Thermal in Cold Chain Logistics: Modeling Time-Above-Threshold Risk under Sensor Uncertainty, Door-Open Events, and Packaging Variability

Abstract

This article presents an engineering-oriented framework that models cold chain thermal control as an end-to-end decision system and quantifies how uncertainty propagates from sensing and environment through exposure modeling and escalation logic into distributional outcomes that matter in operations, including probability of temperature threshold exceedance, expected time-above-threshold per shipment, probability of quality excursion beyond allowable exposure, and the effectiveness of mitigation actions such as pre-cooling, lane-specific packaging selection, and staged escalation during dwell. A scenario-based quantitative study is developed for generic refrigerated transport and cross-dock handling of high-risk perishables and temperature-sensitive products, comparing four operational architectures: baseline compliance logging, increased sensor density without governance, model-based exposure forecasting with limited drift handling, and a governance-optimized two-tier approach that constrains nuisance alarms while improving time-to-decision through drift-aware plausibility checks, door-event segmentation, and staged interventions. Results indicate that tail exposure behavior is dominated by door-open frequency and decision latency rather than by mean trailer temperature, that adding sensors without governance can increase workload and reduce response discipline, and that the two-tier governed approach reduces both exceedance probability and nuisance alarms while improving intervention timeliness, especially under high-variability loading conditions. Up to three copy-ready tables and full prompts for data-driven figures are provided for Techne submission.