Research on Innovative Paths for Regional Logistics and Supply Chain Integration: A Perspective on the Scalable Operations of Micro and Small Logistics Enterprises
DOI:
https://doi.org/10.70393/6a6374616d.343038ARK:
https://n2t.net/ark:/40704/JIET.v1n1a08Disciplines:
Intelligent SystemsSubjects:
OtherReferences:
26Keywords:
Micro and Small Logistics Enterprises, Supply Chain Resilience, Lightweight Digital Empowerment, Supply Chain Integrtion, Industry Standardization, Technology TransferAbstract
The complex integration of Industry 4.0 technologies into micro and small logistics enterprises presents profound adoption barriers stemming from severe resource constraints. Navigating the inherent difficulties of fragmented regional networks, this study constructs a dual driven framework combining lightweight digital empowerment with industry standardization to facilitate scalable and high resilience operations. Rather than assuming linear technological diffusion, we formulate advanced mathematical models including mixed integer programming for cost optimization and continuous time Markov chains for risk evolution, revealing the underlying mechanisms of supply chain resilience. Empirical analyses utilizing operational datasets indicate that algorithmic synergy and standardized data protocols substantially improve order processing efficiency while reducing comprehensive logistics costs. However, considering the potential sample biases inherent in regional data, these sustainability outcomes might also reflect variations in external policy support to some extent. This leads us to further thinking regarding the scalability of such models across divergent economic contexts. Ultimately, the successful technology transfer of these lightweight systems to numerous enterprises demonstrates profound practical significance. Further research is needed to explore the multidimensional variables influencing longitudinal technological assimilation in sustainable supply chain management.
References
[1] Liu, W. (2025). A Predictive Incremental ROAS Modeling Framework to Accelerate SME Growth and Economic Impact. Journal of Economic Theory and Business Management, 2(6), 25–30.
[2] Shcherbakov, V., & Silkina, G. (2021). Supply chain management open innovation: Virtual integration in the network logistics system. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 54.
[3] Wang, M., Childerhouse, P., & Abareshi, A. (2024). Global logistics and supply chain integration in the digital era: a focus on China's Belt and Road Initiative. Journal of international logistics and trade, 22(2), 58-79.
[4] Seo, Y. J., Dinwoodie, J., & Kwak, D. W. (2014). The impact of innovativeness on supply chain performance: is supply chain integration a missing link?. Supply Chain Management: An International Journal, 19(5-6), 733-746.
[5] Liu, W. (2025). Few-Shot and Domain Adaptation Modeling for Evaluating Growth Strategies in Long-Tail Small and Medium-sized Enterprises. Journal of Industrial Engineering and Applied Science, 3(6), 30–35.
[6] Jun, S., Lee, C., Youn, S. J., & Lee, C. (2025). Technological Convergence and Innovation Pathways in Sustainable Logistics Systems: An Integrated Graph Neural Network and Main Path Analysis. Sustainability, 17(23), 10507.
[7] Liu, W. (2025). Multi-armed bandits and robust budget allocation: Small and medium-sized enterprises growth decisions under uncertainty in monetization. European Journal of AI, Computing & Informatics, 1(4), 89–97.
[8] Luo, M., Zhang, W., Song, T., Li, K., Zhu, H., Du, B., & Wen, H. (2021, January). Rebalancing expanding EV sharing systems with deep reinforcement learning. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence (pp. 1338-1344).
[9] Katsela, K., & Pålsson, H. (2021). Viable business models for city logistics: Exploring the cost structure and the economy of scale in a Swedish initiative. Research in Transportation Economics, 90, 100857.
[10] Qi, Z. (2025). Design and Practice of Elastic Scaling Mechanism for Medical Cloud-Edge Collaborative Architecture. Journal of Innovations in Medical Research, 4(5), 13-18.
[11] Luo, M., Du, B., Zhang, W., Song, T., Li, K., Zhu, H., ... & Wen, H. (2023). Fleet rebalancing for expanding shared e-mobility systems: A multi-agent deep reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems, 24(4), 3868-3881.
[12] Wu, Y. (2026). A Study on the Impact of Cross-Departmental Data Collaboration on Marketing Campaign Efficiency in Fast-Moving Consumer Goods E-commerce: The Case of PepsiCo (China)’s 7UP and Mirinda Project. Frontiers in Management Science, 5(1), 7-12.
[13] Zhu, H., Luo, Y., Liu, Q., Fan, H., Song, T., Yu, C. W., & Du, B. (2019). Multistep flow prediction on car-sharing systems: A multi-graph convolutional neural network with attention mechanism. International Journal of Software Engineering and Knowledge Engineering, 29(11n12), 1727-1740.
[14] Wang, C. (2025). Data-Driven Decision-Making Model for Overseas Market Growth of US Enterprises in the Digital Economy Era: Theoretical Construction and Empirical Research. Journal of World Economy, 4(6), 58-65.
[15] Alkaabi, K. (2024). Exploring logistics service providers for small and medium-sized enterprises and large corporations: benefits and challenges. European Business Review, 36(3), 293-310.
[16] Yosephine, V. S., Batara, M., & Setiawati, M. (2025). Scalable and Affordable IoT-based Inventory Control with Real-Time Monitoring for Small and Medium Enterprises. Journal of Industrial Engineering: Research & Application/Jurnal Teknik Industri, 27(1).
[17] Yu, C., Wang, H., Chen, J., Wang, Z., Deng, B., Hao, Z., ... & Song, Y. (2026). When Rules Fall Short: Agent-Driven Discovery of Emerging Content Issues in Short Video Platforms. arXiv preprint arXiv:2601.11634.
[18] Lin, A. (2025). Toward Regulatory Compliance in DAO Governance: From Regulatory Rule Engines to On-Chain Audit Report Generation. Journal of World Economy, 4(6), 12-20.
[19] Wu, Y. (2025). Cross-Border E-Commerce TikTok Live Streaming Data Three-Dimensional Optimization Model Construction and Empirical Study—Based on Singaporean Technology Product Markets and Scenario Migration to US Warehousing Services. Journal of World Economy, 4(6), 44-50.
[20] Jin, Y., Li, Z., Zhang, C., Cao, T., Gao, Y., Jayarao, P., ... & Yin, B. (2024). Shopping mmlu: A massive multi-task online shopping benchmark for large language models. Advances in Neural Information Processing Systems, 37, 18062-18089.
[21] Wang, H., Li, Q., & Liu, Y. (2022). Regularized Buckley–James method for right‐censored outcomes with block‐missing multimodal covariates. Stat, 11(1), e515.
[22] Liu, Z., Jin, C., Li, S., Li, W., & Wang, J. (2024). Improvement for modeling the damping of the wake oscillator based on the Van der Pol scheme. Physics of Fluids, 36(7).
[23] Jain, R., Palaniappan, D., Bekuma, Y., & Parmar, K. (2024). Cloud-Based MIS Model for Medium and Small Enterprises in Ethiopia. International Research Journal of Multidisciplinary Scope, 5(01), 766-780.
[24] Wu, Y. (2026). Research on Dynamic Prediction Model of Brand Marketing Content ROI Based on Machine Learning. International Journal of Advance in Applied Science Research, 5(2), 31-38.
[25] Wang, H., Li, Q., & Liu, Y. (2023). Adaptive supervised learning on data streams in reproducing kernel Hilbert spaces with data sparsity constraint. Stat, 12(1), e514.
[26] Wang, J., Kudagama, B. J., Perera, U. S., Li, S., & Zhang, X. (2025). Framework for generating high-resolution Hong Kong local climate projections to support building energy simulations. Physics of Fluids, 37(3).
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