THE INFLUENCE OF QUICK COMMERCE ON WAREHOUSE MANAGEMENT SYSTEMS

Abstract

This study provides a comprehensive analysis of how quick commerce (q-commerce)- characterized by ultra-fast delivery models promising order fulfilment within 10–30 minutes-has fundamentally transformed warehouse management systems (WMS) in India’s rapidly evolving retail landscape. The research explores the drivers behind q- commerce’s explosive growth, including shifting consumer expectations for immediacy, the proliferation of hyperlocal dark stores, and the adoption of advanced technologies such as AI, IoT, and cloud-based WMS platforms. Through detailed case studies of leading Indian q-commerce players like Zepto, Blinkit, Swiggy Instamart, and Dunzo, the study identifies the operational and technological adaptations required to support ultra-fast fulfillment. Key findings highlight a shift from traditional batch-oriented, centralized warehouse models to distributed networks of micro-fulfillment centers optimized for speed, real-time inventory visibility, and seamless integration with last-mile delivery systems. The research reveals that modern WMS for q-commerce must incorporate features such as predictive analytics, dynamic route optimization, and mobile-first interfaces to maintain high productivity and accuracy under intense time pressures. The study also addresses significant challenges, including inventory fragmentation, integration complexity, and the trade-off between operational efficiency and delivery speed. It identifies critical gaps in current WMS capabilities-such as limited predictive inventory positioning, inadequate dynamic labor allocation, and insufficient cross-store inventory balancing-that hinder scalability and sustainability. Employing the Technology-Organization-Environment (TOE) framework, the research synthesizes insights from secondary data, industry reports, and case analyses to propose strategic recommendations for optimizing WMS in q-commerce contexts. These include adopting hybrid automation, leveraging hyperlocal partnerships, and implementing AI- driven demand forecasting. The study concludes that while q-commerce has redefined warehouse management through technology and process innovation, ongoing challenges in integration, scalability, and workforce management must be addressed to ensure long- term viability and profitability.