Microsoft Hackathon 2021

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:

food factory interview and insights
  • 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. 

devices-chicken-thumbnail

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.

prototyping the system
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

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