INTEGRATING AI-POWERED ROBOTICS IN LARGE-SCALE WAREHOUSE MANAGEMENT: ENHANCING OPERATIONAL EFFICIENCY, COST REDUCTION, AND SUPPLY CHAIN PERFORMANCE MODELS

Abstract

This study addresses a practical problem in large-scale fulfillment: organizations invest in AI-powered robotics yet often fail to see reliable gains in efficiency, cost, and service because orchestration across cloud and enterprise systems is uneven. The purpose is to quantify how adoption of AI-robotic warehousing relates to operational efficiency, cost per order, and supply chain performance, and to test whether systems integration and workforce capability condition those effects. We use a quantitative, cross-sectional, case-based design across 24 cloud-enabled enterprise warehouse cases with 268 respondents and harmonized KPIs. Key variables include AI-robotics adoption, systems integration, workforce capability, operational efficiency lines per labor hour, order cycle time, picking accuracy, cost per order, and on-time-in-full. The analysis plan combines descriptive statistics and correlations with hierarchical OLS, moderation via centered interactions Adoption × Integration and Adoption × Workforce, mediation through operational efficiency using bootstrap confidence intervals, and robustness checks with KPI-level models, site-clustered errors, and leave-one-site-out tests. Headline findings show adoption is positively associated with efficiency β = 0.28, p < .001 and the effect strengthens with higher integration βinteraction = 0.12, p = .003; efficiency is linked to lower log cost per order β = −0.31, p < .001 and higher supply chain performance β = 0.27, p < .001; indirect effects from adoption to cost and service through efficiency are significant and grow with integration, with a practical integration threshold around 3.4 on a five-point Likert scale. Implications are to treat robotics as a data-centric control system, prioritize governed APIs and low-latency event flows, instrument KPI lineage, and invest in role-based training to translate algorithmic potential into sustained cadence and quality.