Blockchain Supply Chain Data Repository for Perishable Commodity Tracking
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Solution Overview
Problem
Current methods for managing the supply chain of perishable commodities are inefficient, leading to significant food waste due to lack of comprehensive data integration, inaccurate chain of custody representations, and inadequate food safety protocols, resulting in redundant data systems and incomplete information access across the supply chain.
Innovation Solution
A method involving the creation of a centralized data repository using blockchain and distributed ledger technology to capture, store, and integrate data from sensors and transactional entities, with a transaction gateway for format translation and a 'gold copy' for security, enabling comprehensive chain of custody representation and analytics for quality assessment and shelf life determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized data repository using blockchain technology is implemented to integrate data from multiple supply chain entities, then data security and information completeness are improved, but device complexity and implementation cost increase
Solution Approach 1:
The system is divided into distinct layers: infrastructure layer for data capture and storage, application layer for data processing and analytics, and user interface layer for interaction. This segmentation allows each layer to be developed, maintained, and secured independently, reducing overall system complexity while maintaining data security through distributed ledger technology.
Solution Approach 2:
A transaction gateway acts as an intermediary component that standardizes data exchange between different supply chain entities and the blockchain repository. This mediator handles format translation and validation, reducing the complexity of direct integrations while ensuring data security through standardized protocols.
2Loss of information
If comprehensive data from all supply chain entities is captured and integrated, then chain of custody accuracy and food safety monitoring are improved, but data management complexity and processing requirements increase
Solution Approach 1:
The blockchain-based repository serves multiple functions simultaneously: it acts as a data storage system, a verification mechanism for chain of custody, a food safety monitoring platform, and an analytics engine. This multi-functionality consolidates what would otherwise require separate systems, reducing data management complexity while maintaining complete information across all supply chain stages.
3Measurement precision
If real-time sensor data and transactional data are continuously captured and stored, then shelf life prediction accuracy and quality assessment are improved, but data storage requirements and processing energy increase
Solution Approach 1:
The system performs preliminary data validation, filtering, and standardization at the transaction gateway before data enters the blockchain repository. This preliminary processing reduces the volume of raw data that requires intensive computation for shelf life prediction, thereby reducing energy consumption while maintaining measurement precision through selective data capture of critical parameters.
4Adaptability or versatility
If multiple data formats from different entities are accepted and integrated, then system adaptability and entity participation are improved, but data standardization complexity increases
Solution Approach 1:
The transaction gateway dynamically adjusts data parameters based on the source entity and format type, automatically applying appropriate transformation rules. This parameter-based approach allows the system to accommodate multiple data formats from different supply chain entities while maintaining a standardized internal representation, reducing the need for complex custom integration logic for each entity.
Data Source
AI summary
A method of managing information concerning the supply chain of a perishable commodity utilizing a data repository capturing multiple data sources. The method further includes the collating of the data into a chain of custody information representation for use by data contributors.