Perishable Goods Logistics Plan Using Thermal Data Modeling
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Solution Overview
Problem
Current methods for shipping perishable goods lack precision in maintaining temperature control, leading to potential spoilage and increased costs due to overestimation of coolant requirements, as human operators rely on estimates rather than data-driven logistics plans.
Innovation Solution
A system and method that collect thermal data from previous shipments, associate it with environmental data, build a model of expected thermal behavior, and determine an optimized logistics plan, including the amount of coolant needed, to ensure accurate temperature maintenance during transportation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human operators estimate coolant requirements based on experience, then the logistics plan can be determined quickly, but the temperature control precision deteriorates leading to potential spoilage
Solution Approach 1:
The system performs preliminary actions by collecting thermal data from previous shipments and building predictive models in advance. This allows the system to determine optimal coolant requirements before shipping begins, replacing human estimation with data-driven predictions that improve temperature control precision while maintaining quick decision-making.
Solution Approach 2:
The system implements feedback by continuously collecting thermal data from temperature sensors during shipments and using this data to refine predictive models. The feedback loop enables the system to learn from actual shipment performance and improve future coolant recommendations, resolving the contradiction between quick decision-making and precise temperature control.
2Reliability
If operators overestimate coolant requirements to minimize spoilage risk, then the reliability of temperature control improves, but the cost of shipping increases due to excess coolant and packaging
Solution Approach 1:
The system changes parameters by using actual thermal data from previous shipments to determine precise coolant requirements, replacing conservative overestimation with data-driven optimization. The predictive model calculates the exact coolant amount needed based on historical performance, environmental conditions, and shipment specifics, maintaining reliability while reducing excess coolant usage.
Solution Approach 2:
The system replaces the mechanical approach of physical trial-and-error and overestimation with an information-based predictive model. By substituting human judgment and conservative buffers with algorithmic predictions based on thermal data, the system achieves reliable temperature control with optimized coolant quantities.
3Manufacturing precision
If precise thermal data collection and modeling is implemented, then the manufacturing precision of logistics plans improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The system achieves universality by creating a multi-functional platform that collects thermal data, builds predictive models, recommends coolant quantities, and audits shipment performance all through a single integrated system. This consolidates multiple functions into one solution, improving logistics plan precision without proportionally increasing overall system complexity.
Data Source
AI summary
Disclosed herein are systems and methods for determining an amount of coolant for shipping a shipment of perishable goods. In one aspect, an exemplary method comprises, collecting thermal data from temperature sensors or temperature indicators from previous shipments, associating the thermal data with its respective shipment, collecting environmental data associated with the previous shipments, building a model of expected thermal behavior that is based both on collected data from temperature sensors or temperature indicators from the previous shipments and the collected environmental data associated with the previous shipments and predicting the amount of coolant for the shipment based on the model of the expected thermal behavior.