Thermal Agent Forecasting for Cold Chain Logistics

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Solution Overview

Problem

Current cold chain logistics rely heavily on manual decision-making and non-standard tools/static rules for temperature control, leading to inconsistent and inaccurate decisions, resulting in higher operational costs, damaged goods, and customer dissatisfaction.

Innovation Solution

A method, system, and computer program product for centrally estimating and forecasting the optimal quantity of thermal agents required in cold chain systems, considering re-icing capabilities, legal constraints, and temperature indices, while auto-calibrating the estimation model using machine learning and feedback.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual decision-making and static rules are used for temperature control, then operational flexibility is maintained, but decision accuracy and consistency deteriorate

Engineering Contradiction:

Improveoperational flexibilityVSAvoiddecision accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent replaces manual decision-making and static rule-based systems with an automated machine learning model that dynamically predicts thermal agent requirements. The system substitutes human judgment and fixed algorithms with an intelligent algorithm that processes multiple data sources (weather forecasts, route information, package characteristics) to generate optimized thermal agent applications, thereby improving decision accuracy while maintaining operational flexibility through automated recommendations.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If excess quantity of dry ice is carried in containers, then the risk of impacting goods is reduced, but operational cost increases

Engineering Contradiction:

Improverisk mitigationVSAvoidoperational cost

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The patent dynamically adjusts the quantity of thermal agents applied based on predicted temperature profiles, route conditions, and package characteristics. Instead of using fixed excess quantities, the system calculates optimized amounts by analyzing multiple variables including weather forecasts, route temperature indices, and thermal properties of goods, thereby reducing unnecessary thermal agent consumption while maintaining adequate protection against temperature excursions.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system incorporates feedback mechanisms where actual temperature data and thermal agent consumption from completed shipments are used to refine and recalibrate the machine learning model. This continuous learning process improves prediction accuracy over time, enabling more precise thermal agent quantity decisions that balance risk mitigation with cost reduction by learning from historical performance data.

Inventive Principle:

Principle #23Feedback

3Loss of energy

If re-icing is done on a need-basis, then operational cost is reduced, but the risk of damaged goods increases

Engineering Contradiction:

Improveoperational costVSAvoidgoods protection

Core Design Contradiction:

Loss of energyVSReliability

Solution Approach 1:

The patent applies thermal agents in advance based on predicted temperature requirements for each route segment and shipment characteristics. The system calculates optimal thermal agent quantities before shipment departure, considering forecasted weather conditions, route temperature profiles, and package thermal properties, thereby ensuring adequate temperature protection without requiring reactive re-icing operations.

Inventive Principle:

Principle #10Preliminary action

4Ease of manufacture

If non-standard tools and static rules are used across facilities, then implementation simplicity is maintained, but decision consistency deteriorates

Engineering Contradiction:

Improveimplementation simplicityVSAvoiddecision consistency

Core Design Contradiction:

Ease of manufactureVSStability of the object's composition

Solution Approach 1:

The patent implements a universal machine learning-based decision support system that can be deployed across multiple facilities and locations. The standardized algorithm processes various input data types (weather, route, package information) and generates consistent thermal agent recommendations regardless of location, thereby improving decision consistency while maintaining implementation simplicity through a single unified platform that replaces multiple facility-specific static rules.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250165904A1Cold chain temperature control optimization

Publication Date: 2025.05.22 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A processor may receive a shipment request associated with a shipment. The processor may analyze the shipment request. The processor may generate one or more estimated metrics associated with the shipment based on the analyzing. The processor may apply an amount of thermal agent for the shipment to reach a final destination based on the one or more estimated metrics.