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@article{205685,
        author = {Bharati Shankarrao Uikey and Prof. Abhijeet Gajbhiye},
        title = {Last-Mile Delivery Optimization in E-Commerce},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {7381-7385},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205685},
        abstract = {The rapid expansion of e-commerce in India has intensified the need for efficient last-mile delivery systems, as this final stage of logistics accounts for more than half of total shipping costs and directly shapes customer satisfaction. This research paper investigates how predictive analytics and dynamic routing algorithms can optimize last-mile delivery operations in the Indian e-commerce sector. Using a mixed-method approach, the study analyses historical delivery data from an anonymized logistics partner and conducts semi-structured interviews with delivery managers. The findings reveal that dynamic routing, incorporating real-time traffic and order density, reduces average delivery time by
22.6 percent and cost per delivery by 18.4 percent compared to static routing methods. Furthermore, machine learning-based estimated time of arrival models demonstrate superior accuracy over traditional rule-based approaches, cutting delivery window deviations by nearly one-third. However, the study also identifies significant implementation barriers, including address fragmentation, technology costs, and driver resistance to automated systems. The research concludes that while predictive optimization offers substantial operational benefits, successful adoption requires investments in data infrastructure, driver training, and customer communication systems. A strategic framework is proposed to help e-commerce firms of varying sizes transition from reactive to proactive last-mile management.},
        keywords = {Last-mile delivery, e-commerce logistics, dynamic routing, predictive analytics, customer satisfaction, machine learning, India.},
        month = {June},
        }