The Impact of Artificial Intelligence and Big Data Integration on Decision-Making and Supply Chain Efficiency in Modern Logistics
DOI:
https://doi.org/10.35870/ijmsit.v6i1.6344Keywords:
Artificial Intelligence, Big Data, Decision-Making, Operational Efficiency, Supply Chain, LogisticsAbstract
This study examines the impact of the integration of Artificial Intelligence (AI) and Big Data on decision-making and operational efficiency in the logistics sector. AI accelerates data processing, while Big Data enables large-scale analysis to make more informed decisions. Both technologies can improve planning, distribution, and equipment monitoring, as well as enhance supply chain operability by reducing human error. The study included case studies of 50 logistics companies that have adopted AI and Big Data. Using this quantitative method, the research team developed survey questions based on the four parameters listed above and answered them by interviewing representatives from each company's management department. The results show that 70% of companies have implemented AI across various operational aspects. Sixty percent of companies are fully utilizing Big Data. AI improves demand forecast accuracy by 30% and reduces inventory waste by 25%. AI implementation saves 15-20% on delivery time in route management, while reducing operational costs. However, there are several drawbacks: it is expensive to build systems using all these technologies, there is a shortage of skilled labor, and companies struggle to integrate new systems with existing legacy systems. This study also shows that organizational readiness plays a critical role in leveraging the technical potential of these two technologies to improve supply chain operational efficiency.
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References
Aljohani, A. (2023). Predictive Analytics and Machine Learning for Real-Time Supply Chain Risk Mitigation and Agility. Sustainability, 15(20), 15088. https://doi.org/10.3390/su152015088
Anam, S., Abhirama, N., Hasan, M. S., & Diofani, H. (2025). Analisis Sistem Rantai Pasok Modern: Studi Literatur Terhadap Perkembangan Terbaru. Menulis: Jurnal Penelitian Nusantara, 1(7), 210-213. https://doi.org/10.59435/menulis.v1i7.526
Arfah, M., Suherlan, S., & Pramono, S. A. (2025). Eksplorasi Transformasi Digital dalam MSDM: Dampak Integrasi Artificial Intelligence dan Big Data Analytics terhadap Pengambilan Keputusan Strategis. Jurnal Minfo Polgan, 14(1), 183-192. https://doi.org/10.33395/jmp.v14i1.14673
Belhadi, A., Mani, V., Kamble, S., Khan, S., & Verma, S. (2021). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation. Annals of Operations Research, 333(2-3), 627-652. https://doi.org/10.1007/s10479-021-03956-x
Danendra Bramantyo, & Muhammad Yasin. (2025). Digitalisasi Rantai Pasok dan Efeknya terhadap Efisiensi Produksi dalam Sektor Industri. Jurnal Riset Ekonomi Dan Akuntansi, 3(4), 110–112. https://doi.org/10.54066/jrea-itb.v3i4.3675
Duan, Y., Edwards, J., & Dwivedi, Y. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Eryc, E., & Indasari Deu. (2024). INTEGRASI TEKNOLOGI DIGITAL DAN AI DALAM MEMPERKUAT AKUNTABILITAS PADA OPERASI MANAJEMEN RANTAI PASOKAN: ANALISIS LITERATUR SISTEMATIS. TEKNIMEDIA: Teknologi Informasi Dan Multimedia, 5(2), 200 -. https://doi.org/10.46764/teknimedia.v5i2.219
Gading Rayya Samita, Wisesa, W., Setiawan, E. D., Respati K, I., & Hidayat, R. (2024). Integrasi Artificial Intelligence dan Teori Bounded Rationality dalam Mengatasi Ketidakpastian Pengambilan Keputusan Bisnis di Era Big Data. Jurnal Bisnis Dan Komunikasi Digital, 2(2), 12. https://doi.org/10.47134/jbkd.v2i2.3460
Govindan, K., Cheng, T., Mishra, N., & Shukla, N. (2018). Big data analytics and application for logistics and supply chain management. Transportation Research Part E Logistics and Transportation Review, 114, 343-349. https://doi.org/10.1016/j.tre.2018.03.011
Kache, F. and Seuring, S. (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. https://doi.org/10.1108/ijopm-02-2015-0078
Khan, S., Piprani, A., & Yu, Z. (2022). Supply chain analytics and post-pandemic performance: mediating role of triple-A supply chain strategies. International Journal of Emerging Markets, 18(6), 1330-1354. https://doi.org/10.1108/ijoem-11-2021-1744
Laurenz, O. (2025). Membangun Ketahanan Rantai Pasokan Maritim: Peran Strategis Manajemen Pelabuhan dalam Menghadapi Gangguan Logistik dan Disrupsi Perdagangan Internasional. Jurnal Ilmiah Wahana Pendidikan, 11(1.A), 207-215. Retrieved from https://www.jurnal.peneliti.net/index.php/JIWP/article/view/9597
Listy, V., & Ilham, I. (2025). Revolusi Sistem Informasi Manajemen di Era AI dan Big Data Mengubah Cara Bisnis Bekerja. Simpatik: Jurnal Sistem Informasi dan Informatika, 5(1), 27-36.
Lubis, A. S. (2025). Implementasi Manajemen Risiko Berbasis Ai Dalam Optimalisasi Rantai Pasok Retail Digital Di Indonesia Tahun 2025. Jurnal Ekonomi Dan Manajemen ( JEMBATAN), 2(2), 52-60. Retrieved from https://journal.beaninstitute.id/index.php/jembatan/article/view/104
Purbasari, R., Novel, N. J. A., Kostini, N. (2023). Digitalisasi Logistik dalam Mendukung Kinerja E-Logistic di Era Digital: A Literature Review. JOMBLO: Jurnal Organisasi, Manajemen, Bisnis dan Logistik, 1(2), 177–196. https://jurnal.unpad.ac.id/jomblo/article/view/50762/21289
Raharjo, P. S. (2025). Pengaruh Perkembangan Teknologi 4.0 terhadap Transformasi Logistik Modern TNI Angkatan Laut dalam Mendukung Kesiapan Operasional Alutsista. Jurnal Penelitian Inovatif, 5(4), 3043–3056. https://doi.org/10.54082/jupin.1939
Rolando, B., Ariyanto, K., Alexia, K. R., & Hartanti, R. (2022). PERAN AI DAN BIG DATA DALAM MENGOPTIMALKAN STRATEGI PEMASARAN DIGITAL. AIRA (Artificial Intelligence Research and Applied Learning), 1(1), 1–17. https://doi.org/10.1234/aira.v1i1.31
Romadhona, H., & Zulfairah. (2024). Pengaruh Internet of Things (IoT) dan Big Data terhadap Akurasi Pelacakan Pengiriman Ekspedisi. Jurnal Greenation Ilmu Teknik, 2(2), 75–84. https://doi.org/10.38035/jgit.v2i2.264
Saarikko, T., Westergren, U., & Blomquist, T. (2020). Digital transformation: Five recommendations for the digitally conscious firm. Business Horizons, 63(6), 825-839. https://doi.org/10.1016/j.bushor.2020.07.005
Seyedan, M. and Mafakheri, F. (2020). Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-020-00329-2
Soekirman, A. (2024). Meningkatkan Efisiensi Rantai Pasok melalui Penyedia Logistik, Transportasi Intermoda, Teknologi Informasi, dan Regulasi Pemerintah. Research Journal of Multidisciplinary Research and Development 6(4). https://doi.org/10.38035/rrj.v6i4.839
Sri Sulistyawati, U., & Munawir. (2024). Decoding Big Data: Mengubah Data Menjadi Keunggulan Kompetitif dalam Pengambilan Keputusan Bisnis. Jurnal Manajemen Dan Teknologi, 1(2), 58-71. https://doi.org/10.63447/jmt.v1i2.1114
Zamani, E., Smyth, C., Gupta, S., & Dennehy, D. (2022). Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review. Annals of Operations Research, 327(2), 605-632. https://doi.org/10.1007/s10479-022-04983-y
Zhu, S., Song, J., Hazen, B., Lee, K., & Cegielski, C. (2018). How supply chain analytics enables operational supply chain transparency. International Journal of Physical Distribution & Logistics Management, 48(1), 47-68. https://doi.org/10.1108/ijpdlm-11-2017-0341
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