AUTOMATED WAREHOUSE SYSTEMS AND THEIR ECONOMIC EFFICIENCY

Abstrak:

The rapid digitalization of global supply chains has accelerated the adoption of automated warehouse systems (AWS) across diverse industrial sectors. This paper presents a systematic review and empirical analysis of robotic and automated warehouse technologies, examining their technical capabilities, implementation costs, and measurable economic outcomes. Drawing on data from 14 peer-reviewed studies, 6 major industry case studies, and primary market research spanning 2018–2024, we demonstrate that AWS adoption yields an average labor cost reduction of 38.6%, an order accuracy improvement of 3.4 percentage points, and a space utilization gain of 35–45% compared to manual operations [1]. Our findings indicate that the global AS/RS (Automated Storage and Retrieval Systems) market reached USD 27.1 billion in 2023 and is projected to surpass USD 52.4 billion by 2028 at a compound annual growth rate (CAGR) of 14.1% [2]. The payback period for a well-implemented AWS ranges from 2.5 to 6.5 years depending on technology class and operational volume [3]. We further identify key enabling factors—including artificial intelligence-driven warehouse management systems (WMS), collaborative robots (cobots), and goods-to-person (GtP) fulfillment architectures—as primary economic drivers. Policy implications for developing economies, including Uzbekistan and Central Asian markets, are discussed in the context of Industry 4.0 readiness and workforce transition.