Authors : Dr. G. Balamurugan; Arunthathi K

Volume/Issue : Volume 10 - 2025, Issue 6 - June

Google Scholar : https://tinyurl.com/3fru8vsd

DOI : https://doi.org/10.38124/ijisrt/25jun1129

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The rapid development of e-commerce has contributed to the demand for effective and reliable logistics management. To address this, predictive analytics and AI chatbot have emerged as breakthrough technologies in the logistics industry. Predictive analytics leverages data-driven insights and machine learning algorithms to forecast patterns of demand, balance inventory, and improve supply chain efficiency, thereby reducing operational inefficiencies and cutting costs. Simultaneously, AI-powered chatbot are leading the charge in bringing about enhanced customer engagement in the form of prompt responses to log-related inquiries, order tracking, and resolving repetitive issues in real time. These two technologies working together not only enhance logistical accuracy and responsiveness but also enable building a customer- centric approach that engenders more satisfaction and loyalty. This article attempts to analyze the multifaceted impact of predictive analytics and AI chatbot on e-commerce logistics based on a combination of empirical data, case studies, and industry reports. Further, the study highlights the challenges of deploying predictive analytics and chatbot, including data integration and privacy concerns, and offers business strategic suggestions to businesses in order to leverage these tools effectively. Through this comprehensive analysis, the article provides valuable insights into how the synergy between predictive analytics and AI-powered chatbot can drive innovation and efficiency in e-commerce logistics.

Keywords : Customer Satisfaction, Dispatch Time, Delivery Failures, Chatbot Accuracy, Order Fulfillment.

References :

1. Acharya, S. (2023). Study of the effectiveness of chatbot in customer service on e-commerce websites.
2. Escudero-Santana, A., Munzuri, J., Lorenzo-Espejo, A., & Munoz-Diaz, M. L. (n.d.). Improving e-commerce distribution through last-mile logistics with multiple possibilities of deliveries based on time and location.
3. Gupta, P., Singh, S., Ranjan, R., & Kharayat, G. (2019). Analysis of delivery issues that customers face upon e-commerce shopping. Journal of Emerging Technologies and Innovative Research, 6(Special Issue 3).
4. Kuo, Y., & Hsieh, C.-H. (n.d.). Effects of service recovery after service failure in online shopping logistics.
5. Lysenko, S., Makovoz, O., & Perederii t. (n.d.). The impact of AI in logistics management on sustainability development of e-business.
6. Rane, N. L., Choudhary, S. P., & Rane, J. (n.d.). Artificial intelligence (AI), internet of things (IOT), and blockchain-powered chatbot for improved customer satisfaction, experience, and loyalty.
7. Shah, W., & Badi, S. (2021, December). AI and big data integration for intelligent supply chain optimization: Boosting efficiency in e-commerce operations.
8. Song, L., Cherrett, T., McLeod, F., & Guan, W. (n.d.). Addressing the last mile problem: The transport impacts of collection/delivery points.
9. Upreti, K., Gangwar, D., Vats, P., Bhardwaj, R., Khatri, V., & Gautam, V. (n.d.). Artificial neural networks for enhancing e-commerce: A study on improving personalization, recommendation, and customer experience.
10. Zainal, F., Baharudin, H., Khalid, A., Karim, N.H., Ramli, S., Batan, A., & Mustapha, L. (2019). Applying artificial intelligence in e-commerce reverse logistics: Enhancing returns management, supply chain efficicency, and sustainability through advanced technologies.