AUTOMATION AND EQUIPMENT OPTIMIZATION STRATEGIES ENHANCING PRODUCTIVITY IN LARGE SCALE CONSTRUCTION OPERATIONS

Abstract

Purpose – This study examines automation and equipment optimization strategies that enhance productivity in large-scale construction operations. It explores how robotics, artificial intelligence (AI), Internet of Things (IoT), and data-driven equipment management improve operational efficiency and project outcomes. Design/methodology/approach – A conceptual analytical approach is adopted, supported by empirical findings from prior studies. The study synthesizes research evidence on automation technologies and equipment optimization frameworks and integrates them into a structured productivity enhancement model. Findings – The analysis indicates that autonomous equipment, AI-based scheduling, IoT-enabled predictive maintenance, and robotics significantly improve productivity, reduce downtime, and enhance safety. Integrated optimization strategies increase equipment utilization rates and improve decision-making efficiency in large scale operations. Practical implications – Construction firms can improve cost efficiency, minimize delays, and enhance safety compliance by adopting structured automation frameworks and real-time equipment monitoring systems. Originality/value – This paper integrates automation technologies and equipment optimization strategies into a unified framework tailored specifically for large-scale construction productivity enhancement.