SupplyChAIn

Demo

About

The problem it solves Quality deterioration and waste: Up to 30-40% of berry shipments experience quality issues or complete loss due to undetected temperature breaches, resulting in millions in financial losses and environmental impact. Lack of accountability: When quality issues arise, there's no reliable way to determine where in the supply chain the problem occurred, leading to disputes between suppliers, transporters, and retailers. Limited traceability: Traditional monitoring systems create siloed data that doesn't follow products through the entire supply chain, preventing comprehensive analysis and improvement. Delayed interventions: Without real-time alerts, temperature excursions are often discovered too late to take corrective action, resulting in entire shipments being rejected upon arrival. Compliance challenges: Food safety regulations require documented temperature control, but current manual systems are prone to errors and manipulation.

How it's made

Challenges we ran into Technical Integration Complexity Connecting IoT temperature sensors across diverse environments (fields, warehouses, trucks, retail) Ensuring reliable data transmission in areas with limited connectivity Maintaining sensor calibration and accuracy in harsh conditions Blockchain Implementation Hurdles Smart contract development and testing for temperature recording and quality scoring Gas fees and transaction costs on the Sonic/EduChain blockchain Balancing on-chain vs. off-chain data storage for cost efficiency Managing blockchain scalability as transaction volume increases Stakeholder Adoption Barriers Resistance from traditional supply chain participants Training requirements for users across technical skill levels Initial investment costs for hardware implementation Convincing stakeholders of ROI despite upfront costs Data Privacy and Governance Balancing transparency with competitive business information Managing access controls while maintaining decentralization Compliance with data protection regulations across jurisdictions Determining ownership of shared supply chain data Quality Assessment Algorithm Refinement Developing accurate scoring models that account for berry variety differences Calibrating shelf-life predictions based on real-world validation Accounting for other variables beyond temperature (humidity, handling) Continuous improvement of AI models with limited initial datasets System Reliability and Fault Tolerance Ensuring system functionality despite sensor failures Creating redundancy in critical monitoring points Managing blockchain network congestion during peak periods Developing offline contingency protocols Interoperability Issues Integration with existing ERP and supply chain management systems Data standardization across different stakeholder platforms API development for third-party application connectivity Forward compatibility with emerging IoT and blockchain standards Regulatory Compliance Meeting food safety req