AI-Powered Food Safety System: From Manual Tracking to Automated Intelligence
Hilights
Led a 72-hour hackathon project that transformed manual food inspection into an AI-powered system, winning 1st place at Microsoft Hackathon 2021 partner group and achieving an 83% reduction in production harm.
Time: Oct 2021 (72 hours) Role: UX Designer & Presenter Team: 1 PM, 5 Devs, 1 Partnership Manager Tools: Figma, Power BI
tl;dr
In Summary, here's what I accomplished:
Designed user flows and interfaces for real-time monitoring of food safety metrics
Created dashboards visualizing AI recognition data for supplier quality tracking
Built worker safety monitoring system using human factors engineering principles
Presented the solution to Microsoft judges, winning 1st place among partners
After implementing this AI project in the factory:
Reduced production harm by 83%
Saved 264 labor hours monthly
Background
Where Microsoft's Manufacturing Vision Meets Factory's Innovation
Microsoft & WiAdvance
As Microsoft’s gold partner and Wistron (Fortune 500) subsidiary, WiAdvance leveraged our manufacturing networks when Microsoft was expanding into the manufacturing sector in 2021. Microsoft Hackathon Partner Group aimed to implement Azure services in real scenarios, helping their teams understand industry users better.
Qin - Chicken Supplier
Qin, Taiwan’s major chicken supplier, in a market where chicken dominates as the healthy protein choice. Health-conscious consumers demanded better food safety transparency, making it perfect for testing Azure’s AI capabilities.
My Role: UX Designer
I joined this challenge as the UX designer in a cross-functional team of at WiAdvance. My focus was on translating complex manufacturing processes into intuitive interfaces that both workers and managers could easily use.
Research & Discovery
Understanding Quality Control Challenges
Key Stakeholder Insights
Through interviews with COO and on-site observations, we identified two critical needs:
Managers needed data-backed decisions for supplier quality control
Workers needed real-time feedback on chicken quality without disrupting their workflow
How Might We
Transform manual quality inspection into an AI-powered system that enables real-time, data-driven decisions?
Business Requirements (Qin)
Real-time quality monitoring system
Traceable data for farm supplier evaluation
Cost-effective solution to replace manual inspection
Technical Requirements (Microsoft)
Implement Azure AI
Use Power BI for data visualization
Showcase Microsoft’s manufacturing capabilities
Where to Start ?
Strategic Moves: Where AI Adds Most Value
Process Mapping
Mapped the quality inspection journey:
Workers manually inspect chickens for defects
Managers record data in spreadsheets
Quality reports created at day’s end
No real-time data for quick decisions
Solution 1
Real-time Quality Monitoring with AI
Record Defect Data with Azure AI
Trained Azure Vision AI to identify 4 defect types, each telling a different story:
Empty hooks: Worker efficiency issues
Wings defect: Rough handling during hanging
Head defect:
Bruised breast: Poor farm treatment
Head defect: Improper blade positioning
Ensure Process Safety with AI
Broke down chicken handling into 4 key movements
Mapped hand washing into 7 hygiene steps
🚨 System alerts focus on safety, not worker surveillance
Warning sounds prevent harmful movements before injury
Fun Fact
In Taiwan, workers and managers use LINE (like WhatsApp) for everything – even factory alerts.
Solution 2
Data-Driven Farm Evaluation
Key Metrics
We use artificial intelligence to collect data in the production line, including the actual number of chickens, the defect rate, and the average weight. Actual numbers differing from supplier data, high rates of breast bruising defects, and average weights that are too light are all evidence that a farm is not up to standard.
Low-fidelity system prototype
The Supplier Management System Prototype
The Production Status Dashboard Prototype
Award
Microsoft Hackathon 2021 Partner Group - 1st place
Being a gold partner of Microsoft in Taiwan, WiAdvance was privileged to receive an invitation to Microsoft’s 2021 Hackathon, focusing on AI industry applications.
I proudly presented our project, the Azure AI food traceability system, which was in active development. The result was truly remarkable as we won first place in the Hackathon, underlining our commitment to excellence in AI and technology.
Outcomes
From Data to Action
After implementing the AI quality control system:
Reduced production harm by 83% through real-time movement monitoring
Saved 264 labor hours monthly by automating quality inspection.
Won 1st place in Microsoft Hackathon 2021 Partner Group
Key Learnings
Start small but impactful – focusing on 4 key defect types proved more valuable than trying to solve everything at once
Balance automation with human expertise – AI should enhance, not replace, worker knowledge
Real-time feedback is crucial – immediate alerts prevent issues before they become problems