Perishable Freshness Scoring Using Sensor and ML Supply Chain Data
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Solution Overview
Problem
The food supply chain faces challenges in maintaining perishable goods within specific environmental conditions, leading to spoilage and excessive waste due to inadequate monitoring and data management, which results in labor inefficiencies, consumer dissatisfaction, and potential health risks.
Innovation Solution
A system that determines and utilizes scores, such as ProofScore and FreshScore, to enhance transparency and freshness tracking of perishables through sensor data analysis and machine learning, incentivizing stakeholders to improve their practices and reduce waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If paper-based supply chain data tracking is used, then implementation cost is low, but data accessibility and transparency are poor leading to undetected spoilage
Solution Approach 1:
The patent replaces manual paper-based data collection with automated sensor systems that continuously monitor temperature, humidity, and shock conditions. This substitution eliminates the need for manual data entry and paper tracking, enabling real-time data accessibility while reducing the operational complexity of data management through automated processing and analysis algorithms.
Solution Approach 2:
The patent introduces a centralized data management platform that acts as an intermediary between sensors, supply chain partners, and consumers. This platform aggregates data from multiple sources, processes it through machine learning algorithms, and presents actionable insights to stakeholders, thereby improving data accessibility without requiring each participant to manage complex individual systems.
2Measurement precision
If real-time sensor monitoring is implemented, then freshness detection accuracy is improved, but implementation cost and device complexity increase
Solution Approach 1:
The patent divides the monitoring system into modular components: temperature sensors, humidity sensors, shock detectors, and communication modules. Each sensor type independently monitors specific parameters, and the system processes data from each segment separately before integrating results. This segmentation improves measurement precision for each parameter while reducing overall system complexity through standardized, interchangeable modules.
Solution Approach 2:
The patent designs a multi-functional sensor platform that simultaneously monitors temperature, humidity, shock, and location using integrated sensors and algorithms. This universal system replaces multiple separate monitoring devices, achieving high measurement precision across all parameters while reducing device complexity through consolidation and shared processing architecture.
3Loss of substance
If continuous monitoring throughout the supply chain is performed, then food waste is reduced, but energy consumption and operational cost increase
Solution Approach 1:
The patent implements periodic sampling of environmental conditions rather than truly continuous monitoring. Sensors collect data at predetermined intervals (e.g., every 15 minutes or at key supply chain milestones), which is sufficient to detect spoilage trends while significantly reducing energy consumption compared to constant monitoring. The system adjusts sampling frequency based on product sensitivity and risk levels.
Solution Approach 2:
The patent employs feedback mechanisms where the system learns from historical data and adjusts monitoring intensity dynamically. When conditions are stable and within acceptable ranges, monitoring frequency is reduced to conserve energy. When anomalies or critical thresholds are approached, the system increases monitoring intensity, thereby reducing food waste through timely detection while optimizing energy consumption through adaptive resource allocation.
Data Source
AI summary
Provided herein are techniques, devices, and systems for reducing waste by determining and utilizing scores associated with perishables that have been, or that are in the process of being, transported along a supply chain. A computing system may receive sensor data from a sensor within a threshold distance of the perishable, may provide the sensor data as input to a trained machine learning model, which generates, as output a score relating to a freshness of the perishable. This score and related information can be made accessible to users by associating an identifier with the score and with the information..