The problem SupplyChAIn 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.

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