The role of IoT in optimizing quick commerce operations: a comprehensive analysis of micro-fulfillment centers and consumer satisfaction
subject: Artificial intelligence; Computer in society; Computer science; Computing methodology; Consumer decisions; Database management system; Fast delivery; Geomatics; Information systems; Internet of Things; Machine learning; Micro-fulfillment centers; Operations management; Product assortment; Product demand; Product quality; Quick commerce
description: This research focuses on integrating Internet of Things (IoT) and AI-driven machine learning algorithms to optimize operations within micro-fulfillment centers for quick commerce in India. The study addresses the growing demand for faster delivery times, driven by the rise of e-commerce, and the challenges associated with managing inventory, logistics, and last-mile delivery. By implementing IoT-enabled devices to monitor real-time data and deploying machine learning algorithms for demand forecasting and route optimization, the study aimed to enhance operational efficiency and reduce delivery times. The research utilized a dataset comprising various operational metrics from micro-fulfillment centers across major Indian cities. A comparative analysis revealed that the AI and IoT integration led to a 22% reduction in delivery time and a 15% improvement in order accuracy. Additionally, the predictive maintenance system, powered by IoT sensors, resulted in a 30% decrease in equipment downtime. The results demonstrate that combining IoT with machine learning optimizes supply chain operation and significantly contributes to meeting consumer expectations in the quick commerce sector. This research provides a comprehensive framework for leveraging technology to address the unique challenges of the Indian market, offering valuable insights for industry stakeholders. 2026 Elsevier Inc. All rights reserved..
creator: Muloor, Kiran Hemanthaj; Shankar Iyer, Lakshmi; Saseekala, M.; Manu, K.S.
source: IoT Security: Fundamentals and Key Enabling Technologies;pp.473-487
publisher: Elsevier
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 978-044334125-0; 978-044334126-7;
language: English
type: Book chapter
coverage: Muloor K.H., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Shankar Iyer L., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Saseekala M., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Manu K.S., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India