Beyond Thresholds: A Quality-Aware Decision Intelligence Framework for Cold Chain IoT Systems

• URL: http://arxiv.org/abs/2608.15082v1
• Date: Sat, 15 Aug 2026 06:59:08 GMT
• Title: Beyond Thresholds: A Quality-Aware Decision Intelligence Framework for Cold Chain IoT Systems
• Abstract summary: Cold chain logistics has advanced technologically, yet most deployed systems remain reactive monitors, not decision-making agents.<n>We address this gap with a Quality-Aware Decision Intelligence framework combining three capabilities.<n>We benchmark against five baselines -- threshold monitoring, physics-only, physicsplusnoise, optimisation-based decisions, and a rule-based expert system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cold chain logistics has advanced technologically, yet most deployed systems remain reactive monitors, not decision-making agents: thresholds trigger alerts, but nothing relates violations to cumulative product degradation or converts degradation signals into logistics decisions. We address this gap with a Quality-Aware Decision Intelligence (QADI) framework combining three capabilities: a structured quality state representation, $S_q = [L, Q, U, R]$ -- remaining shelf life, degradation rate, estimation uncertainty, and operational risk, all derived and computable from the framework equations; a hybrid quality modeling layer combining physics-based microbial kinetics with a data-driven correction term; and a reasoning layer built on Microsoft Phi-4~\cite{Phi4} with retrieval-augmented generation over a structured domain knowledge base. We benchmark against five baselines -- threshold monitoring, physics-only, physics-plus-noise, optimisation-based decisions, and a rule-based expert system -- across eight cold chain scenarios, using pasteurised milk as the primary case, with ground truth shelf-life drawn from published dairy studies~\cite{Singh1994, Smigic2015} independent of our model. Comparisons use Wilcoxon signed-rank tests with Holm correction. Across milk and broccoli scenarios, the framework attains mean absolute shelf-life error of 7.2 hours (versus 30.9 hours, physics-only; $p<0.001$), spoilage rate of 14.5% (versus 16.6%, physics-only and rule-based; p=0.08), and oracle-optimal decisions in 99.5% of scenarios. Removing the LLM reasoning component drops optimality to 45.5% ($p<0.001$). Expert-rated explanation quality reaches 83% ($κ= 0.71$). Ablations show hybrid modeling and LLM reasoning contribute distinct gains, while RAG retrieval mainly drives explanation quality. Code: https://bit.ly/4d6t44C.

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