How One OEM Cut Root Cause Time by 65% - and Saved Millions

A major automotive OEM saved $1.5M per 10 minutes of downtime reduced and significantly reduced root cause analysis time by 65% after partnering with Invisible AI. By deploying AI-powered 3D vision and motion analytics on a new EV battery line, they eliminated hidden downtime, uncovered cross-shift inefficiencies, and validated process improvements — all within weeks. The result: a measurable, scalable, and continuously improving assembly process.

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CHALLENGE

To meet aggressive launch timelines, a global OEM selected its EV battery assembly line as the pilot site for Invisible AI’s next-generation process visibility product. The team aimed to reduce downtime, increase standard work adherence, and enhance root-cause analysis — but lacked the real-time, unbiased data needed to move at speed.

Traditional plant data sources (PLCs, audits, and quality checks) couldn’t answer:

• Why are we losing time in manual processes?
• Which stations have the most waste?
• Are both shifts performing the same?
• Why is one station consistently over takt time?
  
  

The OEM had no scalable way to collect complete process data, deep dive into value-add vs. non-value-add work, or analyze process consistency across shifts. Manual audits were too slow and too subjective. And while vision systems were already in use for quality inspection, they offered no insight into operator behavior — a critical blind spot.

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SOLUTION

Invisible AI’s edge-based 3D vision platform was deployed across the entire EV battery line, delivering automated, real-time motion analytics without adding sensors or interfering with operations.

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Deployed Capabilities:

• Time & motion studies on every process, every cycle
• Standard work & step adherence monitoring
• Ergonomics & motion heatmaps
• Root cause analysis via searchable, timestamped video
• Cross-shift performance comparison tools
• Value-add vs. non-value-add work classification
  
  

In days, engineers and supervisors gained a system of record for how work is performed, not just how parts move. By automating data collection and analysis, the site could now identify problems and validate improvements 10x faster — with zero bias.

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IMPACT

Line Balancing & Labor Optimization

• Real-time data revealed upstream bottlenecks and downstream idle time
• Rebalancing two stations reduced operator walking, eliminated wasted motion, and allowed for strategic headcount reallocation while maintaining takt time
  
  

Cycle Time Reduction

• The target cycle time was under 90 seconds
• Using motion heatmaps, the team identified a station regularly exceeding 100 seconds per cycle
• By standardizing material presentation and shortening the walking path, the team saved 11 seconds per cycle
• The improvement was validated and implemented across the line in less than one week
  
  

Cross-Shift Visibility

• Invisible AI surfaced that second shift consistently outperformed first on one process
• Root-cause: undocumented process improvements adopted by second shift
• Invisible AI enabled faster knowledge transfer and retraining, closing the performance gap
  
  

Accelerated Root-Cause Analysis

• Process investigation time dropped by 65% due to instant video playback and PLC / product metadata integration, creating a searchable video database
• By surfacing issues remotely, engineers now resolve 3x more issues per week and engage in more targeted, productive floor interactions

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Standard Work Adherence

• Real-time alerts identified deviations from standard cycle times, indicating potential non-adherence to standard work
• Supervisors could retrain operators within minutes, preventing negative downstream defects
• No need for disruptive manual audits
  
  

Quantified Value

• Eliminating just 10 minutes of downtime per shift = $1.5M/year saved
• Faster fixes, fewer defects, and millions saved = a 65% reduction in root cause analysis time
• Net new data generation and speed of resolution: Priceless 

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WHY IT MATTERS

Invisible AI bridges the most critical data gap in manufacturing: the human element. While legacy vision systems monitor machines and parts, Invisible AI makes manual assembly work observable, measurable, and improvable at scale.

Faster: Process improvements validated in hours, not weeks
Unbiased: Video-based data eliminates subjectivity
Scalable: Analyze entire shifts, stations, and lines automatically
Actionable: Root-causes and bottlenecks are surfaced in real time

By turning every movement into measurable data, Invisible AI empowers manufacturers to:

• Optimize ramp-up speed
• Reduce downtime and rework
• Improve safety and ergonomics
• Standardize best practices across teams
  
  

‍Want to accelerate ramp-up and unlock hidden efficiencies?
Contact Invisible AI to schedule a demo.