Transit Item Mortality Prediction via Sensor Data and Blockchain
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Solution Overview
Problem
Environmental conditions during transit, such as vibration, shock, heat, and humidity, can negatively impact the condition and expected lifespan of items, leading to uncertainty in item reliability and consumer confidence, as these conditions are often unknown or misrepresented, resulting in potential premature failures and reputation damage for retailers.
Innovation Solution
Implementing a system with sensors in shipping containers that track environmental conditions and share data via a blockchain to determine item mortality based on sensor data, using mortality models that consider the susceptibility of individual components to predict failure rates and remaining lifespan, enabling informed disposition recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors are installed in shipping containers to track environmental conditions, then the reliability of item data is improved, but the device complexity increases
Solution Approach 1:
The system segments the monitoring function by installing individual sensors for different environmental conditions (temperature, humidity, shock, vibration) within shipping containers. Each sensor independently measures a specific parameter, and the data is aggregated later. This segmentation allows reliable tracking without requiring a single complex monolithic system.
Solution Approach 2:
A blockchain platform serves as an intermediary between the sensors and the data processing system. The blockchain verifies and records sensor data without requiring centralized authority or complex verification mechanisms, thereby improving data integrity while keeping the overall system architecture relatively simple.
2Measurement precision
If mortality models use detailed component-specific data to predict failure rates, then the measurement precision of item reliability is improved, but the loss of information increases due to trade secret protection requirements
Solution Approach 1:
The system creates a simplified copy or representation of component-level mortality data that captures the essential patterns and relationships needed for accurate reliability prediction, while omitting or anonymizing specific trade-secret-protected details. This allows the mortality model to function with reduced information content that does not expose proprietary data.
Solution Approach 2:
The system transforms detailed component-specific parameters into aggregated or anonymized parameter representations. Instead of using raw trade-secret-protected component data, the system uses transformed parameters that maintain the predictive capability while removing identifiable information about specific components or manufacturing processes.
3Reliability
If multiple sensors track various environmental conditions during transit, then the reliability of item condition assessment is improved, but the device complexity and cost increase
Solution Approach 1:
The monitoring system is segmented into separate sensor modules, each dedicated to measuring a specific environmental condition (temperature sensors, humidity sensors, shock sensors, vibration sensors). This modular segmentation allows reliable multi-parameter tracking while keeping each individual sensor component simple and manageable.
Solution Approach 2:
The system uses a universal data collection and processing framework that handles multiple sensor types and environmental parameters through a single integrated architecture. The blockchain platform and data processing systems serve multiple functions (verification, storage, analysis) across all sensor types, reducing overall system complexity compared to separate dedicated systems for each parameter.
Data Source
AI summary
Item mortality, such as failure rates and expected remaining lifespan, may be determined based on tracked environmental conditions during transit, such as vibration, temperature, and other conditions. For complex items, multiple different internal components may have varying degrees of susceptibility to damage from unfavorable conditions, with each of the components able to individually cause a failure of the entire item. Thus, the specific set of internal components may drive item mortality prediction. Sensors may be located in shipping containers that share data collected throughout the supply chain, using a blockchain, to increase confidence in the integrity of the data. The use of mortality models generated with anonymized component or process data may enable manufacturers to protect trade secrets, such as the specific components or manufacturing processes used, even while leveraging the manufacturer's detailed knowledge of an item.