Autonomous Logistics and Real-Time Analytics in Disruption Mitigation: Evidence from Cross-Sectoral Secondary Datasets in Post-Pandemic Supply Networks

Abstract The post-pandemic supply-chain literature increasingly links autonomous logistics systems (ALS) and real-time analytics (RTA) with resilience, yet the evidence is fragmented across technologies and sectors and often interpreted causally from descriptive associations. This study revisits cross-sectoral secondary evidence for retail, pharmaceuticals, automotive, and food/agriculture over 2019–2024 and asks whether resilience is more strongly associated with isolated technologies or with an integrated digital-autonomy capability. The analytical unit is the sector-year, giving a maximum balanced panel of N = 24 observations (four sectors x six years). In addition to descriptive trend analysis, the revision reports Pearson correlations with 95% confidence intervals and false-discovery-rate adjusted p-values, a standardized multivariate OLS model reconstructed from the reported correlation matrix, multicollinearity diagnostics, and principal-component analysis (PCA). The four technology indicators are highly intercorrelated; their first principal component explains 79.3% of technology variance and is strongly associated with shorter recovery time (r = -0.835, p < 0.001) and lower disruption magnitude (r = -0.624, p = 0.001). In the multivariate recovery model, the four technologies jointly explain 70.7% of variance (F(4,19) = 11.46, p < 0.001), but no individual standardized coefficient remains statistically significant, consistent with shared variance and technological complementarity rather than a single dominant technology effect. The study therefore advances an Autonomous Resilience Architecture (ARA) in which sensing, interpreting, executing, and learning capabilities operate as a coordinated bundle. The evidence is explicitly associational, and ARA is presented as a theoretically grounded framework requiring future causal and formal validation.