AI-Driven Quantum-Inspired Predictive Analytics Framework for Procurement Optimization in Critical Infrastructure Networks

Abstract

This study addresses the growing difficulty of optimizing procurement in critical infrastructure networks where uncertain demand, supplier disruption, fragmented data, price volatility, long lead times, and complex sourcing constraints can threaten operational continuity and infrastructure resilience. The purpose of the research was to develop and quantitatively evaluate an AI-driven quantum-inspired predictive analytics framework that integrates predictive intelligence, advanced optimization, real-time procurement information, and supplier-risk analysis to improve procurement decision-making. A quantitative, cross-sectional, case-study-based design was employed using enterprise-level critical infrastructure cases across energy and power, transportation, telecommunications, water utilities, oil and gas, and related infrastructure organizations. The final analytical sample comprised 286 procurement, sourcing, supply-chain, logistics, operations, engineering, risk, compliance, data analytics, and IT professionals, representing an 89.4% valid response rate. The key variables were AI-driven predictive analytics capability, quantum-inspired optimization capability, real-time data integration, predictive supplier and supply-chain risk analytics, and procurement optimization performance. Data were collected through a five-point Likert-scale questionnaire and analyzed using descriptive statistics, Cronbach's alpha, Pearson correlation, multiple regression, and multicollinearity diagnostics. Findings showed high levels of AI-driven predictive analytics (M = 4.12), real-time data integration (M = 4.05), predictive risk analytics (M = 4.09), and procurement optimization (M = 4.15), while quantum-inspired optimization remained moderately high (M = 3.86). Predictive risk analytics demonstrated the strongest correlation with procurement optimization (r = .71, p < .001), followed by AI-driven predictive analytics (r = .68), real-time data integration (r = .64), and quantum-inspired optimization (r = .59). The regression model explained 68.4% of procurement-optimization variance, with predictive risk analytics emerging as the strongest predictor (β = .31), followed by AI-driven predictive analytics (β = .27), real-time data integration (β = .23), and quantum-inspired optimization (β = .18). These results imply that critical infrastructure organizations should integrate predictive analytics, risk intelligence, connected procurement data, and advanced optimization within a unified decision-support architecture to strengthen sourcing efficiency, supplier reliability, responsiveness, continuity, and resilience.