<![CDATA[This article presents a reliability-centered framework for cold chain engineering that integrates stochastic excursion modeling, sensor uncertainty characterization, and product degradation kinetics into a unified decision pipeline that supports shipment-level risk scoring, lane qualification, packaging selection, and escalation rules for intervention. A scenario-based quantitative study is developed using representative distributions of ambient conditions, dwell times at transfer nodes, refrigeration performance, and door-open events, while incorporating realistic sensor noise and bias drift to evaluate how monitoring quality influences the probability of detecting excursions and the confidence of acceptance decisions. Comparative analysis is performed across four operational strategies that span passive packaging, active temperature control, hybrid risk-based controls, and governance-optimized monitoring with intervention triggers. Results demonstrate that (i) time-to-excursion is dominated by interface dwell and door-open variability rather than by steady-state transport, (ii) small sensor bias and sampling-rate limitations can materially distort excursion severity and time-above-threshold estimates, leading to both false compliance and unnecessary rejection, and (iii) a governed two-tier decision architecture, where monitoring triggers verification or operational intervention rather than serving as the sole acceptance authority, yields the best cost–risk balance for typical pharmaceutical and perishable profiles. The study provides implementable tables, figure prompts, and decision rules suitable for applied engineering practice and journal submission.]]>
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