Endüstri 4.0 ve Emek Piyasasının Geleceği

Year 2022, Volume: 4 Issue: 2, 80 - 88, 31.12.2022

Cemal Erdem Hepaktan , Deniz Şimşek

https://doi.org/10.47899/ijss.1174005

Abstract

Üçüncü Sanayi Devrimi ile hayatımıza bilgisayarlar ve internetin girmesinin ardından kablosuz ağlar, bilgi işlem, bulut altyapıları, büyük veri teknolojileri, yapay zeka ile güçlendirilmiş robotlar, nesnelerin interneti (IoT), siber-fiziksel sistemler(CPS) gibi dijital gelişmeleri ile bugün yeni bir teknolojik çağ başladı. Endüstri 4.0 olarak anılan Dördüncü Sanayi Devrimi beraberinde getirdiği teknolojik araçları ile bir çok alanda olduğu gibi üretim modellerinde de dönüşümü başlatacaktır. Endüstri 4.0’ın beraberinde anılan bileşenleri siber-fiziksel sistemler (CPS), nesnelerin İnterneti (IoT) ve bulut bilişim, yapay zeka ve robotik teknolojiler üretimi dönüştürerek birçok işin doğasını değiştirdi. Bu dijital dönüşüm emek piyasası ve emek faktörünün dönüşümü üzerinde baskı oluşturmakta ve çözüme kavuşturulması gereken çok boyutlu bir problem olarak önümüzde durmaktadır.
Bu teknolojiler üretimde maliyet avantajı ve verimliliği yükseltmese bakımından rekabet gücünü arttırırken, üretim yapısında da değişiklikler meydana getirmektedir. Yaşamın her alanında bahsi geçen teknoloji “insansız” misyonuna hizmet edecek şekilde planlanıyor. İnsansız ulaşım araçları, eğitim kurumları, hastaneler, fabrikalar ve daha niceleri ile günlük hayatta insan emeğinin yerini yapay zeka harikası robotlar, robotik kollara ve makinalar alacaktır. Tam otomasyonun kusursuz örneği karanlık fabrikalar, yapay zeka teknolojisine sahip robotlar, robotik kollar ve kendi aralarında iletişime geçen makinalarıyla, emeğin üretimdeki yerini ciddi anlamda sarsacak gibi görünmektedir. Bu süreçte akla şu soru gelmektedir: Emeğin yerini alması beklenen makineler istihdamı olumsuz etkiler mi yoksa emeğin bu teknolojik dönüşüme ayak uydurması mümkün müdür? Teknolojik dönüşümün insansız misyonu göz önüne alındığında, bu durumun emek faktörünün istihdamını olumsuz etkileyeceği ve teknolojik işsizliğin artacağı anlamına gelmektedir. Ancak yüksek düzeyde otomasyonun sağlandığı ortamda emeğin dezavantajlı konumda olması mevcut emek piyasasının nitelikleri açısından değerlendirilmelidir. Geleceğin teknolojik dönüşümden geçen işleri, daha fazla teknolojik bilgi ve insan becerisi gerektirir. Her ne kadar teknolojik gelişmeler üretimi yapay zeka, robotlar ve akıllı makinalara bırakıyor olsa da hala bu teknoloji harikalarının arkasında insan zekasına ihtiyaç bulunmaktadır. Tam otomasyon üzerine kurulu tüm üretim ve hizmet birimlerinde, o teknolojinin kusursuz işlemesinin arka planında çalışan teknisyen ve mühendislere her zaman ihtiyaç duyulacaktır. Bu durum, niteliksiz emek gücünü teknoloji karşısında savaşı kaybedeceğini gösterse de, nitelikli emek gücü için her zaman umudun olduğunu göstermektedir.
Çalışmada teknolojik gelişmelerin işgücü piyasasındaki etkisine yönelik bir literatür çalışması yapılarak mevcut durum ortaya konulmuş ve endüstri 4.0 kavramı ve bileşenleri hakkında bilgi verilerek bu teknolojik dönüşümün işgücü piyasasına olan etkilerinden bahsedilmiştir. Mal ve hizmet üretiminde, akıllı teknolojilerin yoğun kullanımı nedeniyle, işgücü piyasasındaki olası değişikliklere de yer verilmiştir.

Keywords

Endüstri 4.0, Teknolojik İşsizlik, İşgücü Piyasası

Industry 4.0 and the Future of the Labor Market

Abstract

After the introduction of computers and the Internet into our lives with the Third Industrial Revolution, digital developments such as wireless networks, computing, cloud infrastructures, big data technologies, artificial intelligence-enhanced robots, internet of things (IoT), and cyber-physical systems (CPS) are starting of a new technological age. The Fourth Industrial Revolution, known as Industry 4.0, will initiate a transformation in production models, as in many areas, with its technological tools. The aforementioned components of Industry 4.0, cyber-physical systems (CPS), Internet of Things (IoT), and cloud computing, artificial intelligence and robotic technologies have transformed production and changed the nature of many businesses. This digital transformation puts pressure on the labor market and the transformation of the labor factor and stands before us as a multidimensional problem that needs to be resolved. While these technologies increase competitiveness in terms of cost advantage and efficiency in production, they bring about changes in the production structure. The technology mentioned in all areas of life is planned to serve the "unmanned" mission. With unmanned transportation vehicles, educational institutions, hospitals, factories and many others, human labor will be replaced by artificial intelligence wonder robots, robotic arms and machines in daily life. The perfect example of full automation, dark factories, robots with artificial intelligence technology, robotic arms and machines that communicate with each other, seem to seriously shake the place of labor in production. In this process, the following question comes to mind: Will the machines that are expected to replace labor affect employment negatively or is it possible for labor to keep up with this technological transformation? Considering the unmanned mission of technological transformation, it means that this situation will negatively affect the employment of the labor factor and technological unemployment will increase. However, the disadvantaged position of labor in an environment with a high level of automation should be evaluated in terms of the characteristics of the current labor market. The jobs of the future undergoing technological transformation require more technological knowledge and human skills. Although technological developments leave production to artificial intelligence, robots and smart machines, human intelligence is still needed behind these technological wonders. In all production and service units built on full automation, there will always be a need for technicians and engineers working in the background of the flawless operation of that technology. Although this shows that unskilled labor force will lose the war against technology, it shows that there is always hope for qualified labor force. In the study, the current situation is revealed by making a literature study on the effect of technological developments in the labor market, and the effects of this technological transformation on the labor market are mentioned by giving information about the concept of industry 4.0 and its components.
Possible changes in the labor market due to the intensive use of smart technologies in the production of goods and services are also included.

Keywords

Industry 4.0, technological unemployment, labor market

References

• Akinrobotics, https://www.akinrobotics.com/tr/hakkimizda (Accessed: 2.5.2020).
• Alguliyev, R., Imamverdiyev, Y., & Sukhostat, L. (2018). Cyber-physical systems and their security issues. Computers in Industry, 100, 212-223.
• Asghari, P., Rahmani, A. M., & Javadi, H. H. S. (2019). Internet of Things applications: A systematic review. Computer Networks, 148, 241-261.
• Automotiveit, https://www.automotiveit.eu/produktion/audi-setzt-auf-mensch-roboter-kollaboration-278.html (Accessed:11.04.2020)
• Bahrin, M. A. K., Othman, M. F., Azli, N. N., & Talib, M. F. (2016). Industry 4.0: A review on industrial automation and robotic. Jurnal Teknologi, 78(6-13), 137-143.
• Baeg, S. H., Park, J. H., Koh, J., Park, K. W., & Baeg, M. H. (2007, October). Building a smart home environment for service robots based on RFID and sensor networks. In 2007 International Conference on Control, Automation and Systems (pp. 1078-1082). IEEE.
• Bortolini, M., Calabrese, F., Galizia, F. G., Mora, C., & Ventura, V. (2021, September). Industry 4.0 Technologies: A Cross-sector Industry-Based Analysis. In Proceedings of the International Conference on Sustainable Design and Manufacturing (pp. 140-148). Springer, Singapore.
• Brousell, D. R., Moad, J. R., & Tate, P. (2014). The next industrial revolution: how the internet of things and embedded, connected, intelligent devices will transform manufacturing. Frost & Sullivan, A Manufacturing Leadership White Paper.
• Chen, G., Wang, P., Feng, B., Li, Y., & Liu, D. (2020). The framework design of smart factory in discrete manufacturing industry based on cyber-physical system. International Journal of Computer Integrated Manufacturing, 33(1), 79-101.
• Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., & Fraisse, P. (2016). Collaborative manufacturing with physical human–robot interaction. Robotics and Computer-Integrated Manufacturing, 40, 1-13.
• Clarke, R. (2019). Why the world wants controls over Artificial Intelligence. Computer Law & Security Review, 35(4), 423-433.
• Corke, Peter. (2017) Robotics, vision and control: fundamental algorithms in MATLAB® second, completely revised. Springer.
• Craigen, D., Diakun-Thibault, N., & Purse, R. (2014). Defining cybersecurity. Technology Innovation Management Review, 4(10).
• Enterpriseiotinsights, https://enterpriseiotinsights.com/20160810/internet-of-things/lights-out-manufacturing-tag31-tag99 (Accessed: 29.04.2020).
• Fanuc, https://www.fanuc.eu/tr/tr/who-we-are/fanuc-history, Accessed: 25.07.2022
• Fatorachian, H., & Kazemi, H. (2018). A critical investigation of Industry 4.0 in manufacturing: theoretical operationalisation framework. Production Planning & Control, 29(8), 633-644, . doi.org/10.1080/09537287.2018.1424960
• Fisher, D., DeLine, R., Czerwinski, M., & Drucker, S. (2012). Interactions with big data analytics. interactions, 19(3), 50-59.
• Firu, A. C., Tapîrdea, A. I., Feier, A. I., & Drăghici, G. (2021). Virtual reality in the automotive field in industry 4.0. Materials Today: Proceedings, 45, 4177-4182.
• Fonseca, L. M. (2018, May). Industry 4.0 and the digital society: concepts, dimensions and envisioned benefits. In Proceedings of the international conference on business excellence (Vol. 12, No. 1, pp. 386-397). Sciendo DOI: 10.2478/picbe-2018-0034
• Fragapane, G., Ivanov, D., Peron, M., Sgarbossa, F., & Strandhagen, J. O. (2020). Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics. Annals of Operations Research, 1-19. doi.org/10.1007/s10479-020-03526-7
• Gasparetto, A., & Scalera, L. (2019). From the Unimate to the Delta robot: the early decades of Industrial Robotics. In Explorations in the History and Heritage of Machines and Mechanisms (pp. 284-295). Springer, Cham.
• Gray, K., & Wegner, D. M. (2012). Feeling robots and human zombies: Mind perception and the uncanny valley. Cognition, 125(1), 125-130.
• Hentout, A., Aouache, M., Maoudj, A., & Akli, I. (2019). Human–robot interaction in industrial collaborative robotics: a literature review of the decade 2008–2017. Advanced Robotics, 33(15-16), 764-799.
• Kiran, D. R. (2019). Elements of production planning and control. Production Planning and Control, 495-513.
• Kragic, D., Gustafson, J., Karaoguz, H., Jensfelt, P., & Krug, R. (2018, July). Interactive, Collaborative Robots: Challenges and Opportunities. In IJCAI (pp. 18-25).
• Kusiak, A. (2017). Smart manufacturing must embrace big data. Nature, 544(7648), 23-25.
• Liu, Y., Zhang, W., Pan, S., Li, Y., & Chen, Y. (2020). Analyzing the robotic behavior in a smart city with deep enforcement and imitation learning using IoRT. Computer Communications, 150, 346-356.
• Liu, Q., Chen, J., Liao, Y., Mueller, E., Jentsch, D., Boerner, F., & She, M. (2015). An application of horizontal and vertical integration in cyber-physical production systems. In 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (pp. 110-113). DOI: 10.1109/CyberC.2015.22.
• McKinsey, D. (2016). Industry 4.0 after the initial hype. Where manufacturers are finding value and how they can best capture it.
• Mell, Peter, et al. (2011). The NIST definition of cloud computing. https:/doi.org/10.6028/NIST.SP.800-145.
• Peshkin, M. A., Colgate, J. E., Wannasuphoprasit, W., Moore, C. A., Gillespie, R. B., & Akella, P. (2001). Cobot architecture. IEEE Transactions on Robotics and Automation, 17(4), 377-390.
• Park, K., Nguyen, M. C., & Won, H. (2015, July). Web-based collaborative big data analytics on big data as a service platform. In 2015 17th international conference on advanced communication technology (icact) (pp. 564-567). IEEE.
• Raja, M., & Priya, G. G. (2021). Conceptual Origins, Technological Advancements, and Impacts of Using Virtual Reality Technology in Education. Webology, 18(2).
• Rainie, L., Anderson, J., & Connolly, J. (2014). Cyber attacks likely to increase.
• Rojko, A. (2017). Industry 4.0 concept: background and overview. International Journal of Interactive Mobile Technologies (iJIM), 11(5), 77-90. doi.org/10.3991/ijim.v11i5.7072
• Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020). Cybersecurity data science: an overview from machine learning perspective. Journal of Big data, 7(1), 1-29.
• Shafiq, S. I., Sanin, C., Toro, C., & Szczerbicki, E. (2015). Virtual engineering object (VEO): Toward experience-based design and manufacturing for industry 4.0. Cybernetics and Systems, 46(1-2), 35-50. doi.org/10.1080/01969722.2015.1007734
• Sisinni, E., Saifullah, A., Han, S., Jennehag, U., & Gidlund, M. (2018). Industrial internet of things: Challenges, opportunities, and directions. IEEE transactions on industrial informatics, 14(11), 4724-4734.
• Thames, L., & Schaefer, D. (2017). Cybersecurity for industry 4.0. New York: Springer https://link.springer.com/content/pdf/10.1007/978-3-319-50660-9.pdf
• Tuptuk, N., & Hailes, S. (2018). Security of smart manufacturing systems. Journal of manufacturing systems, 47, 93-106. doi.org/10.1016/j.jmsy.2018.04.007
• Turkey's Industry 4.0 Platform, https://www.endustri40.com/karanlik-fabrikalar-ile-insansiz-uretim/#:~:text=Karanl%C4%B1k%20(lights%2Dout)%20%C3%BCretim,%C3%BCretim%20tamamen%20robotik%20sistemlerle%20ger%C3%A7ekle%C5%9Ftirilir, Accessed: 05.05.2020.
• Universal Robots, https://www.universal-robots.com/products/collaborative-robots-cobots-benefits/ , Accessed: 05.05.2020)
• Von Solms, R., & Van Niekerk, J. (2013). From information security to cyber security. computers & security, 38, 97-102.
• Wadhwa, R. S. (2012). Flexibility in manufacturing automation: A living lab case study of Norwegian metalcasting SMEs. Journal of Manufacturing Systems, 31(4), 444-454.
• Wang, S., Zhang, C., Liu, C., Li, D., & Tang, H. (2017). Cloud-assisted interaction and negotiation of industrial robots for the smart factory. Computers & Electrical Engineering, 63, 66-78. doi.org/10.1016/j.compeleceng.2017.05.025
• Weforum https://www.weforum.org/reports/the-future-of-jobs-report-2020/in-full/infographics-e4e69e4de7 (Accessed: 15.07.2022).
• El Zaatari, S., Marei, M., Li, W., & Usman, Z. (2019). Cobot programming for collaborative industrial tasks: An overview. Robotics and Autonomous Systems, 116, 162-180.
• Zhu, W., Fan, X., & Zhang, Y. (2019). Applications and research trends of digital human models in the manufacturing industry. Virtual reality & intelligent hardware, 1(6), 558-579.
• Zuehlke, D. (2010). SmartFactory—Towards a factory-of-things. Annual reviews in control, 34(1), 129-138.