ColdRescue
ColdRescue helps cold-chain dispatchers coordinate simultaneous refrigeration failures by prioritizing urgent shipments and optimizing limited receiver capacity. It combines thermal-risk estimation, physics-bounded safety checks, and human-approved recovery decisions to help reduce avoidable cargo loss.
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ColdRescue: Smarter Recovery Decisions for Cold-Chain Emergencies
Every minute matters when refrigerated cargo loses cooling. A failed truck refrigeration unit, a cold-room power outage, or a warehouse grid failure can put perishable goods at risk. When multiple incidents occur simultaneously, dispatchers face a difficult challenge: deciding which shipments need attention first and allocating limited cold-storage capacity before recovery windows expire.
ColdRescue is an AWS-backed emergency cold-chain recovery and decision-support prototype designed to help dispatchers make coordinated, explainable decisions during these situations.
The scale of the problem
Food loss is a significant challenge across India's agricultural supply chain. A study commissioned by India's Ministry of Food Processing Industries and conducted by NABARD Consultancy Services (NABCONS) estimated annual post-harvest losses of 7.36 million tonnes of fruits and 11.97 million tonnes of vegetables. Together, that is approximately 19.33 million tonnes per year, averaging around 53,000 tonnes per day.
The financial impact is also substantial. The study estimated the annual monetary value of post-harvest losses at approximately ₹29,545 crore for fruits and ₹27,459 crore for vegetables—a combined estimated value of ₹57,004 crore.
These figures are study estimates based on data for 2020–22, not live 2026 measurements. They cover losses across multiple stages of the agricultural supply chain, including harvesting, handling, transportation and storage. They must not be interpreted as losses caused solely by refrigeration failures.
ColdRescue focuses on one specific part of this broader challenge: helping operators respond when refrigeration or power failures threaten temperature-sensitive cargo and several shipments compete for limited recovery capacity.
The problem
Cold-chain emergencies rarely happen at convenient times. A night-shift dispatcher may need to coordinate several failing shipments while receiver facilities have limited storage capacity and different recovery constraints. Handling each incident independently can overlook conflicts between shipments competing for the same receiver.
When decisions are delayed or recovery destinations are poorly allocated, cargo may remain exposed to unsuitable temperatures for longer than necessary. Depending on the cargo, its condition and the applicable recovery policy, this can increase the risk of spoilage, disposal and financial loss.
ColdRescue approaches the problem as a coordinated allocation challenge rather than a series of isolated dispatch decisions.
How ColdRescue works
The system evaluates incidents by urgency and estimated time-to-spoilage risk. It then compares cargo-to-receiver assignments across competing incidents, accounting for receiver capacity, estimated travel times and configured recovery constraints.
Its workflow combines four key capabilities:
- Thermal-risk estimation: Uses temperature-related information and a physics-based model, with machine-learning inference where configured.
- Global allocation: Evaluates competing incidents together rather than assigning each shipment independently.
- Deterministic safety checks: Checks proposed recovery plans against configured, physics-bounded recovery limits.
- Human approval: Requires an operator to review and approve a recommendation before dispatch.
The objective is not simply to divert the greatest possible quantity of cargo. It is to help dispatchers make coordinated recovery decisions while respecting the constraints that matter to each incident.
Building with AWS
AWS provides cloud infrastructure for the prototype's deployed and configured workflows. Infrastructure as code supports reproducible resource management, while cloud services can connect telemetry ingestion, processing, persistence, authentication and recovery orchestration.
Depending on the integrations enabled in the current deployment, the architecture includes services such as AWS Amplify Hosting for the operator console, Amazon API Gateway and Amazon Cognito for the authenticated API workflow, AWS Lambda and AWS Step Functions for processing and orchestration, Amazon DynamoDB for application data, Amazon SageMaker for thermal-risk inference, and Amazon CloudWatch for operational logging and monitoring.
The thermal-risk workflow includes a deterministic fallback when model inference is unavailable. This helps keep safety decisions grounded in explicit policies and physics-based constraints rather than depending solely on a machine-learning prediction.
What the benchmark tells us
In a benchmark of 300 simulated scenarios, global allocation diverted a mean 1.61% less raw cargo than the greedy baseline. In a separate, constructed, capacity-constrained suite of 50 scenarios using Seed 77, it diverted 8.34% more cargo overall, with the difference arising from one scenario.
These results show that allocation strategy can affect outcomes when incidents compete for limited receiver capacity. They do not establish that global allocation consistently outperforms the baseline. They also do not, by themselves, demonstrate how many tonnes of food or how much money ColdRescue would save in real-world operations.
Further benchmarking should measure recovery-window violations, cargo successfully recovered, capacity conflicts and estimated financial outcomes across multiple seeds and scenarios. These measurements would help determine whether the allocation approach improves both recovery outcomes and safety-policy compliance.
Potential impact: food saved and financial losses avoided
ColdRescue is intended for refrigerated transport operators, food distributors, cold-storage facilities and potential recovery partners such as food banks. By helping dispatchers prioritize incidents and coordinate scarce receiver capacity, the project aims to reduce avoidable cargo loss and improve emergency response.
Its potential impact can be measured in three practical ways:
- Cargo preserved: The additional kilograms or tonnes successfully recovered compared with the existing dispatch approach.
- Financial losses avoided: The estimated value of cargo recovered that would otherwise have been lost, calculated using documented cargo quantities and appropriate product values.
- Recovery performance: Changes in response times, configured recovery-window violations, unused receiver capacity and the number of shipments successfully reassigned.
India's estimated ₹57,004 crore in annual fruit and vegetable post-harvest losses illustrates the wider economic importance of reducing food loss. However, it is not the amount ColdRescue can save. Only a portion of national losses relates to the refrigeration and power emergencies this prototype addresses, and only a fraction of those losses may be preventable through better allocation.
The current prototype uses synthetic telemetry and simulated scenarios. A real-world pilot has not yet been conducted, and the quantity of cargo and monetary value ColdRescue could save have not yet been established through validated field data.
Illustrative estimate based on stated simulation assumptions, not measured real-world savings.
What's next
Future work includes validating the workflow against real operational data, measuring safety-window violations across allocation strategies, improving resilience to telemetry and service failures, and evaluating performance and cost at larger fleet sizes.
A pilot with a refrigerated transport operator or food distributor could help validate the system using real incident records, cargo quantities, temperature histories, recovery travel times and receiver capacities. Comparing ColdRescue recommendations with existing dispatch decisions would provide a stronger basis for estimating actual cargo preservation and financial benefits.
ColdRescue demonstrates how cloud infrastructure, optimization, thermal-risk modeling and human oversight can be brought together to address a practical logistics challenge. Its ultimate impact will depend on measurable operational evidence and validation in real cold-chain environments.
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