Deep Integration of Intelligent Logistics and Supply Chain under Digital Transformation Empirical Evidence of Inventory Precision Control and Cost Optimization Supported by IoT Technology
Main Article Content
Abstract
Driven by the digital economy and industrial digital transformation, the deep integration of intelligent logistics and supply chains has become a key approach to improving operational efficiency and resilience. Wireless sensing and electromagnetic information transmission enabled by Internet of Things (IoT) technologies provide essential support for real-time perception and intelligent coordination in modern logistics systems. Traditional supply chains still suffer from inventory backlogs, demand forecasting deviations, high operational costs, and information asymmetry. To address these challenges, this study constructs a four-in-one analytical framework integrating IoT technology, inventory precision control, cost optimization, and supply chain integration. The proposed framework systematically investigates the internal mechanisms of intelligent logistics under digital transformation and emphasizes the application of IoT-enabled real-time data acquisition, information exchange, and intelligent decision-making for precise inventory management. The results demonstrate that the proposed approach provides an effective pathway for improving inventory control accuracy, reducing operational costs, and enhancing supply chain collaboration, offering valuable guidance for intelligent logistics systems supported by wireless communication and electromagnetic sensing technologies..
Downloads
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
S. Deepti, K. Bijendra, S. Samayveer, et al., “A Secure IoT-Based Mutual Authentication for Healthcare Applications in Wireless Sensor Networks Using ECC,” International Journal of Healthcare Information Systems and Informatics (IJHISI), vol. 16, no. 2, pp. 21-48, 2021, doi: 10.4018/IJHISI.20210401.OA2.
H. John, T. Stephen, M. Subhas, et al., “A randomised control trial for measuring student engagement through the Internet of Things and serious games,” Internet of Things, vol. 13, Art. no. 100332, 2021, doi: 10.1016/j.iot.2020.100332.
G. Martin, B. Joakim, P. Francesca, et al., “Evaluating the performance of the OSCORE security protocol in constrained IoT environments,” Internet of Things, vol. 13, Art. no. 100333, 2021, doi: 10.1016/j.iot.2020.100333.
A. Ramsha, C. Yueyun, H. Bilal, et al., “CR-IoTNet: Machine learning based joint spectrum sensing and allocation for cog nitive radio enabled IoT cellular networks,” Ad Hoc Networks, vol. 112, Art. no. 102390, 2021, doi: 10.1016/j.adhoc.2020.102390.
N. Sameena, “Detection of Phishing in Internet of Things Using Machine Learning Approach,” International Journal of Digital Crime and Forensics (IJDCF), vol. 13, no. 2, pp. 1-15, 2021, doi: 10.4018/IJDCF.2021030101.
B.D, “D, Fadi A,” Lightweight authentication for IoT/Cloud-based forensics in intelligent data computing. Future Generation Computer Systems, vol. 116, pp. 406-425, 2021, doi: 10.1016/j.future.2020.11.010.
R. Hasan M, R. Asifur M N A, A. Habiba U, et al., “IoT Botnet Detection Using Various One-Class Classifiers,” Vietnam Journal of Computer Science, pp. 8(2), 2021, doi: 10.1142/S2196888821500123.
M.C, “R A, R,” B A, M. A P O, et al. Multifaceted infrastructure for self-adaptive IoT systems. Information and Software Technology, vol. 132, Art. no. 106505, 2021, doi: 10.1016/J.INFSOF.2020.106505.
G. Mengmeng, S. Firdous N, F. Xiping, et al., “Towards a deep learning-driven intrusion detection approach for Internet of Things,” Computer Networks, vol. 186, 2021, doi: 10.1016/J.COMNET.2020.107784.
I. Bakir, M. Aerts-Veenstra, J. Roodbergen K, et al., “Supply chain and logistics in digital transformation,” A Research Agenda for Digital Transformation, pp. 187-224, 2024, doi: 10.4337/9781035306435.00014.
H. Timber, L. Minh T P, N. Nhan, et al., “Business model innovation through the application of the Internet-of-Things: A comparative analysis,” Journal of Business Research, vol. 126, pp. 126-136, 2021, doi: 10.1016/J.JBUSRES.2020.12.034.
L. Qiang, S. Songlin, Y. Xueguang, et al., “Ambient backscatter communication-based smart 5G IoT network,” EURASIP Journal on Wireless Communications and Networking, Art. no. 2021(1), 2021, doi: 10.1186/S13638-021-01917-3.
R. Khaled and C. Jieren, “Adaptive XACML access policies for heterogeneous distributed IoT environments,” Information Sciences, vol. 548, pp. 135-152, 2021, doi: 10.1016/j.ins.2020.09.051.
P. Alejandro, C. Carlos, and P. Ernesto, “Modelling digital avatars: A tuple space approach,” Science of Computer Programming, pp. 203, 2021, doi: 10.1016/J.SCICO.2020.102583.
N. Agafonova A, “Digital transformation of logistics,” Vestnik of Samara State University of Economics, vol. 9, no. 203, pp. 18-22, 2021, doi: 10.46554/1993-0453-2021-9-203-18-22.
A. Nazir, S. Sholla, and A. Bashir, “An Ontology based Approach for Context-Aware Security in the Internet of Things (IoT),” International Journal of Wireless and Microwave Technologies(IJWMT), vol. 11, no. 1, pp. 28-46, 2021, doi: 10.5815/IJWMT.2021.01.04.
J. Mukhopadhyay, V. Singh K, S. Mukhopadhyay, et al., “A truthful budget feasible mechanism for IoT-based participa tory sensing with incremental arrival of budget,” Journal of Ambient Intelligence and Humanized Computing, 2021, doi: 10.1007/S12652-020-02844-9.
A. Kerem and K. Ömer, “IoT based intelligence for proactive waste management in Quick Service Restaurants,” Journal of Cleaner Production, pp. 284, 2021, doi: 10.1016/J.JCLEPRO.2020.125401.
R. Akpalu, “Digital Transformation in Logistics: Strategic Use of Mathematics for Supply Chain Optimization,” International Journal of Research and Innovation in Social Science, vol. IX, no. I, pp. 4155-4163, 2025, doi: 10.47772/ijriss.2025.9010324.
A. Toymentseva I, D. Chichkina V, and A. Shafieva M, “Digital Transformation of Transport Logistics Under Current Conditions,” Lecture Notes in Networks and Systems, pp. 355-362, 2022, doi: 10.1007/978-3-030-83175-2_45.
R. Stasiak-Betlejewska and K. Czarczyk, “Digital Transformation of Logistics: How Modern Technologies Increase Supply Chain Safety,” System Safety: Human - Technical Facility - Environment, vol. 6, no. 1, pp. 410-420, 2024, doi: 10.2478/czoto-2024-0042.
J. Maecka, M. Grodecka, and A. Koliski, “Digital Transformation in Logistics: Adoption of Intelligent Technologies and Determinants in Supply Chain Enterprises,” Horizons of Politics / Horyzonty Polityki, pp. 16(57), 2025, doi: 10.35765/HP.2919.
A. Volkova, Y. Nikitin, and V. Plotnikov, “Digital transformation of procurement logistics,” Economics and Management, 2022, doi: 10.35854/1998-1627-2022-8-778-785.
H. Tsikh and V. Suhoversha, “Logistics in the context of digital transformation,” Galician economic journal, vol. 91, no. 6, pp. 40-48, 2024, doi: 10.33108/galicianvisnyk_tntu2024.06.040.
S. Pan, D. Trentesaux, D. McFarlane, B. Montreuil, E. Ballot, and Q. Huang G, “Digital interoperability and transforma tion in logistics and supply chain management: Editorial,” Computers in Industry, pp. 129(1), 2021, doi: 10.1016/j.compind.2021.103462.
T. Tran H, T. Pham Q T, and V. Nguyen P, “Digital transformation solution implementation risk in logistics and supply chain industry,” Discover Sustainability, pp. 7(1), 2026, doi: 10.1007/s43621-026-02603-4.
E. Ali A. A., Z. Xinli, M. Ul Hoque M., et al., “Impact of Digital Transformation of Logistics and Supply Chain in the Automotive Manufacturing Industry,” Journal of Business & Management Studies, vol. 7, no. 10, 2025, doi: 10.32996/jbms.2025.7.10.3.