ÜRETİM ENDÜSTRİSİNİ DÖNÜŞTÜREN TEKNOLOJİ TRENDLERİNE GENEL BİR BAKIŞ

Yıl 2023, , 1339 - 1354, 26.09.2023

Yunus Emre Gür , Koray Gündüz

https://doi.org/10.18069/firatsbed.1297867

Öz

Bilgi ve iletişim teknolojisi hızla gelişmekte ve bulut bilişim, Nesnelerin İnterneti, büyük veri analitiği ve yapay zekâ gibi birçok yıkıcı teknoloji ortaya çıkmaktadır. Bu teknolojiler üretim endüstrisine nüfuz etmekte ve endüstriyel üretimin dördüncü aşamasının (yani Endüstri 4.0) gelişini belirleyen siber-fiziksel sistemler (CPS) aracılığıyla fiziksel ve sanal dünyaların kaynaşmasını sağlamaktadır. CPS’nin üretim ortamlarında yaygın olarak uygulanması, üretim sistemlerini giderek daha akıllı hale getirmektedir. Endüstri 4.0’ın üretim endüstrisinde uygulanmasına ilişkin araştırmaları ilerletmek için bu çalışmada, ilk olarak, Endüstri 4.0 için kavramsal bir çerçeve sunulmuştur. İkinci olarak, bu çerçevede sunulan ön uç teknolojiler ile ilgili örnek senaryolar açıklanmıştır. Buna ek olarak, Endüstri 4.0 temel teknolojileri ve bunların Endüstri 4.0 akıllı üretim sistemlerine yönelik olası uygulamaları gözden geçirilmiştir. Son olarak, zorluklar ve gelecek perspektifleri belirlenmiş ve tartışılmıştır.

Anahtar Kelimeler

Endüstri 4.0, Akıllı Üretim Sistemleri, Nesnelerin İnterneti, Siber-Fiziksel Sistemler, Büyük Veri Analitiği

An Overview Of Technology Trends That Are Transforming The Manufacturing Industry

Öz

Information and communication technology is developing rapidly and many disruptive technologies such as cloud computing, Internet of Things, big data analytics and artificial intelligence are emerging. These technologies permeate the manufacturing industry and enable the fusion of the physical and virtual worlds through cyber-physical systems (CPS), which marks the advent of the fourth stage of industrial production (i.e. Industry 4.0). The widespread application of CPS in production environments is making production systems increasingly intelligent. In order to advance research on the application of Industry 4.0 in the manufacturing industry, firstly, a conceptual framework for Industry 4.0 is presented in this study. Secondly, sample scenarios related to front-end technologies presented in this framework are explained. In addition, Industry 4.0 core technologies and their possible applications for Industry 4.0 smart manufacturing systems are reviewed. Finally, challenges and future perspectives are identified and discussed.

Anahtar Kelimeler

Industry 4.0, Intelligent Manufacturing Systems, Internet of Things, Cyber-Physical Systems, Big Data Analytics.

Kaynakça

• Antrobus V, Burnett G, and Krehl C. (2017). Driver-Passenger Collaboration as a Basis for Human-Machine İnterface Design for Vehicle Navigation Systems. Ergonomics, 60(3): 321–332.
• Arunachalam, D., Kumar, N., and Kawalek, J. P. (2018). Understanding Big Data Analytics Capabilities in Supply Chain Management: Unravelling the İssues, Challenges and İmplications for Practice. Transportation Research Part E: Logistics and Transportation Review, 114, 416-436.
• Badarinath, R., and Prabhu, V. V. (2017). Advances in Internet Of Things (Iot) in Manufacturing. In Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing: IFIP WG 5.7 International Conference, APMS, Hamburg, Germany, September 3-7, Proceedings, Part I, 111-118.
• Baheti R, and Gill H. (2011). Cyber-physical systems. In: Samad T, Annaswamy AM, Editors The İmpact of Control Technology: Overview, Success Stories and Research Challenges. New York: IEEE Control Systems Society, 161–166.
• Baines, T., Ziaee Bigdeli, A., Bustinza, O. F., Shi, V. G., Baldwin, J., and Ridgway, K. (2017). Servitization: Revisiting the State-of-the-Art and Research Priorities. International Journal of Operations & Production Management, 37(2), 256-278.
• Belhadi, A., Zkik, K., Cherrafi, A., and Sha'ri, M. Y. (2019). Understanding Big Data Analytics for Manufacturing Processes: İnsights From Literature Review and Multiple Case Studies. Computers & Industrial Engineering, 137, 106099.
• Ben-Daya, M., Hassini, E., and Bahroun, Z. (2019). Internet of Things and Supply Chain Management: A Literature Review. International Journal of Production Research, 57(15–16), 4719–4742.
• Bibby, L., and B. Dehe. (2018). Defining and Assessing Industry 4.0 Maturity Levels–Case of the Defence Sector. Production Planning & Control, 29 (12), 1030–1043. doi:10.1080/09537287.2018.1503355.
• Bloom N, Garicano L, Sadun R, and Van Reenen J. (2014). The Distinct Effects of İnformation Technology and Communication Technology on Firm Organization. Manage Sci, 60(12), 2859–2885.
• Bond, T. C. (1999). The Role of Performance Measurement in Continuous Improvement. International Journal of Operations & Production Management 19 (12), 1318–1334. doi:10.1108/01443579910294291.
• Calabrese, A., M. Dora, N. Levialdi Ghiron, and L. Tiburzi. (2020). Industry’s 4.0 Transformation Process: How to Start, Where to Aim, What to Be Aware of. Production Planning & Control 32, 1–21.
• Chiang, L., Lu, B., and Castillo, I. (2017). Big Data Analytics in Chemical Engineering. Annual Review of Chemical and Biomolecular Engineering, 8, 63-85.
• Choi, S., Kim, B. H. and Do Noh, S. (2015). A Diagnosis and Evaluation Method for Strategic Planning and Systematic Design of A Virtual Factory in Smart Manufacturing Systems. Int. J. Precis. Eng. Manuf., 16(6), 1107–1115,
• Colin, M., Galindo, R., and Hernández, O. (2015). Information and Communication Technology As A Key Strategy for Efficient Supply Chain Management in Manufacturing Smes. Procedia Computer Science, 55, 833–842.
• Dalenogare, L. S., G. B. Benitez, N. F. Ayala, and A. G. Frank. (2018). The Expected Contribution of Industry 4.0 Technologies for Industrial Performance. International Journal of Production Economics, 204, 383–394. doi:10.1016/j.ijpe.2018.08.019.
• Davis, J., Edgar, T., Graybill, R., Korambath, P., Schott, B., Swink, D., Wang, J. and Wetzel, J. (2015). Smart Manufacturing. Annual Review of Chemical and Biomolecular Engineering, 6, 141–160.
• Derler P, Lee EA, and Vincentelli AS. (2012). Modeling Cyber-Physical Systems. Proc IEEE, 100(1), 13–28. Dewar, R. D., and J. E. Dutton. (1986). The Adoption of Radical and Incremental Innovations: An Empirical Analysis. Management Science, 32 (11), 1422–1433. doi:10.1287/mnsc.32.11.1422.
• E. Wallace and F. Riddick, (2013). Panel on Enabling Smart Manufacturing. State College, USA. Eardley, A., H. Shah, and A. Radman. (2008). A Model for Improving the Role of IT in BPR. Business Process Management Journal, 14(5), 629–653. doi:10.1108/14637150810903039.
• El Kadiri, S., Grabot, B., Thoben, K. D., Hribernik, K., Emmanouilidis, C., Von Cieminski, G., and Kiritsis, D. (2016). Current Trends on ICT Technologies for Enterprise İnformation Systems. Computers in Industry, 79, 14-33.
• Farooq MU, Waseem M, Mazhar S, Khairi A, and Kamal T. (2015). A Review on Internet of Things (IoT). Int J Comput Appl, 113(1), 1–7.
• Ferdows, K. (2018). Keeping Up with Growing Complexity of Managing Global Operations. International Journal of Operations & Production Management, 38(2), 390–402. doi:10.1108/IJOPM-01-2017-0019.
• Fernando, N., Loke, S. W., and Rahayu, W. (2013). Mobile Cloud Computing: A survey. Future Generation Computer Systems, 29(1), 84-106.
• Fleischmann, M., Bloemhof-Ruwaard, J. M., Dekker, R., Van der Laan, E., Van Nunen, J. A., and Van Wassenhove, L. N. (1997). Quantitative Models for Reverse Logistics: A review. European Journal of Operational Research, 103(1), 1-17.
• Frank, A. G., G. H. Mendes, N. F. Ayala, and A. Ghezzi. (2019). Servitization and Industry 4.0 Convergence in the Digital Transformation of Product Firms: A Business Model Innovation Perspective. Technological Forecasting and Social Change, 141, 341–351. doi:10. 1016/j.techfore.2019.01.014.
• Frank, A. G., L. S. Dalenogare, and N. F. Ayala. (2019). Industry 4.0 Technologies: Implementation Patterns in Manufacturing Companies. International Journal of Production Economics, 210, 15–26. doi:10.1016/ j.ijpe.2019.01.004.
• Ge, Z., Song, Z., Ding, S. X., and Huang, B. (2017). Data Mining and Analytics in the Process İndustry: The Role of Machine Learning. Ieee Access, 5, 20590-20616.
• Gilchrist, A. (2016). Industry 4.0: The İndustrial İnternet of Things. Apress. New York.
• Guo ZX, Ngai EWT, Yang C, and Liang X. (2015). An RFID-Based İntelligent Decision Support System Architecture for Production Monitoring and Scheduling İn A Distributed Manufacturing Environment. Int J Prod Econ, 159, 16–28.
• He, Q. P., and Wang, J. (2018). Statistical Process Monitoring As A Big Data Analytics Tool for Smart Manufacturing. Journal of Process Control, 67, 35-43.
• Ivezic, N., Kulvatunyou, B. and Srinivasan, V. (2014). On Architecting and Composing Through-life Engineering Information Services to Enable Smart Manufacturing, Procedia CIRP, 22, 45-52.
• Jeschke, S., Brecher, C., Meisen, T., Özdemir, D., and Eschert, T. (2017). Industrial İnternet of Things and Cyber Manufacturing Systems. Springer International Publishing, 3-19.
• Johnston, R., L. Fitzgerald, E. Markou, and S. Brignall. (2001). Target Setting for Evolutionary and Revolutionary Process Change. International Journal of Operations & Production Management, 21(11), 1387–1403. doi:10.1108/01443570110407409.
• Jun, H. B., Kiritsis, D., and Xirouchakis, P. (2007). Research issues on closed-loop PLM. Computers in İndustry, 58(8-9), 855-868.
• Kache, F., and S. Seuring. (2017). Challenges and Opportunities of Digital Information at the Intersection of Big Data Analytics and Supply Chain Management. International Journal of Operations & Production Management, 37(1), 10–36. doi:10.1108/IJOPM-02-2015-0078.
• Ketteni E, Kottaridi C, and Mamuneas TP. (2015). Information and Communication Technology and Foreign Direct İnvestment: Interactions and Contributions to Economic Growth. Empir Econ, 48(4), 1525–1539.
• Kim, D. Y., V. Kumar, and U. Kumar. (2012). Relationship between Quality Management Practices and Innovation. Journal of Operations Management, 30(4), 295–315. doi:10.1016/j.jom.2012.02.003.
• Klotz E, and Duwe J. (2017). A Pneumatic Revolution in Automation. Control Eng Europ, Apr, 34–35.
• Krumeich, J., Werth, D., and Loos, P. (2016). Prescriptive Control of Business Processes: New Potentials Through Predictive Analytics of Big Data in the Process Manufacturing İndustry. Business & Information Systems Engineering, 58, 261-280.
• Lasi, H., Fettke, P., Kemper, H. G., Feld, T., and Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242.
• Lu, Y., Morris, K. C. and Frechette, S. (2016). Current Standards Landscape for Smart Manufacturing Systems. National Institute of Standards and Technology.
• Lund D, MacGillivray C, Turner V, and Morales M. (2014). Worldwide and Regional Internet of Things (IoT) 2014–2020 Forecast: A Virtuous Circle of Proven Value and Demand. Framingham: International Data Corporation; May, Report No.: IDC #248451.
• Luo M, Yan HC, Hu B, Zhou JH, and Pang CK. (2015). A Data-Driven Two-Stage Maintenance Framework for Degradation Prediction in Semiconductor Manufacturing İndustries. Comput Ind Eng, 85, 414–422.
• MacCarthy, B. L., C. Blome, J. Olhager, J. S. Srai, and X. Zhao. (2016). Supply Chain Evolution–Theory, Concepts and Science. International Journal of Operations & Production Management, 36(12), 1696–1718, doi:10.1108/IJOPM-02-2016-0080.
• Manavalan, E., and Jayakrishna, K. (2019). A Review of İnternet of Things (Iot) Embedded Sustainable Supply Chain for İndustry 4.0 Requirements. Computers & Industrial Engineering, 127, 925–953.
• Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Hung Byers, A. (2011). Big Data: the Next Frontier for İnnovation, Competition and Productivity. McKinsey Global Institute.
• Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., and Ueda, K. (2016). Cyber-Physical Systems in Manufacturing. Cirp Annals, 65(2), 621-641.
• Mortensen, S. T., and Madsen, O. (2018). A Virtual Commissioning Learning Platform. Procedia Manufacturing, 23, 93-98.
• Muller, J. M., O. Buliga, and K.-I. Voigt. (2018). Fortune Favors the Prepared: How SMEs Approach Business Model Innovations in Industry 4.0. Technological Forecasting and Social Change, 132, 2–17, doi:10.1016/j.techfore.2017.12.019.
• Nguyen, T., Li, Z. H. O. U., Spiegler, V., Ieromonachou, P., and Lin, Y. (2018). Big Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review. Computers & Operations Research, 98, 254–264.
• Pfohl, H. C., Yahsi, B., and Kurnaz, T. (2017). Concept and Diffusion-Factors of İndustry 4.0 in the Supply Chain. In Dynamics in Logistics: Proceedings of the 5th International Conference LDIC, 2016 Bremen, Germany, 381-390.
• Priego R, Iriondo N, Gangoiti U, and Marcos M. (2017). Agent-Based Middleware Architecture for Reconfigurable Manufacturing Systems. Int J Adv Manuf Tech, 92(5–8), 1579–1590.
• Qin, S. J. (2014). Process Data Analytics in the Era of Big Data. AIChE Journal, 60(9), 3092-3100.
• Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., and Harnisch, M. (2015). Industry 4.0: The Future of Productivity and Growth in Manufacturing İndustries. Boston Consulting Group, 9(1), 54-89.
• Schniederjans, D. G. (2018). Business Process Innovation on Quality and Supply Chains. Business Process Management Journal, 24(3), 635–651. doi:10.1108/BPMJ-04-2016-0088.
• Schuh, G., R. Anderl, J. Gausemeier, M. Ten Hompel, and W. Wahlster. (2017). Industrie 4.0 Maturity Index. In Managing the Digital Transformation of Companies. Acatech Study, Herbert Utz. Munich.
• Shen WM, Hao Q, Wang S, Li Y, and Ghenniwa H. (2007), An Agent-Based Service-Oriented İntegration Architecture for Collaborative İntelligent Manufacturing. Robot Com-Int Manuf, 23(3), 315–325.
• Simpson TW, Jiao JR, Siddique Z, and Hölttä-Otto K, (2014). Advances in Product Family and Product Platform Design: Methods & Applications. New York: Springer-Verlag.
• Stock, T., Obenaus, M., Kunz, S., and Kohl, H. (2018). Industry 4.0 as Enabler for A Sustainable Development: A Qualitative Assessment of İts Ecological and Social Potential. Process Safety and Environmental Protection, 118, 254-267.
• Taherdoost, H. (2023). An Overview of Trends in Information Systems: Emerging Technologies that Transform the Information Technology Industry. Cloud Computing and Data Science, 1-16.
• Tan, Y., Goddard, S., and Perez, L. C. (2008). A prototype architecture for cyber-physical systems. ACM Sigbed Review, 5(1), 1-2.
• Thoben, K. D., Wiesner, S., and Wuest, T. (2017). Industrie 4.0 and Smart Manufacturing-A Review of Research İssues and Application Examples. International Journal of Automation Technology, 11(1), 4-16.
• Wagire, A. A., R. Joshi, A. P. S. Rathore, and R. Jain. (2020). Development of Maturity Model for Assessing the Implementation of Industry 4.0: learning from Theory and Practice. Production Planning & Control, 1–20.
• Wamba, S. F., Akter, S., Edwards, A., Chopin, G., and Gnanzou, D. (2015). How ‘Big Data’can Make Big İmpact: Findings From A Systematic Review and A Longitudinal Case Study. International journal of production economics, 165, 234-246. Wang YM, Wang YS, and Yang YF. (2010). Understanding the Determinants of RFID Adoption in the Manufacturing İndustry. Technol Forecast Soc, 77(5), 803–815.
• Wang, G., Gunasekaran, A., Ngai, E. W., and Papadopoulos, T. (2016b). Big Data Analytics in Logistics and Supply Chain Management: Certain İnvestigations for Research and Applications. International Journal of Production Economics, 176, 98-110.
• Wang, S., Wan, J., Zhang, D., Li, D., and Zhang, C. (2016a). Towards Smart Factory for İndustry 4.0: A Self-Organized Multi-Agent System With Big Data Based Feedback and Coordination. Computer Networks, 101, 158-168.
• Wang, X. V., and Xu, X. W. (2013). An İnteroperable Solution for Cloud Manufacturing. Robotics and Computer-İntegrated Manufacturing, 29(4), 232-247.
• Weller, C., Kleer, R., and Piller, F. T. (2015). Economic İmplications of 3D Printing: Market Structure Models in Light of Additive Manufacturing Revisited. International Journal of Production Economics, 164, 43-56.
• Wided, G., David, C., and Yannick, N., (2009). A Maturity Model for Enterprise İnteroperability, On the Move to Meaningful Internet Systems: OTM 2009 Workshops. Lecture Notes in Computer Science, 5872, 216– 225.
• Willcocks, L. P. (2002). How Radical Was IT-Enabled BPR? Evidence on Financial and Business Impacts. International Journal of Flexible Manufacturing Systems, 14(1), 11–31. doi:10.1023/A:101380 6417513.
• Wu, L., Yue, X., Jin, A., and Yen, D. C. (2016). Smart Supply Chain Management: A Review and İmplications for Future Research. The International Journal of Logistics Management, 27(2), 395–417.
• Xia, F., Yang, L. T., Wang, L., and Vinel, A. (2012). Internet of Things. International Journal of Communication Systems, 25(9), 1101-1102.
• Xu LD, He W, and Li S. (2014). Internet of Things in İndustries: A survey. IEEE Trans Ind Inform, 10(4), 2233-2243.
• Xu X. (2017). Machine Tool 4.0 For The New Era of Manufacturing. Int J Adv Manuf Tech, 92(5–8), 1893–1900.
• Xu, Li Da., Eric L. Xu, and Ling Li. (2018). Industry 4.0: State of the Art and Future Trends. International Journal of Production Research, 56(8), 2941–2962. doi:10.1080/00207543.2018.1444806.
• Xu, X. (2012). From Cloud Computing to Cloud Manufacturing. Robotics and Computer-İntegrated Manufacturing, 28(1), 75-86.
• Yew AWW, Ong SK, and Nee AYC. (2016). Towards A Griddable Distributed Manufacturing System with Augmented Reality İnterfaces. Robot Com-Int Manuf, 39, 43–55.
• Zhang, G., Yang, Y., and Yang, G. (2023). Smart Supply Chain Management in Industry 4.0: The Review, Research Agenda And Strategies in North America. Annals of Operations Research, 322(2), 1075-1117.
• Zhong RY, Huang GQ, Lan S, Dai QY, Chen X, and Zhang T. (2015b). A Big Data Approach for Logistics Trajectory Discovery From RFID-Enabled Production Data. Int J Prod Econ, 165, 260–272.
• Zhong RY, Huang GQ, Lan S, Dai QY, Zhang T, v and Xu C. (2015). A Two-Level Advanced Production Planning and Scheduling Model for RFID-Enabled Ubiquitous Manufacturing. Adv Eng Inform. 29(4), 799–812.
• Zhong RY, Newman ST, and Huang GQ, Lan S. (2016). Big Data For Supply Chain Management in the Service and Manufacturing Sectors: Challenges, Opportunities and Future Perspectives. Comput Ind Eng, 101, 572–91.
• Zhong, R. Y., Xu, X., Klotz, E., and Newman, S. T. (2017). Intelligent Manufacturing in the Context of İndustry 4.0: A Review. Engineering, 3(5), 616-630.
• Zhou, W., Piramuthu, S., Chu, F., and Chu, C. (2017). RFID-Enabled Flexible Warehousing. Decision Support Systems, 98, 99-112.
• Zou J, Chang Q, Arinez J, Xiao G, and Lei Y. (2017), Dynamic Production System Diagnosis and Prognosis Using Model-Based Data-Driven Method. Expert Syst Appl, 80, 200–209.