Streamlined Inspection Workflow
A more guided, step-by-step structure reduced operator confusion and improved consistency.
UX Case Study
AI-Driven Material & Input Inspection System
Redesigning Volvo’s incoming material inspection process — a critical quality checkpoint in automotive and EV manufacturing.
- Role
- End-to-End UX/UI Designer
- Duration
- 6+ Months
- Team
- Senior Designer, Lead, Devs
- Industry
- Automotive / EV Mfg.
At-a-Glance 👀
During my internship, I worked on redesigning Volvo’s incoming material inspection process. The existing workflow relied heavily on manual visual inspection, which resulted in inconsistent accuracy, slow detection of defects, and very limited traceability. These issues increased labour cost, created variability in quality, and caused production disruptions.
I contributed to designing an AI-assisted inspection system that automates defect detection, simplifies the inspection workflow, and provides digital, timestamped traceability across the line.
The Problem
The previous inspection process had major challenges:
- Inspectors manually checked each incoming part
- High variability in defect detection between operators
- Labour-intensive and time-consuming workflow
- No structured digital traceability
- Frequent line disruptions caused by undetected defects
This created inefficiencies, delays, and quality risks for downstream production.
🎨 My Role
Even as an intern, I directly contributed to:
- Redesigning the inspection workflow end-to-end
- Creating simplified structures for defect logging
- Designing automation-ready UI patterns
- Streamlining the left/right part identification flow
- Conceptualizing traceability and dashboard structures
- Iterating with developers on feasibility
Research Approach
As an intern, I worked under close guidance from senior designers and leads. My research included:
- Identifying usability gaps
- Understanding workflow constraints from manager briefs
- Feasibility discussions with developers
- Iteration cycles with senior designer reviews
Even without direct user interviews (due to access limitations), I gained clear insights into pain points and system requirements.
✨ Design Strategy & Decisions
The redesigned system focused on speed, clarity, and automation readiness.
Automated Defect Detection
The interface was designed to support AI-powered visual inspection, reducing manual effort drastically.
Clear Part Identification
Improved part labeling eliminated one of the most common operator mistakes (Left vs Right).
Digital Traceability
Every inspection was logged with timestamps, operator data, and status — replacing manual notebooks.
Error Prevention Mechanisms
Validation steps ensured defects were logged accurately before progressing.
📈 Impact
Published By NorrSpect
| Area | Before | After |
|---|---|---|
| Defect Detection Accuracy | ~78% | >98.5% |
| Inspection Time per Part | 3–6 minutes | <10 seconds |
| Traceability | Manual logs | Fully digital, timestamped |
| Operator Effort | High | Near-zero |
| Production Downtime | Frequent | Reduced by ~40% |
💬 What I Learned
This project was a major milestone in my UX journey. I learned:
- Enterprise UX demands clarity over decoration
- Workflow simplification directly impacts accuracy
- Automation + UX significantly reduces human variability
- Designing for factories requires understanding real constraints
- Developer collaboration ensures the design is implementable
- Data + traceability improve decision-making at every level
This experience strengthened my confidence in handling complex, large-scale UX problems.
Public Disclosure Note
To respect confidentiality, all UI visuals, workflows, diagrams, and system details are intentionally omitted.
Only high-level design decisions, challenges, and measurable outcomes are shared.
I’m happy to walk through my thought process during an interview under confidentiality.