Industrial Engineering 4.0 Strategies to Improve Manufacturing Efficiency and Productivity
Article Sidebar
-
Engineering, Efficiency, Productivity
Abstract
The Industrial Revolution 4.0 has brought significant changes in the manufacturing sector through the application of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics. The application of these technologies aims to address the challenges of improving efficiency and productivity in an increasingly complex and competitive manufacturing environment. This research aims to identify and analyse effective strategies in implementing Industry 4.0 techniques to improve operational efficiency and productivity in the manufacturing sector. This research uses a case study method on several manufacturing companies that have implemented Industry 4.0 technology. Data were collected through interviews, direct observation, and analysis of company documents. Qualitative and quantitative approaches were used to analyse the data obtained. The results showed that the implementation of IoT, AI, and big data analytics technologies significantly improved efficiency and productivity. Successful implementation involves good integration between technology and business processes, employee training, and commitment from top management. Companies that implement these strategies successfully reduce downtime, improve product quality, and speed up production time. The research concludes that the right Industry 4.0 strategy can deliver significant improvements in manufacturing efficiency and productivity. The key to success lies in thorough technology integration, improved workforce competencies, and strong management support. Effective implementation of these strategies can give companies a competitive advantage in the global market
Full text article
References
Ait Moussa, K., Selmaoui, S., & Ouzennou, N. (2024). The questionnire on learning strategies “Mes Outils de Travail Intellectuel”: Adaptation and validation among Moroccan nursing students. Educación Médica, 25(4), 100905. https://doi.org/10.1016/j.edumed.2024.100905
Alma Çall?, B., & Ediz, Ç. (2023). Top concerns of user experiences in Metaverse games: A text-mining based approach. Entertainment Computing, 46, 100576. https://doi.org/10.1016/j.entcom.2023.100576
Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133–148. https://doi.org/10.1080/01587919.2018.1553562
Alshater, M. (2022). Exploring the Role of Artificial Intelligence in Enhancing Academic Performance: A Case Study of ChatGPT. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4312358
Atitallah, S. B., Driss, M., Boulila, W., & Ghézala, H. B. (2020). Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions. Computer Science Review, 38, 100303. https://doi.org/10.1016/j.cosrev.2020.100303
Baek, S., Kim, S., & Rhee, T. (2021). [Editors’ Remarks] Special Theme 1: Globalization in the Era of COVID-19. Journal of Economic Integration, 36(1), 1–2. https://doi.org/10.11130/jei.2021.36.1.1
Besser, A., Flett, G. L., & Zeigler-Hill, V. (2022). Adaptability to a sudden transition to online learning during the COVID-19 pandemic: Understanding the challenges for students. Scholarship of Teaching and Learning in Psychology, 8(2), 85–105. https://doi.org/10.1037/stl0000198
Betlem, K., Kaur, A., Hudson, A. D., Crapnell, R. D., Hurst, G., Singla, P., Zubko, M., Tedesco, S., Banks, C. E., Whitehead, K., & Peeters, M. (2019). Heat-Transfer Method: A Thermal Analysis Technique for the Real-Time Monitoring of Staphylococcus aureus Growth in Buffered Solutions and Digestate Samples. ACS Applied Bio Materials, 2(9), 3790–3798. https://doi.org/10.1021/acsabm.9b00409
Bhuiyan, M. N., Rahman, M. M., Billah, M. M., & Saha, D. (2021). Internet of Things (IoT): A Review of Its Enabling Technologies in Healthcare Applications, Standards Protocols, Security, and Market Opportunities. IEEE Internet of Things Journal, 8(13), 10474–10498. https://doi.org/10.1109/JIOT.2021.3062630
Bice, K., & Kroll, J. F. (2019). English only? Monolinguals in linguistically diverse contexts have an edge in language learning. Brain and Language, 196, 104644. https://doi.org/10.1016/j.bandl.2019.104644
Castañeda-Babarro, A., Arbillaga-Etxarri, A., Gutiérrez-Santamaría, B., & Coca, A. (2020). Physical Activity Change during COVID-19 Confinement. International Journal of Environmental Research and Public Health, 17(18), 6878. https://doi.org/10.3390/ijerph17186878
Chow, J. C. L., Sanders, L., & Li, K. (2023). Impact of ChatGPT on medical chatbots as a disruptive technology. Frontiers in Artificial Intelligence, 6, 1166014. https://doi.org/10.3389/frai.2023.1166014
Cohen, L., Malloy, C., & Nguyen, Q. (2020). Lazy Prices. The Journal of Finance, 75(3), 1371–1415. https://doi.org/10.1111/jofi.12885
Dong, H., & Liu, Y. (2023). Metaverse Meets Consumer Electronics. IEEE Consumer Electronics Magazine, 12(3), 17–19. https://doi.org/10.1109/MCE.2022.3229180
Dube, B. (2020). Rural online learning in the context of COVID 19 in South Africa: Evoking an inclusive education approach. Multidisciplinary Journal of Educational Research, 10(2), 135. https://doi.org/10.17583/remie.2020.5607
Dubey, J. P. (2021). Toxoplasmosis of Animals and Humans (3rd ed.). CRC Press. https://doi.org/10.1201/9781003199373
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Else, H. (2023). Abstracts written by ChatGPT fool scientists. Nature, 613(7944), 423–423. https://doi.org/10.1038/d41586-023-00056-7
Favale, T., Soro, F., Trevisan, M., Drago, I., & Mellia, M. (2020). Campus traffic and e-Learning during COVID-19 pandemic. Computer Networks, 176, 107290. https://doi.org/10.1016/j.comnet.2020.107290
Gosal, A. S., Geijzendorffer, I. R., Václavík, T., Poulin, B., & Ziv, G. (2019). Using social media, machine learning and natural language processing to map multiple recreational beneficiaries. Ecosystem Services, 38, 100958. https://doi.org/10.1016/j.ecoser.2019.100958
Hao, J., & Ho, T. K. (2019). Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language. Journal of Educational and Behavioral Statistics, 44(3), 348–361. https://doi.org/10.3102/1076998619832248
Ibrar, I., Naji, O., Sharif, A., Malekizadeh, A., Alhawari, A., Alanezi, A. A., & Altaee, A. (2019). A Review of Fouling Mechanisms, Control Strategies and Real-Time Fouling Monitoring Techniques in Forward Osmosis. Water, 11(4), 695. https://doi.org/10.3390/w11040695
Jansen, M., Donkervliet, J., Trivedi, A., & Iosup, A. (2023). Can My WiFi Handle the Metaverse? A Performance Evaluation Of Meta’s Flagship Virtual Reality Hardware. Companion of the 2023 ACM/SPEC International Conference on Performance Engineering, 297–303. https://doi.org/10.1145/3578245.3585022
Jia, L., Du, Y., Chu, L., Zhang, Z., Li, F., Lyu, D., Li, Y., Li, Y., Zhu, M., Jiao, H., Song, Y., Shi, Y., Zhang, H., Gong, M., Wei, C., Tang, Y., Fang, B., Guo, D., Wang, F., … Qiu, Q. (2020). Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: A cross-sectional study. The Lancet Public Health, 5(12), e661–e671. https://doi.org/10.1016/S2468-2667(20)30185-7
Kang, D., Choi, H., & Nam, S. (2022). Learning Cultural Spaces: A Collaborative Creation of a Virtual Art Museum Using Roblox. International Journal of Emerging Technologies in Learning (iJET), 17(22), 232–245. https://doi.org/10.3991/ijet.v17i22.33023
Kapasia, N., Paul, P., Roy, A., Saha, J., Zaveri, A., Mallick, R., Barman, B., Das, P., & Chouhan, P. (2020). Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal, India. Children and Youth Services Review, 116, 105194. https://doi.org/10.1016/j.childyouth.2020.105194
Kolb, J. (2023). Muslim diversity, religious formation and Islamic religious education. Everyday practical insights into Muslim parents’ concepts of religious education in Austria. British Journal of Religious Education, 45(2), 172–185. https://doi.org/10.1080/01416200.2021.1911787
Larchen Costuchen, A., Darling, S., & Uytman, C. (2021). Augmented reality and visuospatial bootstrapping for second-language vocabulary recall. Innovation in Language Learning and Teaching, 15(4), 352–363. https://doi.org/10.1080/17501229.2020.1806848
Loewen, S., Crowther, D., Isbell, D. R., Kim, K. M., Maloney, J., Miller, Z. F., & Rawal, H. (2019). Mobile-assisted language learning: A Duolingo case study. ReCALL, 31(3), 293–311. https://doi.org/10.1017/S0958344019000065
Maulida, A., Hanif, H., Kamal, M., & Suryani Oktari, R. (2023). Roblox-based tsunami survival game: A tool to stimulate early childhood disaster preparedness skills. E3S Web of Conferences, 447, 02003. https://doi.org/10.1051/e3sconf/202344702003
Memon, N. A., Chown, D., & Alkouatli, C. (2021). Descriptions and enactments of Islamic pedagogy: Reflections of alumni from an Islamic Teacher Education Programme. Pedagogy, Culture & Society, 29(4), 631–649. https://doi.org/10.1080/14681366.2020.1775687
Oulaich, S. (2020). Pedagogy in the Digital Age: Making Learning Effective and Engaging for Students. In M. Ben Ahmed, A. A. Boudhir, D. Santos, M. El Aroussi, & ?. R. Karas (Eds.), Innovations in Smart Cities Applications Edition 3 (pp. 168–182). Springer International Publishing. https://doi.org/10.1007/978-3-030-37629-1_14
Pardo, A., Jovanovic, J., Dawson, S., Gaševi?, D., & Mirriahi, N. (2019). Using learning analytics to scale the provision of personalised feedback. British Journal of Educational Technology, 50(1), 128–138. https://doi.org/10.1111/bjet.12592
Payal, R., Sharma, N., & Dwivedi, Y. K. (2024). Unlocking the impact of brand engagement in the metaverse on Real-World purchase intentions: Analyzing Pre-Adoption behavior in a futuristic technology platform. Electronic Commerce Research and Applications, 65, 101381. https://doi.org/10.1016/j.elerap.2024.101381
Pokhrel, S., & Chhetri, R. (2021). A Literature Review on Impact of COVID-19 Pandemic on Teaching and Learning. Higher Education for the Future, 8(1), 133–141. https://doi.org/10.1177/2347631120983481
Selwyn, N. (2019). What’s the Problem with Learning Analytics? Journal of Learning Analytics, 6(3). https://doi.org/10.18608/jla.2019.63.3
Shadiev, R., & Yang, M. (2020). Review of Studies on Technology-Enhanced Language Learning and Teaching. Sustainability, 12(2), 524. https://doi.org/10.3390/su12020524
Spernjak, A. (2021). Using ICT to Teach Effectively at COVID-19. 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO), 617–620. https://doi.org/10.23919/MIPRO52101.2021.9596878
Teimouri, Y., Plonsky, L., & Tabandeh, F. (2022). L2 grit: Passion and perseverance for second-language learning. Language Teaching Research, 26(5), 893–918. https://doi.org/10.1177/1362168820921895
Yudiawan, A., Sunarso, B., Suharmoko, S., Sari, F., & Ahmadi, A. (2021). Successful online learning factors in COVID-19 era: Study of Islamic higher education in West Papua, Indonesia. International Journal of Evaluation and Research in Education (IJERE), 10(1), 193. https://doi.org/10.11591/ijere.v10i1.21036
Authors
Copyright (c) 2024 Lie Jie
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.