A Study on the Application of Machine Learning Optimization Models in Last-Mile Delivery among SME Logistics Companies in Malaysia

The rapid growth of e-commerce and increasing customer expectations for fast and reliable delivery have intensified the complexity of last-mile logistics, particularly among small and medium-sized enterprise (SME) logistics companies in Malaysia. Despite their critical role in the supply chain, many SMEs face challenges in optimizing delivery routes, reducing operational costs, and maintaining service quality due to limited technological capabilities and resource constraints. In this context, machine learning (ML) optimization models have emerged as a promising solution to enhance decision-making and operational efficiency in last-mile delivery.
This study aims to investigate the application of machine learning-based optimization models in improving last-mile delivery performance among SME logistics companies in Malaysia, with a focus on identifying key factors influencing adoption and evaluating their impact on delivery efficiency, cost reduction, and service reliability. Drawing upon relevant theoretical perspectives, the study proposes a conceptual framework that integrates technological, organizational, and environmental factors in explaining ML adoption and its effectiveness. A quantitative research approach will be employed, involving data collection from SME logistics providers through structured questionnaires, and the data will be analyzed using appropriate statistical techniques to examine the relationships between ML adoption and performance outcomes.
The expected findings will provide empirical insights into how ML-driven optimization can support SMEs in overcoming logistical challenges and achieving competitive advantage. This study contributes to both theory and practice by advancing the understanding of advanced analytics adoption in SME logistics and offering practical recommendations for industry stakeholders and policymakers to support digital transformation in last-mile delivery operations in Malaysia.

Machine Learning Optimization, Last-Mile Delivery, SME Logistics, Technology Adoption, Logistics Performance

1. D. Bertsimas and N. Kallus, “From predictive to prescriptive analytics,” Management Science, vol. 66, no. 3, pp. 1025–1044, 2020. [Google Scholar] [Crossref]

2. N. Boysen, S. Fedtke, and S. Schwerdfeger, “Last-mile delivery concepts: A survey,” European Journal of Operational Research, vol. 293, no. 2, pp. 343–359, 2021. [Google Scholar] [Crossref]

3. R. Gevaers, E. Van de Voorde, and T. Vanelslander, “Characteristics and typology of last-mile logistics,” Procedia - Social and Behavioral Sciences, vol. 125, pp. 57–71, 2011. [Google Scholar] [Crossref]

4. M. Ghobakhloo, “Industry 4.0, digitization, and opportunities for SMEs,” Journal of Manufacturing Technology Management, vol. 31, no. 5, pp. 1039–1064, 2020. [Google Scholar] [Crossref]

5. G. Laporte, “Fifty years of vehicle routing,” Transportation Science, vol. 43, no. 4, pp. 408–416, 2009. [Google Scholar] [Crossref]

6. S. F. W. T. Lim, X. Jin, and J. S. Srai, “Consumer-driven e-commerce: A literature review,” International Journal of Physical Distribution & Logistics Management, vol. 48, no. 3, pp. 308–332, 2018. [Google Scholar] [Crossref]

7. C. Liu, Y. Zhang, and X. Zhang, “Machine learning in logistics optimization,” Transportation Research Part E, vol. 145, p. 102162, 2021. [Google Scholar] [Crossref]

8. R. Mangiaracina, A. Perego, A. Seghezzi, and A. Tumino, “Innovative solutions for last-mile delivery,” International Journal of Physical Distribution & Logistics Management, vol. 49, no. 9, pp. 901–920, 2019. [Google Scholar] [Crossref]

9. H. Min, “Artificial intelligence in supply chain management,” International Journal of Logistics Research and Applications, vol. 22, no. 5, pp. 1–22, 2019. [Google Scholar] [Crossref]

10. S. Mittal, M. A. Khan, D. Romero, and T. Wuest, “A critical review of smart manufacturing for SMEs,” Journal of Manufacturing Systems, vol. 49, pp. 194–214, 2018. [Google Scholar] [Crossref]

11. T. Oliveira and M. F. Martins, “Literature review of information technology adoption models,” Information Systems Frontiers, vol. 13, no. 1, pp. 1–16, 2011. [Google Scholar] [Crossref]

12. L. Ranieri, S. Digiesi, B. Silvestri, and M. Roccotelli, “A review of last-mile logistics innovations,” Sustainability, vol. 10, no. 11, pp. 1–19, 2018. [Google Scholar] [Crossref]

13. SME Corp Malaysia, SME Annual Report 2023, 2023. [Google Scholar] [Crossref]

14. E. Taniguchi, R. G. Thompson, and T. Yamada, City Logistics: Network Modelling and Intelligent Transport Systems. Elsevier, 2020. [Google Scholar] [Crossref]

15. L. G. Tornatzky and M. Fleischer, The Processes of Technological Innovation. Lexington Books, 1990. [Google Scholar] [Crossref]

16. P. Toth and D. Vigo, Vehicle Routing: Problems, Methods, and Applications. SIAM, 2014. [Google Scholar] [Crossref]

17. S. F. Wamba, A. Gunasekaran, S. Akter, S. J. Ren, R. Dubey, and S. J. Childe, “Big data analytics and firm performance,” Journal of Business Research, vol. 70, pp. 356–365, 2020. [Google Scholar] [Crossref]

18. G. Wang, A. Gunasekaran, E. W. T. Ngai, and T. Papadopoulos, “Big data analytics in logistics and supply chain management,” International Journal of Production Economics, vol. 176, pp. 98–110, 2020. [Google Scholar] [Crossref]