 4.0: An overview. International Journal of Advance Engineering and Research Development, 3, 535–541
  • Reddy, B. R. (2018). A Comprehensive Literature Review on Data Analytics in IIoT (Industrial Internet of Things). HELIX, 8(1), 2757–2764. https://doi.org/10.29042/2018-2757-2764
  • Rehman, M. H., Yaqoob, I., Salah, K., Imran, M., Jayaraman, P. P., & Perera, C. (2019). The role of big data analytics in industrial Internet of Things. Future Generation Computer Systems, 99, 247–259. https://doi.org/10.1016/j.future.2019.04.020
  • Industry 4.0 in Management Studies: A Systematic Literature Review
  • Piccarozzi, M., Aquilani,B., & Gatti, C. (2018).Industry 4.0 in Management Studies: A Systematic Literature Review, 1-24
  • Sarma, S., Dutt, N., Gupta, P., Venkatasubramanian, N & Nicolau, A. (2015). Cyberphysical-system-on-chip (cpsoc): A self-aware mpsoc paradigm with cross-layer virtual sensing and actuation, in Design, Automation Test in Europe Conference Exhibition, pp. 625–628
  • Schwab, K. (2016). The fourth industrial revolution Geneva: World Economic Forum.
  • Scott, J., Gupta, N., Wember, C., & Newsom, S., & Caffrey, T. (2012). Additive Manufacturing: Status and Opportunities, 1-29.
  • Shafiq, S. I., Sanin, C., Szczerbicki, E., & Toro, C. (2015). Virtual Engineering Object / Virtual Engineering Process: A specialized form of Cyber Physical System for Industrie 4.0. Procedia Computer Science, 60, 1146-1155. https://doi.org/10.1016/j.procs.2015.08.166
  • Shamim, S. E. A. (2016). Management Approaches for Industry 4.0 – A human resources management perspective. IEEE Congress on Evolutionary Computation (CEC), https://www.researchgate.net/publication/311251654, pp. 5309-5314.
  • Shank, P. 2016. The Fourth Industrial Revolution: What Happens With Employment? https://www.td.org/Publications/Blogs/Learning-Executive Blog/2016/05/The-Fourth Industrial-Revolution-What-Happens-withEmployment. Accessed 10-9-2019.
  • Shava, E., & Hofisi, C. (2017). Challenges and opportunities for public administration in the Fourth Industrial Revolution. African Journal of Public Affairs, 9(9), 203-215.
  • Strupczewski, L. (2015, January 10). Big Data or Big Confusion? Retrieved May 29, 2018, from https://www.business2community.com/big-data/big-data-bigconfusion-01118253
  • Sreedharan, V. R. & Unnikrishnan, A. (2017). Moving towards industry 4.0: a systematic review. International Journal of Pure and
  • Applied Mathematics, 117(20), 929-936.
  • Sung, T. K. (2018). Industry 4.0: A Korea perspective. Technological Forecasting and Social Change, 132, 40–45. https://doi.org/10.1016/j.techfore.2017.11.005
  • Teslya, N & Ryabchikov, I. (2017). Blockchain-based platform architecture for industrial IoT, in Proc. 21st Conf. of Open Innovations Assoc., Helsinki, Finland, pp. 321–329.
  • Thompson, A. A., Strickland, A. J. & Gamble, J. E. (2007). Crafting and Executing Strategy Concepts and Cases, (15th Edition), USA: McGraw-Hill/Irwin
  • Xu, L. D., & Duan, L. (2018). Big data for cyber physical systems in industry 4.0: a survey. Enterprise Information Systems, 13(2), 148–169. https://doi.org/10.1080/17517575.2018.1442934
  • Walwei, U. (2016, September). Digitalization and structural labour market problems: The case of Germany. Retrieved May 19, 2018, from http://www.ilo.org/wcmsp5/groups/public/--dgreports/---inst/documents/publication/wcms_522355.pdf
  • Wang, X., Ong, S. K., & Nee, A. Y. C. (2016). A comprehensive survey of augmented reality assembly research. Advances in Manufacturing, 4, 1–22.
  • Wang et al. (2017, November 28). Industry 4.0: A way from mass customization to mass personalization production. Retrieved June 16, 2018, from https://link.springer.com/article/10.1007/s 40436-017-0204-7
  • WCED, S. W. S. (1987). World commission on environment and development. Our common future, 17, 1-91.
  • Witkowski, K. (2017). Internet of things, big data, Industry 4.0 – innovative solutions in logistics and supply chains management. Procedia Engineering, 182, 763-769
  • World Economic Forum (WEF). (2016). Five Million Jobs by 2020: The Real Challenge of the Fourth Industrial Revolution. Available at https://www.weforum.org/press/2016/01/five-million-jobs-by-2020-the-real-challenge-of-the-fourth-industrial-revolution. Accessed 15-9-2019.
  • Yang, G., Xie, L., Mantsalo, M., Zhou, X., Pang, Z., Xu, L., Kao-Walter, S., Chen, Q., & Zheng, L. (2014). A Health-IoT Platform Based on the Integration of Intelligent Packaging, Unobtrusive Bio-Sensor and Intelligent Medicine Box. IEEE Transactions on Industrial informatics, 10, 2180–2191.
  • Yin, S., & Kaynak, O. (2015). Big Data for Modern Industry: Challenges and Trends, Proc. of IEEE, vol. 103, no. 2, pp. 143–146.
  • Ylijoki, O., & Porras, J. (2018). A recipe for big data value creation. Business Process Management Journal, 25(5), 1085–1100. https://doi.org/10.1108/bpmj-03-2018-0082
  • Verl, A. Robotics & Industrie 4.0. (2017). In IFR-International Federation of Robotics. Available online: https://scholar.archive.org/work/q26zts5rhjd5jhfbjzv6dhi62u/access/wayback/https://ifr.org/img/uploads/Presentation_Industry_i4.0_Rob_Alexander_VERL_29_9_16.pdf (accessed on 7 February 2022).
  • Zhang, Y., Ma, S., Yang, H., Lv, J., & Liu, Y. (2018). A big data driven analytical framework for energyintensive manufacturing industries. Journal of Cleaner Production, 197, 57–72.
  • https://doi.org/10.1016/j.jclepro.2018.06.170
  • Zheng, T., Ardolino, M., Bacchetti, A. & Perona, M. (2021). The applications of Industry4.0 technologies inmanufacturing context: asystematic literatüre review. International Journal of Production Research, vol. 59, no.6, pp.1922-1954.
  • Zhou, C., Damiano, N., Whisner, B., & Reyes, M. D. (2017). Industrial Internet of Things: (IIoT) applications in underground coal mines. Mining Engineering, 69(12):50-56.
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
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.