Technology Integration in Non-Robotic Warehouses: Implementing LiDAR and IoT for Efficiency Gains

Abstract

The rapid evolution of global supply chains and increasing consumer demands have intensified the need for more efficient, responsive, and accurate warehousing operations. While fully automated warehouses, powered by robotics and artificial intelligence, have demonstrated the potential to revolutionize logistics, the high implementation costs, complexity, and infrastructural overhaul required pose significant barriers for many organizations—particularly small and medium-sized enterprises (SMEs). In response, this study explores the integration of two emerging yet accessible technologies—Light Detection and Ranging (LiDAR) and the Internet of Things (IoT)—into traditional non-robotic warehouse environments as a strategic alternative to full automation.

This research adopts a mixed-methods approach, combining empirical data from ten industry case studies, semi-structured interviews with five warehouse managers, and a comparative analysis of pre- and post-integration performance metrics. The study evaluates four key performance indicators (KPIs): inventory accuracy, order picking speed, labor cost efficiency, and safety incident frequency. Results reveal that the integration of LiDAR and IoT leads to substantial operational improvements, including an 11% increase in inventory accuracy, a 37.5% increase in picking speed, a 22% reduction in labor costs, and a 57% decrease in safety-related incidents.

The paper presents a modular implementation framework for integrating LiDAR and IoT technologies in phases, enabling warehouses to optimize space utilization, enhance real-time asset visibility, support predictive analytics, and improve workplace safety—all without replacing the human workforce. The synergistic use of spatial data mapping (via LiDAR) and interconnected sensing and communication systems (via IoT) provides a cost-effective pathway for achieving automation-level efficiency in traditional facilities.

By addressing a critical gap in current logistics and operations management literature, this study contributes a practical, scalable model for technology-enabled warehouse modernization. The findings are particularly relevant for decision-makers seeking to enhance efficiency, maintain operational flexibility, and extend the lifespan of existing warehouse infrastructure, while avoiding the financial and operational risks associated with full robotic automation.