Efficient practices of cognitive technology application for smart manufacturing

• Autorzy: Sira Mariya

Identyfikatory

DOI

10.2478/mspe-2022-0023

• Języki publikacji: EN

Abstrakty

EN

Cognitive manufacturing (CM) provides for the merging of sensor-based information, advanced analytics, and cognitive technologies, mainly machine learning in the context of Industry 4.0. Manufacturers apply cognitive technologies to review current business metrics, solve essential business problems, generate new value in their manufacturing data and improve quality. The article investigates four powerful applications for cognitive manufacturing and their influence on a company`s maintenance. The study aims to observe kinds of cognitive technology applications for smart manufacturing, distinguish their prospective gains for manufacturers and provide successful examples of their adoption. The analysis is based on the literature and report review. Assessment of the cases of technology adoption proves that cognitive manufacturing provides both enhanced knowledge management and helps organizations improve fundamental business measurements, such as productivity, product reliability, quality, safety, and yield while reducing downtime and lowering costs.

Słowa kluczowe

EN

cognitive manufacturing; cognitive technology; Industry 4.0

• Wydawca: STE GROUP
• Czasopismo: Management Systems in Production Engineering
• Rocznik: 2022
• Tom: nr 2 (30)
• Strony: 187--191
• Opis fizyczny: Bibliogr. 29 poz., tab.

Twórcy

autor

Sira Mariya

• Silesian University of Technology

• https://orcid.org/0000-0002-9970-1549

Bibliografia

• [1] B.E.L.R. Flaih, D. Yuvaraj, S.K.A. Jayanthiladevi and T.S. Kumar, "Use Case of Artificial Intelligence in Machine Learning Manufacturing 4.0," International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), 2019, pp. 656-659, doi: 10.1109/ICCIKE47802.2019.9004327.
• [2] S. Wang, J. Wan, D. Li, and C. Zhang, “Implementing Smart Factory of Industrie 4.0: An Outlook,” International Journal of Distributed Sensor Networks, vol. 12, no. 1, p. 3159805, Jan. 2016, doi: 10.1155/2016/3159805.
• [3] Deloitte, “2022 manufacturing industry outlook,” 2021. Accessed: Feb. 18, 2022. [Online]. Available: https://www2.deloitte.com/content/dam/Deloitte/us/Documents/energy-resources/us2022-manufacturing-industry-outlook.pdf.
• [4] McKinsey & Company, “Global survey: The state of AI in 2021|McKinsey,” www.mckinsey.com, Dec. 08, 2021. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-aiin-2021 (accessed Feb. 18, 2022).
• [5] A. Kwilinski and A. Kuzior. “Cognitive technologies in the management and formation of directions of the priority development of industrial enterprises”, Management Systems in Production Engineering, 2020, 28(2), pp. 133-138.
• [6] P. Fobel and A. Kuzior, “The future (Industry 4.0) is closer than we think. Will it also be ethical?,” Proceedings of the International Conference of Computational Methods in Sciences and Engineering 2019 (ICCMSE-2019), 2019, doi: 10.1063/1.5137987.
• [7] S. Phuyal, D. Bista, and R. Bista, “Challenges, Opportunities and Future Directions of Smart Manufacturing: A State of Art Review,” Sustainable Futures, vol. 2, p. 100023, 2020, doi: 10.1016/j.sftr.2020.100023.
• [8] P. Grefen, I. Vanderfeesten, K. Traganos, Z. DomagalaSchmidt, and J. van der Vleuten, “Advancing Smart Manufacturing in Europe: Experiences from Two Decades of Research and Innovation Projects,” Machines, vol. 10, no. 1, p. 45, Jan. 2022, doi: 10.3390/machines10010045.
• [9] K.-D. Thoben, S. Wiesner, and T. Wuest, “‘Industrie 4.0’ and Smart Manufacturing – A Review of Research Issues and Application Examples,” International Journal of Automation Technology, vol. 11, no. 1, pp. 4-16, Jan. 2017, doi: 10.20965/ijat.2017.p0004.
• [10] “Cognitive Manufacturing: An Overview and Four Applications that are Transforming Manufacturing Today,” 2018. [Online]. Available: https://www.ibm.com/downloads/cas/VDNKMWM6.
• [11] I. B. M. Contributor, “IBM BrandVoice: How Cognitive Computing And The IoT Can Transform Manufacturing To Please Customers,” Forbes, Aug. 11, 2016. https://www.forbes.com/sites/ibm/2016/08/11/howcognitive-computing-and-the-iot-can-transform-manufacturing-to-please-customers/?sh=2dc93097272f (accessed Feb. 19, 2022).
• [12] P. Moens et al., “Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications,” Sensors, vol. 20, no. 15, p. 4308, Aug. 2020, doi: 10.3390/s20154308.
• [13] S. Iarovyi, J.L.M. Lastra, R. Haber, and R. del Toro, “From artificial cognitive systems and open architectures to cognitive manufacturing systems,” 2015 IEEE 13th International Conference on Industrial Informatics (INDIN), vol. pp. 1225-1232, Jul. 2015, doi: 10.1109/indin.2015.7281910.
• [14] Deloitte, “Exponential technologies in manufacturing,” 2018. Accessed: Feb. 19, 2022. [Online]. Available: https://www.compete.org/storage/reports/exponential_technologies_2018_study.pdf.
• [15] Kiritsis, D., Hodkiewicz, M., Lazaro, O., Lee, J., Ni, J., eds. “Data-Driven Cognitive Manufacturing – Applications in Predictive Maintenance and Zero Defect Manufacturing”. Lausanne: Frontiers Media SA. 2021, doi: 10.3389/978-2- 88966-583-9
• [16] R. Cioffi, M. Travaglioni, G. Piscitelli, A. Petrillo, and F. De Felice, “Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions,” Sustainability, vol. 12, no. 2, p. 492, Jan. 2020, doi: 10.3390/su12020492.
• [17] T. Kalsoom et al., “Impact of IoT on Manufacturing Industry 4.0: A New Triangular Systematic Review,” Sustainability, vol. 13, no. 22, p. 12506, Nov. 2021, doi: 10.3390/su132212506.
• [18] S. Bonnaud, C. Didier, and A. Kohler, “Industry 4.0 and Cognitive Manufacturing Architecture Patterns, Use Cases and IBM Solutions,” Sep. 2019. [Online]. Available: https://www.ibm.com/downloads/cas/M8J5BA6R.
• [19] D.R. Sjödin, V. Parida, M. Leksell, and A. Petrovic, “Smart Factory Implementation and Process Innovation,” Research-Technology Management, vol. 61, no. 5, pp. 22-31, Sep. 2018, doi: 10.1080/08956308.2018.1471277.
• [20] M.F. Zaeh et al., “The Cognitive Factory,” Springer Series in Advanced Manufacturing, pp. 355-371, doi: 10.1007/978- 1-84882-067-8_20.
• [21] M.S. Sheikh, “Cognitive Manufacturing in Perspective of Future Manufacturing Industries,” International Journal for Research in Applied Science and Engineering Technology, vol. 8, no. 10, pp. 862-866, Oct. 2020, doi: 10.22214/ijraset.2020.32041.
• [22] A.V. Carvalho, A. Chouchene, T.M. Lima, and F. CharruaSantos, “Cognitive Manufacturing in Industry 4.0 toward Cognitive Load Reduction: A Conceptual Framework,” Applied System Innovation, vol. 3, no. 4, p. 55, Dec. 2020, doi: 10.3390/asi3040055.
• [23] D. Schatsky, C. Muraskin, and M. Wagner, “Complimentary article reprint Cognitive technologies The real opportunities for business,” 2015. Accessed: Apr. 11, 2022. [Online]. Available: https://www2.deloitte.com/tr/en/pages/technology-media-and-telecommunications/articles/cognitive-technologies.html
• [24] M. Lester and A. Htet, “What are cognitive technologies and how are they classified? – Intelligent Automation Blog|Deloitte Australia,” Deloitte, 2019. https://www2.deloitte.com/au/en/blog/intelligent-automation-blog/2019/what-are-cognitive-technologies-howthey-classified.html (accessed Apr. 11, 2022).
• [25] J. Soldatos, O. Lazaro, and F. Cavadini, “The Digital Shopfloor: Industrial Automation in the Industry 4.0 Era,” The Digital Shopfloor: Industrial Automation in the Industry 4.0 Era, vol. pp. 3-46, pp. 1-496, Apr. 2019, doi: 10.13052/rp-9788770220408.
• [26] P. Baruchelli, F. Botto, and A. Cimatti (FBK), “End-to-end digitized production test beds Overview on maturity of AI innovations in manufacturing,” Dec. 2020. [Online]. Available: https://eit.europa.eu/sites/default/files/overview_on_maturity_of_ai_innovations_in_manufacturing_20529-d11.pdf.
• [27] A. Kuzior and M. Staszek. Energy management in the railway industry: a case study of rail freight carrier in Poland. Energies, 2021, 14 iss. 21 pp. 1-21 (art. no. 6875) https://doi.org/10.3390/en14216875
• [28] A. Kuzior, A. Kwilinski and V. Tkachenko. Sustainable development of organizations based on the combinatorial model of artificial intelligence. Entrepreneurship and Sustainability, 2019, 7(2), 1353-1376. http://doi.org/10.9770/jesi.2019.7.2(39)
• [29] V. Tkachenko, A. Kuzior and A. Kwilinski. Introduction of artificial intelligence tools into the training methods of entrepreneurship activities. Journal of Entrepreneurship Education, 2019, 22(6), pp. 1-10. Retrieved from https://www.abacademies.org/articles/Introduction-ofartificialintelligence-tools-1528-2651-22-6-477.pdf.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023).