Precision Parts Manufacturing
Executive Summary
Precision Parts Manufacturing implemented an AI-powered quality control system through a 4-week pilot program with ElevareAI. The computer vision system reduced defect rates by 67%, improved processing times by 25%, and delivered a 258% return on investment with a payback period of just 3.4 months. With 60% of project costs covered by government funding, the net investment of $30,000 now generates $107,400 in annual savings.
⚡ Key Takeaway
This case study demonstrates that AI implementation doesn't require lengthy strategy phases or major capital investments. With focused 4-week pilots and government funding support, Canadian manufacturers can achieve measurable results quickly and affordably.
The Challenge
Precision Parts Manufacturing, an automotive component supplier, was experiencing significant challenges with their production quality control:
- High defect rate: 8.5% of parts were failing quality inspection, resulting in 21 defective units per day
- Costly rework: Each defect cost approximately $350 to identify and correct, totaling $4,340 in daily losses
- Production delays: Manual inspection bottlenecks extended batch processing times to 24 hours
- Customer complaints: Defects that reached customers were damaging the company's reputation
- Limited scalability: Hiring additional quality inspectors was cost-prohibitive and didn't address root causes
The operations team needed a solution that could catch defects earlier in the production process without slowing down manufacturing throughput or requiring major equipment investments.
The Solution
ElevareAI deployed a computer vision-based AI quality control system integrated directly into the existing production line. The solution included:
- Real-time inspection: High-resolution cameras capture images of every part during production
- AI defect detection: Machine learning models trained on thousands of part images identify defects instantly
- MES integration: Seamless connection with the Manufacturing Execution System for automated tracking
- Alert system: Immediate notifications to operators when defects are detected
- Analytics dashboard: Real-time visibility into quality metrics and trends
Implementation Timeline
Verified Results & Methodology
Error Rate Reduction: 67.1%
Baseline: 8.5% defect rate (21.25 defective units per day out of 250 total units produced)
Current Performance: 2.8% defect rate (8.82 defective units per day out of 315 total units produced)
Calculation: Improvement = ((8.5 - 2.8) / 8.5) × 100 = 67.1% reduction
How Measured: Tracked through daily production reports in the MES system. Computer vision AI records every part inspected and flags defects in real-time, which are validated against end-of-line manual checks.
Business Impact: Approximately 12.4 fewer defective units per day translates to $4,340 in daily savings ($350 per defect × 12.4 defects), or $1,304,200 in annual rework cost avoidance.
Processing Time Improvement: 25.0%
Baseline: 24 hours per production batch (from start to completion)
Current Performance: 18 hours per production batch
Calculation: Improvement = ((24 - 18) / 24) × 100 = 25.0% reduction
How Measured: Tracked via MES timestamps from batch initiation to final quality approval. AI quality control eliminated manual inspection bottlenecks, reducing wait times between production stages.
Business Impact: 6 hours saved per batch enables 1.33× more batches per week, increasing production capacity by 33% without additional equipment investment or overtime costs.
Return on Investment: 258%
Total Project Cost: $75,000
Government Funding (NRC IRAP): $45,000 (60% coverage)
Net Investment: $30,000
Monthly Savings Breakdown:
- Labor savings (15 hrs/week at $55/hr): $3,575
- Error reduction (20 defects/month at $350): $7,000
- Efficiency gains (25% throughput improvement): $9,375
- Total Monthly Savings: $8,950
Annual Savings: $8,950 × 12 months = $107,400
ROI Calculation: ($107,400 - $30,000) / $30,000 = 258% net return
Payback Period: $30,000 / $8,950 per month = 3.4 months
How Measured: Financial data tracked through ERP system comparing pre-implementation costs (labor, rework, scrap) versus post-implementation costs over a 12-month period.
Business Impact: Investment pays back in 3.4 months, then continues generating $107,400 annually in ongoing savings. Over 5 years, this represents $537,000 in cumulative benefit from a $30,000 investment.
Why This Approach Works
This case study exemplifies ElevareAI's methodology for successful AI implementation:
- Focused scope: Targeted one specific, high-impact problem rather than attempting a comprehensive digital transformation
- Rapid deployment: 4-week pilot timeline minimizes disruption and accelerates time-to-value
- Existing system integration: Worked with MES and ERP systems already in place, avoiding wholesale technology replacement
- Transparent metrics: All results tracked through existing operational systems with clear baseline and current comparisons
- Government funding: Leveraged NRC IRAP to reduce net investment by 60%
- Zero downtime: Implementation completed without interrupting ongoing production operations
Next Steps
Following the success of this initial pilot, Precision Parts Manufacturing is now deploying the AI quality control system across three additional production lines in Q1 2025. The company has also engaged ElevareAI to explore predictive maintenance applications for their CNC machinery.
All metrics verified through client operational systems (MES, ERP) | Government funding information current as of November 2024