Smart Automation of Production and Digital Supply Chain
Bibliometric Analysis and Systematic Review
Abstract
Transformasi digital dan otomasi cerdas semakin penting bagi manajemen produksi dan rantai pasok. Hal ini karena perusahaan harus merespons volatilitas permintaan, kompleksitas operasi, dan tekanan keberlanjutan. Penelitian ini bertujuan mengkaji perkembangan penerapan kecerdasan buatan, internet of things, smart manufacturing, dan Industry 4.0/5.0 dalam produksi serta rantai pasok melalui systematic literature review yang diperkuat analisis bibliometrik. Data bibliometrik dianalisis menggunakan Bibliometrix/Biblioshiny terhadap 109 dokumen dari 99 sumber publikasi pada periode 2021-2026. Hasil analisis menunjukkan pertumbuhan publikasi yang tinggi dengan annual growth rate 45,41%, melibatkan 359 penulis, 313 kata kunci penulis, 6.142 referensi, dan international co-authorship 31,19%. Peta co-occurrence memperlihatkan bahwa artificial intelligence, supply chain management, industry 4.0, internet of things, decision making, dan smart manufacturing menjadi simpul utama struktur pengetahuan. Thematic map menunjukkan decision making, internet of things, dan smart manufacturing sebagai motor theme, sedangkan artificial intelligence dan supply chain management menjadi basic theme yang relevan tetapi masih membutuhkan konsolidasi konseptual. Temuan ini menegaskan bahwa transformasi digital tidak cukup dipahami sebagai adopsi teknologi, melainkan sebagai sistem keputusan sosio-teknis yang memadukan data, algoritma, manusia, etika, dan tata kelola. Artikel ini berkontribusi dengan menyintesis agenda penelitian yang menyeimbangkan efisiensi teknis, kolaborasi manusia-AI, interpretabilitas, dan keberlanjutan rantai pasok.
Keywords:
artificial intelligence, bibliometrix, industri 5.0, manajemen produksi, rantai pasok
References
Adadi, A. dan Berrada, M., 2018, Explainable AI: A review of methods and applications, Information Fusion, vol 57, hal 82-91, https://doi.org/10.1016/j.inffus.2019.12.012
Alfayoumi, S., Eltazi, N. dan Elgammal, A., 2023, AI-driven optimization approach based on genetic algorithm in mass customization supplying and manufacturing, International Journal of Advanced Computer Science and Applications, vol 14, no 11. https://10.14569/IJACSA.2023.01411106
Aria, M. dan Cuccurullo, C., 2017, bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, vol 11, no 4, hal 959-975, https://doi.org/10.1016/j.joi.2017.08.007.
Arinez, J. F., Chang, Q., Gao, R. X., Xu, C. dan Zhang, J., 2020, Artificial intelligence in advanced manufacturing: Current status and future outlook, Journal of Manufacturing Science and Engineering, vol 142, no 11, hal 1-16, https://doi.org/10.1115/1.4047855.
Denyer, D. dan Tranfield, D., 2009, Producing a systematic review, Buchanan, D. A. dan Bryman, A. (ed.): The SAGE Handbook of Organizational Research Methods, Sage Publications, London.
Dong, Y., 2022, Optimization and analysis of raw material supply chain based on computational intelligence, Mobile Information Systems, vol 2022, artikel 8683598. https://doi.org/10.1155/2022/8683598
Dixit, C., Haleem, A., & Javaid, M. (2024). Role of Digital Supply Chain in Industry 4.0: A Bibliometric Analysis. Journal of Industrial Integration and Management, 9(4), 495–518. https://doi.org/10.1142/S2424862224500106
González Rodríguez, G., Gonzalez-Cava, J. M. dan Méndez Pérez, J. A., 2020, An intelligent decision support system for production planning based on machine learning, Journal of Intelligent Manufacturing, vol 31, hal 1257-1273. https://doi.org/10.1007/s10845-019-01510-y
Honti, B., Farkas, A., Nagy, Z. K., Pataki, H. dan Nagy, B., 2024, Explainable deep recurrent neural networks for the batch analysis of a pharmaceutical tableting process in the spirit of Pharma 4.0, International Journal of Pharmaceutics, vol 662, artikel 124509. https://doi.org/10.1016/j.ijpharm.2024.124509
Hu, L., Wang, C. dan Fan, T., 2024, Sustainable operation and management of a dynamic supply chain under the framework of a community with a shared future for mankind, Sustainability, vol 16, no 17, artikel 7780. https://doi.org/10.3390/su16177780
Kitchenham, B., 2007, Guidelines for performing systematic literature reviews in software engineering, Technical Report, Keele University and Durham University.
Kritzinger, W., Karner, M., Traar, G., Henjes, J. dan Sihn, W., 2018, Digital twin in manufacturing: A categorical literature review and classification, IFAC-PapersOnLine, vol 51, no 11, hal 1016-1022. https://doi.org/10.1016/j.ifacol.2018.08.474
Lee, J., Bagheri, B. dan Jin, C., 2016, Introduction to cyber manufacturing, Manufacturing Letters, vol 8, hal 11-15. https://doi.org/10.1016/j.mfglet.2016.05.002
Liao, Q. V., Gruen, D. M. dan Miller, S., 2021, Questioning AI: Informing design for explainable AI user experiences, Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, hal 1-16. https://doi.org/10.1145/3411764.3445331
Munn, Z., Peters, M. D. J., Stern, C., Tufanaru, C., McArthur, A. dan Aromataris, E., 2018, Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach, BMC Medical Research Methodology, vol 18, artikel 143. https://doi.org/10.1186/s12874-018-0611-x
Nonaka, I. dan Takeuchi, H., 1995, The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University Press, New York.
Qu, S., Li, X., Lim, M. K., Liu, W., Li, Z., & Wang, Y., 2025, Smart supply chain management: a systematic literature review and future research agenda. International Journal of Logistics Research and Applications. https://doi.org/10.1080/13675567.2025.2569701
Raza, S. A., Govindaluri, S. M., & Bhutta, M. K., 2023, Research themes in machine learning applications in supply chain management using bibliometric analysis tools. Benchmarking, 30(3), 834–867. https://doi.org/10.1108/BIJ-12-2021-0755
Samek, W., Wiegand, T. dan Müller, K. R., 2021, Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models, IEEE Signal Processing Magazine, vol 38, no 1, hal 22-59. https://doi.org/10.1109/MSP.2020.3019877
Sahoo, S. K., Goswami, S. S., Sarkar, S. dan Mitra, S., 2023, A review of digital transformation and Industry 4.0 in supply chain management for small and medium-sized enterprises, Spectrum of Engineering and Management Sciences, vol 1, no 1, hal 58-70. https://doi.org/10.31181/sems1120237j
Siau, K. dan Wang, W., 2020, Artificial intelligence ethics: Ethics of AI and ethical AI, Journal of Database Management, vol 31, no 2, hal 58-68. https://doi.org/10.4018/JDM.2020040105
Shrivastava, A., Praveen, R. V. S., Muhsnhasan, M., Bansal, S., Dwivedi, S. P., & Krishna, O. (2025). Industry 4.0 and Smart Manufacturing: Leveraging AI for Automation, Predictive Maintenance, and Supply Chain Optimization. 2025 International Conference on Computing and Communications, Computingcon 2025. https://doi.org/10.1109/COMPUTINGCON64838.2025.11376637
Song, L., & Cheng, L. (2025). Research Status and Trends in the Field of Digital Supply Chain—A CiteSpace-Based Bibliometric Analysis. Proceedings of 2025 International Conference on Management Science and Computer Engineering, MSCE 2025, 683–689. https://doi.org/10.1145/3760023.3760131
Tao, F., Zhang, M., Liu, Y. dan Nee, A. Y. C., 2018, Digital twin in industry: State-of-the-art, IEEE Transactions on Industrial Informatics, vol 15, no 4, hal 2405-2415. https://doi.org/10.1109/TII.2018.2873186
Tranfield, D., Denyer, D. dan Smart, P., 2003, Towards a methodology for developing evidence-informed management knowledge by means of systematic review, British Journal of Management, vol 14, no 3, hal 207-222. https://doi.org/10.1111/1467-8551.00375
Vlacic, L., Huang, H., Dotoli, M., Wang, Y., Ioannou, P. A., Fan, L., Wang, X., Carli, R., Lv, C., Li, L., Na, X., Han, Q. L. dan Wang, F. Y., 2024, Automation 5.0: The key to systems intelligence and Industry 5.0, IEEE/CAA Journal of Automatica Sinica, vol 11, no 8, hal 1723-1727. https://doi.org/10.1109/JAS.2024.124635
Wang, L., Törngren, M. dan Onori, M., 2020, Current status and advancement of cyber-physical systems in manufacturing, Journal of Manufacturing Systems, vol 49, hal 254-273. https://doi.org/10.1016/j.jmsy.2018.09.004
Wuest, T., Weimer, D., Irgens, C. dan Thoben, K. D., 2016, Machine learning in manufacturing: Advantages, challenges, and applications, Production & Manufacturing Research, vol 4, no 1, hal 23-45. https://doi.org/10.1080/21693277.2016.1192517
Yu, H., 2022, Modeling a remanufacturing reverse logistics planning problem: Some insights into disruptive technology adoption, The International Journal of Advanced Manufacturing Technology, vol 123, hal 4231-4249. https://doi.org/10.21203/rs.3.rs-1940773/v1
Zahid, A., 2025, Exploring the potential of industry 4 . 0 in manufacturing and supply chain systems : Insights and emerging trends from bibliometric analysis. Supply Chain Analytics, 10(January), 100108. https://doi.org/10.1016/j.sca.2025.100108
Alfayoumi, S., Eltazi, N. dan Elgammal, A., 2023, AI-driven optimization approach based on genetic algorithm in mass customization supplying and manufacturing, International Journal of Advanced Computer Science and Applications, vol 14, no 11. https://10.14569/IJACSA.2023.01411106
Aria, M. dan Cuccurullo, C., 2017, bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, vol 11, no 4, hal 959-975, https://doi.org/10.1016/j.joi.2017.08.007.
Arinez, J. F., Chang, Q., Gao, R. X., Xu, C. dan Zhang, J., 2020, Artificial intelligence in advanced manufacturing: Current status and future outlook, Journal of Manufacturing Science and Engineering, vol 142, no 11, hal 1-16, https://doi.org/10.1115/1.4047855.
Denyer, D. dan Tranfield, D., 2009, Producing a systematic review, Buchanan, D. A. dan Bryman, A. (ed.): The SAGE Handbook of Organizational Research Methods, Sage Publications, London.
Dong, Y., 2022, Optimization and analysis of raw material supply chain based on computational intelligence, Mobile Information Systems, vol 2022, artikel 8683598. https://doi.org/10.1155/2022/8683598
Dixit, C., Haleem, A., & Javaid, M. (2024). Role of Digital Supply Chain in Industry 4.0: A Bibliometric Analysis. Journal of Industrial Integration and Management, 9(4), 495–518. https://doi.org/10.1142/S2424862224500106
González Rodríguez, G., Gonzalez-Cava, J. M. dan Méndez Pérez, J. A., 2020, An intelligent decision support system for production planning based on machine learning, Journal of Intelligent Manufacturing, vol 31, hal 1257-1273. https://doi.org/10.1007/s10845-019-01510-y
Honti, B., Farkas, A., Nagy, Z. K., Pataki, H. dan Nagy, B., 2024, Explainable deep recurrent neural networks for the batch analysis of a pharmaceutical tableting process in the spirit of Pharma 4.0, International Journal of Pharmaceutics, vol 662, artikel 124509. https://doi.org/10.1016/j.ijpharm.2024.124509
Hu, L., Wang, C. dan Fan, T., 2024, Sustainable operation and management of a dynamic supply chain under the framework of a community with a shared future for mankind, Sustainability, vol 16, no 17, artikel 7780. https://doi.org/10.3390/su16177780
Kitchenham, B., 2007, Guidelines for performing systematic literature reviews in software engineering, Technical Report, Keele University and Durham University.
Kritzinger, W., Karner, M., Traar, G., Henjes, J. dan Sihn, W., 2018, Digital twin in manufacturing: A categorical literature review and classification, IFAC-PapersOnLine, vol 51, no 11, hal 1016-1022. https://doi.org/10.1016/j.ifacol.2018.08.474
Lee, J., Bagheri, B. dan Jin, C., 2016, Introduction to cyber manufacturing, Manufacturing Letters, vol 8, hal 11-15. https://doi.org/10.1016/j.mfglet.2016.05.002
Liao, Q. V., Gruen, D. M. dan Miller, S., 2021, Questioning AI: Informing design for explainable AI user experiences, Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, hal 1-16. https://doi.org/10.1145/3411764.3445331
Munn, Z., Peters, M. D. J., Stern, C., Tufanaru, C., McArthur, A. dan Aromataris, E., 2018, Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach, BMC Medical Research Methodology, vol 18, artikel 143. https://doi.org/10.1186/s12874-018-0611-x
Nonaka, I. dan Takeuchi, H., 1995, The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University Press, New York.
Qu, S., Li, X., Lim, M. K., Liu, W., Li, Z., & Wang, Y., 2025, Smart supply chain management: a systematic literature review and future research agenda. International Journal of Logistics Research and Applications. https://doi.org/10.1080/13675567.2025.2569701
Raza, S. A., Govindaluri, S. M., & Bhutta, M. K., 2023, Research themes in machine learning applications in supply chain management using bibliometric analysis tools. Benchmarking, 30(3), 834–867. https://doi.org/10.1108/BIJ-12-2021-0755
Samek, W., Wiegand, T. dan Müller, K. R., 2021, Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models, IEEE Signal Processing Magazine, vol 38, no 1, hal 22-59. https://doi.org/10.1109/MSP.2020.3019877
Sahoo, S. K., Goswami, S. S., Sarkar, S. dan Mitra, S., 2023, A review of digital transformation and Industry 4.0 in supply chain management for small and medium-sized enterprises, Spectrum of Engineering and Management Sciences, vol 1, no 1, hal 58-70. https://doi.org/10.31181/sems1120237j
Siau, K. dan Wang, W., 2020, Artificial intelligence ethics: Ethics of AI and ethical AI, Journal of Database Management, vol 31, no 2, hal 58-68. https://doi.org/10.4018/JDM.2020040105
Shrivastava, A., Praveen, R. V. S., Muhsnhasan, M., Bansal, S., Dwivedi, S. P., & Krishna, O. (2025). Industry 4.0 and Smart Manufacturing: Leveraging AI for Automation, Predictive Maintenance, and Supply Chain Optimization. 2025 International Conference on Computing and Communications, Computingcon 2025. https://doi.org/10.1109/COMPUTINGCON64838.2025.11376637
Song, L., & Cheng, L. (2025). Research Status and Trends in the Field of Digital Supply Chain—A CiteSpace-Based Bibliometric Analysis. Proceedings of 2025 International Conference on Management Science and Computer Engineering, MSCE 2025, 683–689. https://doi.org/10.1145/3760023.3760131
Tao, F., Zhang, M., Liu, Y. dan Nee, A. Y. C., 2018, Digital twin in industry: State-of-the-art, IEEE Transactions on Industrial Informatics, vol 15, no 4, hal 2405-2415. https://doi.org/10.1109/TII.2018.2873186
Tranfield, D., Denyer, D. dan Smart, P., 2003, Towards a methodology for developing evidence-informed management knowledge by means of systematic review, British Journal of Management, vol 14, no 3, hal 207-222. https://doi.org/10.1111/1467-8551.00375
Vlacic, L., Huang, H., Dotoli, M., Wang, Y., Ioannou, P. A., Fan, L., Wang, X., Carli, R., Lv, C., Li, L., Na, X., Han, Q. L. dan Wang, F. Y., 2024, Automation 5.0: The key to systems intelligence and Industry 5.0, IEEE/CAA Journal of Automatica Sinica, vol 11, no 8, hal 1723-1727. https://doi.org/10.1109/JAS.2024.124635
Wang, L., Törngren, M. dan Onori, M., 2020, Current status and advancement of cyber-physical systems in manufacturing, Journal of Manufacturing Systems, vol 49, hal 254-273. https://doi.org/10.1016/j.jmsy.2018.09.004
Wuest, T., Weimer, D., Irgens, C. dan Thoben, K. D., 2016, Machine learning in manufacturing: Advantages, challenges, and applications, Production & Manufacturing Research, vol 4, no 1, hal 23-45. https://doi.org/10.1080/21693277.2016.1192517
Yu, H., 2022, Modeling a remanufacturing reverse logistics planning problem: Some insights into disruptive technology adoption, The International Journal of Advanced Manufacturing Technology, vol 123, hal 4231-4249. https://doi.org/10.21203/rs.3.rs-1940773/v1
Zahid, A., 2025, Exploring the potential of industry 4 . 0 in manufacturing and supply chain systems : Insights and emerging trends from bibliometric analysis. Supply Chain Analytics, 10(January), 100108. https://doi.org/10.1016/j.sca.2025.100108
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2026-05-19
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