GE Aerospace AI Demo Showcase

Interactive Demonstrations of Anthropic AI Solutions for Supply Chain Transformation

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Demo 1: Supplier Quality Incident Resolution

From 24 Hours to 6 Hours - AI-Powered Root Cause Analysis

The Scenario

A priority supplier at the Muskegon, Michigan facility (hot section components for CFM LEAP engines) reports a batch of defective turbine blades with micro-cracks discovered during quality inspection. This is blocking $2M+ of engine assemblies and threatens delivery schedules.

Traditional Approach
24+ Hours
Manual investigation and documentation
→
With Claude Enterprise
6 Hours
AI-powered analysis and automation

Traditional Approach (24+ Hours)

  • Engineer receives defect report via email
  • Manually searches through 100+ previous defect reports for similar issues
  • Reviews supplier's manufacturing specs (200-page PDF)
  • Consults with materials expert (schedule delay)
  • Drafts root cause analysis report (4-6 hours)
  • Creates corrective action plan
  • Submits for AS9100D compliance review
  • Communicates back to supplier

With Claude Enterprise (6 Hours)

Engineer prompt to Claude:

"Analyze this turbine blade micro-crack defect report from Muskegon supplier. Cross-reference with our historical quality database and identify root cause."
Claude Response (generated in ~15 minutes): Analysis of Defect Report: Turbine Blade Micro-Cracks (Part #LEAP-TB-4428) ROOT CAUSE IDENTIFIED: Based on analysis of 127 similar historical defects, this pattern is consistent with heat treatment process deviation during solution annealing. EVIDENCE: 1. Historical Pattern: 23 similar micro-crack incidents (2018-2024) - 19 of 23 traced to annealing temperature variance >±15°F - Supplier's furnace #3 showed similar patterns in Jan 2024 incident 2. Material Analysis: Inconel 718 superalloy requires precise thermal cycle - Specification: 1750°F ±10°F for 1 hour - Deviation creates residual stress → micro-cracks during cooling 3. Supplier Equipment: Furnace #3 thermocouple calibration overdue - Last calibration: 180 days ago (spec requires 90-day intervals) - Similar equipment issue caused 2024-01 batch rejection RECOMMENDED IMMEDIATE ACTIONS: 1. Quarantine all parts processed in Furnace #3 since last calibration (est. 450 parts) 2. Emergency thermocouple calibration and furnace validation 3. Re-process quarantined parts through validated furnace 4. Implement automated calibration alerts (preventive measure) SUPPLIER IMPACT: - Affected batch: 87 turbine blades (current) - Potentially affected: 450 parts (precautionary quarantine) - Recovery timeline: 5-7 days with expedited re-processing CORRECTIVE ACTION PLAN: See attached AS9100D-compliant CAPA document (auto-generated)
  • Rapid Analysis (15 minutes): Claude analyzes defect report against 10,000+ historical quality reports
  • Automated Documentation (30 minutes): Generates 8D report, CAPA form, supplier notification, updated scorecard
  • Expert Consultation (AI-Assisted): Engineer asks follow-up questions for risk-weighted analysis
  • Preventive Measures (45 minutes): Claude suggests automated monitoring and updated procedures
Time Saved
75%
24 hours → 6 hours resolution
Quality Impact
450
Potentially defective parts identified
Cost Avoidance
$12M+
$2M immediate + $10M+ field failures
Documentation
100%
AS9100D compliant (automated)

Business Value Demonstrated

AI processes vast quality databases instantly (1M token context = 500+ reports), generates compliant documentation automatically, provides predictive recommendations to prevent recurrence, and calculates business impact for informed decision-making.

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Demo 2: Supplier Integration Project Acceleration

From 6-9 Months to 6 Weeks - Claude Code in Action

The Scenario

GE Aerospace needs to integrate a new secondary supplier in Durham, NC for LEAP compressor blades as part of the 80% dual-sourcing goal and $16M Durham facility investment. Traditional supplier integration takes 6-9 months—AI accelerates this to 6 weeks.

Traditional Approach
6-9 Months
Manual development and integration
→
With Claude Code
6 Weeks
AI-accelerated development

Week-by-Week Breakdown

  • Week 1: Requirements & Analysis
    Claude Code analyzes supplier data format and GE integration requirements, creates comprehensive technical design document (50 pages), generates data mapping matrix, produces API integration architecture diagram
  • Week 2-3: Development
    Data extraction layer (SFTP + validation), transformation & validation logic, SAP & Teamcenter PLM integration, monitoring & alerting dashboards—all generated with Claude Code assistance
  • Week 4-5: Testing & Validation
    Comprehensive test suite (unit, integration, end-to-end), automated test data generation, performance testing (10,000+ parts/day), security testing for ITAR compliance
  • Week 6: Deployment & Documentation
    Deployment automation (Infrastructure as Code), operations runbook, user training materials, API documentation, maintenance and support guide

Code Generation Example

Engineer prompt to Claude Code:

"Create Python module to extract data from supplier's SFTP server, validate against XSD schema, and stage in AWS S3"

Claude Code Produces (automatically):

✓ SFTP connection handler with retry logic
✓ XML validation against aerospace data standards (SPEC2000)
✓ S3 upload with encryption and metadata tagging
✓ Comprehensive error handling and logging
✓ Unit tests with 95%+ coverage
✓ Complete documentation

Timeline Reduction
75-90%
6-9 months → 6 weeks
Cost Savings
$300K
$200K project + faster ROI
Code Coverage
95%+
Comprehensive testing included
Scalability
49+
Template for remaining suppliers

Proven Results: Altana Case Study

2-10x developer velocity improvement for supply chain systems. GE Aerospace achieves similar results, supporting 80% dual sourcing goal and accelerating $16M Durham facility ROI.

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Demo 3: Real-Time FLIGHT DECK SQDC Monitoring

From Weekly Reports to Real-Time Insights in 30 Seconds

The Scenario

GE Aerospace has deployed FLIGHT DECK methodology across 15 priority suppliers, achieving 26% material input increase. Leadership needs real-time visibility across the supplier network to scale these gains to 200+ suppliers while maintaining >90% on-time delivery.

Traditional Approach
Weekly
Manual reports with 24-48 hour lag
→
With Claude Enterprise
Real-Time
Instant insights via natural language

Executive Natural Language Query

Executive asks Claude:

"Show me SQDC status for all 15 priority suppliers. Highlight any concerning trends and recommend actions."
Claude Response (Generated in ~30 seconds): ═══════════════════════════════════════════════════════════════ FLIGHT DECK SQDC DASHBOARD - Priority Suppliers (15 sites) Last Updated: October 27, 2025 10:45 AM EDT ═══════════════════════════════════════════════════════════════ 📊 OVERALL HEALTH: 🟢 STRONG (13 sites) | 🟡 ATTENTION (2 sites) | 🔴 CRITICAL (0 sites) ═══════════════════════════════════════════════════════════════ 🛡️ SAFETY (Priority #1) ═══════════════════════════════════════════════════════════════ Status: 🟢 ALL SITES COMPLIANT • Days Since Last Recordable: 847 days (industry-leading) • Near-Miss Reports: 23 this week (↑ 15% vs. last week) → Trend: Increased reporting = positive safety culture • Leading Indicator: All sites completed monthly safety training ⚠️ ATTENTION ITEM: • Muskegon facility: 3 near-misses related to hot section handling → Recommended Action: Supplemental PPE training scheduled Nov 2 → Risk Level: LOW (preventive measure) ═══════════════════════════════════════════════════════════════ ✓ QUALITY ═══════════════════════════════════════════════════════════════ Status: 🟡 ATTENTION NEEDED (1 site) • Network-Wide Defect Rate: 0.23% (↓ 12% vs. last month) 🟢 • Supplier Quality Index: 94.7/100 (target: 95+) 🟡 • AS9100D Compliance: 100% (15/15 sites certified) 🟢 ⚠️ ATTENTION ITEMS: 1. Muskegon Facility - Turbine Blade Micro-Cracks • Issue: Heat treatment furnace calibration overdue • Impact: 87 parts quarantined, 450 parts under review • Status: Corrective action in progress (ETA: Nov 1) • Root Cause: Equipment maintenance scheduling gap • PREDICTION: Issue resolved within 5 days, no delivery impact 2. Greater Cincinnati - Minor Paint Adhesion Issues • Issue: Humidity control variance in coating booth • Impact: 12 parts require rework (non-critical path) • Status: Facility HVAC adjustment scheduled Oct 28 🎯 POSITIVE TRENDS: • Durham facility: 45-day streak of zero defects (RECORD) 🎉 • Lafayette facility: Quality index improved 18% since AI tools deployed ═══════════════════════════════════════════════════════════════ 📦 DELIVERY ═══════════════════════════════════════════════════════════════ Status: 🟢 EXCELLENT • Network On-Time Delivery: 92.3% (↑ from 50% in early 2024) 🟢 • LEAP Engine Components: 94.1% OTD (target: 90%+) 🟢 • Material Input Increase: 26% vs. 2024 baseline (FLIGHT DECK success) 🟢 🔮 PREDICTIVE INSIGHTS: • Next 30 Days Forecast: 93.1% OTD (↑ 0.8 points) • Risk Factors: 2 suppliers showing early capacity strain signals → West Chester: Production lead time increased 1.5 days (↑ 12%) → Asheville: CMC material yield declining 3% (still within spec) → RECOMMENDED ACTION: Deploy engineering support to both sites (proactive) ═══════════════════════════════════════════════════════════════ 💰 COST ═══════════════════════════════════════════════════════════════ Status: 🟢 ON TARGET • Cost Performance Index (CPI): 1.04 (4% under budget) 🟢 • Supplier Investment ROI: $100M+ deployed, 22% faster ROI vs. forecast 🟢 • Cost Avoidance: $12.3M YTD (prevented quality issues, disruptions) 💡 OPTIMIZATION OPPORTUNITIES: 1. Energy Costs: 4 suppliers eligible for LED lighting rebates ($75K/year) 2. Material Waste: AI-optimized cutting patterns = $200K/year savings 3. Logistics: Consolidated shipping from Cincinnati region = $150K/year Total Identified Savings: $425K/year with <6 month payback
Response Time
30 Sec
vs. 24-48 hour reports
Data Sources
15
Suppliers monitored real-time
Predictive
30 Days
Forecast accuracy: 95%+
Scalability
200+
Suppliers (same effort)

Scale FLIGHT DECK Success: 26% → 40%+ Material Input Increase

AI enables real-time visibility and predictive analytics across entire supplier network, maintaining >90% on-time delivery while scaling from 15 to 200+ suppliers.

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Demo 4: New Engineer Onboarding & Knowledge Transfer

From 6 Months to 2 Months - AI-Accelerated Learning

The Scenario

GE Aerospace is hiring 5,000 engineers in 2025 and deploying 550+ to supplier sites. Traditional onboarding takes 6 months before engineers reach full productivity. Claude Enterprise acts as a 24/7 AI mentor, accelerating onboarding to 2 months.

Traditional Onboarding
6 Months
To full productivity
→
With Claude Enterprise
2 Months
AI-assisted ramp-up

Day 1: AI-Powered Orientation

New Engineer asks Claude:

"I'm a new mechanical engineer assigned to support the Muskegon supplier site for CFM LEAP turbine blades. What do I need to know?"
Claude Response (Comprehensive Onboarding): Welcome to GE Aerospace! I'll be your AI assistant throughout your career here. ROLE OVERVIEW: Supplier Site Engineer - Muskegon Facility ═══════════════════════════════════════════════════════════════ FACILITY PROFILE: • Location: Muskegon, Michigan (70 miles north of Grand Rapids) • Products: Hot section turbine blades for CFM LEAP engine • Materials: Inconel 718, Inconel 738 (nickel-based superalloys) • Processes: Investment casting, heat treatment, machining, coating • Production Volume: 2,500 blades/month (ramping to 3,000/month by Q2 2026) • GE Engineers On-Site: 12 (including you) • Your Manager: Sarah Johnson, Senior Manufacturing Engineer STRATEGIC IMPORTANCE: • Critical path component for LEAP engine (each engine requires 80 turbine blades) • Supplier performance directly impacts GE's 15-20% LEAP delivery increase goal • Recent challenge: Heat treatment quality issues (Oct 2025 - being resolved) • Part of $70M Muskegon facility expansion (new equipment arriving Q1 2026) YOUR KEY RESPONSIBILITIES: 1. Quality assurance: Monitor and improve First Pass Yield (target: 98%+) 2. FLIGHT DECK implementation: Lead daily SQDC standup meetings 3. Process improvement: Identify and execute kaizen opportunities 4. Supplier collaboration: Bridge between GE and supplier engineering teams 5. Technical problem-solving: Root cause analysis for quality escapes GETTING STARTED - FIRST 30 DAYS: □ Week 1: Facility tour, meet the team, safety certification □ Week 2: Learn turbine blade manufacturing process end-to-end □ Week 3: Shadow senior engineer on quality incident investigation □ Week 4: Lead your first SQDC meeting, present findings to team KNOWLEDGE BASE ACCESS: I have access to: • 15,000+ engineering documents for LEAP engine program • 10 years of Muskegon facility quality history • GE's manufacturing processes and standards library • FLIGHT DECK methodology best practices • AS9100D aerospace quality requirements Ask me anything, anytime. I'm here 24/7 to support you.

Week 3: Technical Deep Dive

Engineer asks for technical guidance:

"Explain the heat treatment process for Inconel 718 turbine blades and why temperature control is so critical."

Claude Provides:

✓ Graduate-level materials science explanation
✓ Phase transformation diagrams
✓ Precipitate formation chemistry (gamma prime, gamma double prime)
✓ Microstructure photos from GE's database
✓ Case studies of heat treatment failures
✓ Quality checkpoints and inspection criteria

Week 4: First Quality Issue (AI-Guided Problem Solving)

Engineer faces first challenge:

"I'm investigating a batch of blades with dimensional non-conformance. Blade trailing edge is 0.015" thicker than spec. Walk me through root cause analysis."

Claude Guides Through 8D Problem-Solving:

1️⃣ Defines the problem in technical terms
2️⃣ Implements immediate containment actions
3️⃣ Guides root cause investigation (5-Why, Fishbone diagram)
4️⃣ Helps develop corrective actions
5️⃣ Generates AS9100D-compliant documentation
6️⃣ Suggests preventive measures

Result: Engineer successfully resolves issue in 1 week (vs. 3-4 weeks traditional)

Onboarding Time
67%
Reduction: 6 months → 2 months
Productivity Curve
70-80%
Effective at Day 30 (vs. 30-40%)
Knowledge Access
24/7
Expert guidance anytime
Mentor Ratio
1:10+
One senior engineer supports 10+ new hires

Business Value: $262M/Year

5,000 new engineers × 4 months earlier productivity × $175K average cost = massive organizational capability acceleration. Plus knowledge retention and best practice sharing across 550+ deployed engineers.

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Demo 5: Advanced Manufacturing System Development

From 18 Months to 3 Months - Production Control for $51M Auburn Facility

The Scenario

GE Aerospace is investing $51M in Auburn, Alabama facility to scale additive manufacturing (3D printing) for LEAP engine fuel nozzles. Traditional software development for production control takes 18 months. Claude Code accelerates to 3 months.

Traditional Development
18 Months
Requirements → production deployment
→
With Claude Code
3 Months
AI-accelerated development

Month 1: System Architecture

Engineer prompt to Claude Code:

"Design production control system for 10x EOS M400 3D printers manufacturing LEAP engine fuel nozzles. Requirements: real-time monitoring, quality prediction, MES integration, AS9100D traceability."

Claude Code Delivers:

1. System Architecture Document (75 pages)
• Hardware interface design (printer APIs)
• Cloud infrastructure (AWS)
• Database schema (time-series + relational)
• Security architecture (ITAR compliance)
• Scalability plan (10 printers → 50 printers)

2. Technical Specifications
• Real-time data collection (50+ sensors per printer)
• Machine learning model for print quality prediction
• Automated defect detection using computer vision
• Digital twin simulation for process optimization

Month 2: Development Sprint (4 Major Applications)

  • Real-Time Monitoring Dashboard
    React/TypeScript frontend with WebSocket updates, Python FastAPI backend (1M+ data points/hour), TimescaleDB for time-series sensor data, mobile-responsive design
  • Predictive Quality System
    ML model predicting quality 4+ hours before completion, 94% accuracy, automated alerts for intervention, explainable AI (SHAP values)
  • MES Integration & Traceability
    Dassault DELMIA API client, automated work order processing, material traceability (powder lot → finished part), AS9100D documentation generation, blockchain for tamper-proof records
  • Computer Vision Quality Inspection
    CNN-based defect detection (trained on 50,000+ images), automated dimensional measurement, pass/fail decision with confidence scoring, continuous learning from human corrections

Production Results (After Deployment)

Print Success Rate
96%
Up from 89% (predictive quality)
Inspection Time
87.5%
Reduction: 2 hours → 15 min/part
Documentation
Automatic
AS9100D compliance (4 hours → 0)
Throughput
+20%
1,200 parts/month (vs. 850 target)

ROI: $1.6M Saved + 15 Months Time-to-Value

Traditional approach: $2M + 18 months. With Claude Code: $400K + 3 months. Faster ROI on $51M Auburn facility investment, plus template reusable for $14M West Chester expansion and future advanced manufacturing initiatives.

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Demo 6: Executive Decision Support

From 2-3 Weeks to 4 Hours - Strategic Capital Allocation

The Scenario

Technology & Operations leadership needs to decide: Should we accelerate Durham secondary supplier ramp-up or invest in additional capacity at existing Muskegon supplier? $25M capital allocation decision requiring comprehensive cross-functional analysis.

Traditional Analysis
2-3 Weeks
Multiple teams, meetings, spreadsheets
→
With Claude Enterprise
4 Hours
Comprehensive AI analysis

Executive Natural Language Query

Executive asks Claude:

"Analyze two options for increasing CFM LEAP turbine blade capacity:

Option A: Accelerate Durham secondary supplier ramp-up ($25M investment)
Option B: Expand existing Muskegon supplier capacity ($25M investment)

Provide comprehensive analysis including: financial ROI, risk assessment, supply chain resilience, technical feasibility, and recommendation."
Claude Response (Generated in ~5 minutes): ═══════════════════════════════════════════════════════════════ EXECUTIVE DECISION ANALYSIS: LEAP Turbine Blade Capacity Expansion ═══════════════════════════════════════════════════════════════ INVESTMENT COMPARISON: Option A (Durham) vs. Option B (Muskegon) 📊 FINANCIAL ANALYSIS OPTION A: Durham Secondary Supplier Acceleration • Capital Investment: $25M • Implementation Timeline: 12 months to full production • Incremental Capacity: 1,500 blades/month • Cost per Part: $1,850 (10% higher due to learning curve) • Payback Period: 2.3 years • NPV (10% discount rate): $67M over 10 years • IRR: 24.5% OPTION B: Muskegon Existing Supplier Expansion • Capital Investment: $25M • Implementation Timeline: 8 months to full production • Incremental Capacity: 1,800 blades/month • Cost per Part: $1,680 (mature process, economies of scale) • Payback Period: 1.9 years • NPV (10% discount rate): $79M over 10 years • IRR: 28.2% FINANCIAL WINNER: Option B (Muskegon) - Higher NPV, faster payback ⚠️ RISK ANALYSIS OPTION A: Durham (LOWER RISK - 3.2/10) ✓ Supply Chain Resilience: Achieves 80% dual-sourcing strategic goal ✓ Quality Diversification: Durham has 45-day zero-defect streak ✓ Geographic diversity: NC vs. MI (reduces weather/labor risks) ✗ Execution Risk: New supplier in ramp-up phase OPTION B: Muskegon (HIGHER RISK - 6.8/10) ✓ Execution Certainty: Mature supplier (15+ years) ✓ Faster time-to-production: 8 vs. 12 months ✗ Concentration Risk: Single supplier for critical component = vulnerability ✗ Recent quality issue (Oct 2025 furnace calibration) demonstrates risk ✗ Strategic misalignment with 80% dual-sourcing goal RISK WINNER: Option A (Durham) - Better strategic risk profile 💡 RECOMMENDATION RECOMMENDED DECISION: Option A - Durham Secondary Supplier Acceleration RATIONALE: While Option B (Muskegon) offers superior financial returns ($12M NPV difference), Option A (Durham) is strategically superior: 1. RISK MITIGATION: Recent Muskegon quality issue demonstrates single-source vulnerability. Durham secondary source provides resilience. 2. STRATEGIC ALIGNMENT: Directly achieves 80% dual-sourcing goal and aligns with GE's supply chain transformation vision. 3. LONG-TERM VALUE: $12M NPV difference is small compared to risk mitigation value. A single major disruption costs $10M+/month. 4. COMPETITIVE DYNAMICS: Dual sourcing creates healthy competition, driving quality and cost improvements from both suppliers over time. HYBRID ALTERNATIVE (If Budget Allows): $35M investment: $25M Durham + $10M Muskegon process optimization • Achieves both goals: dual sourcing + immediate capacity boost • Best of both worlds with 40% additional budget

Real-Time Executive Discussion

  • CFO asks: "What if Durham ramp-up takes 18 months instead of 12? Does recommendation change?"
    Claude provides: Sensitivity analysis in 30 seconds showing break-even scenarios
  • VP Operations asks: "Show me the risk-adjusted return calculation."
    Claude generates: Monte Carlo simulation with risk-weighted NPV across 1,000 scenarios
  • CEO asks: "If we do Durham, what contingency plans do we need for LEAP delivery in the first 6 months?"
    Claude outlines: Bridge strategies including overtime at Muskegon, inventory buffer, tertiary source partial qualification
Decision Speed
90%+
Reduction: 2-3 weeks → 4 hours
Data Integration
Cross-Functional
Finance, Ops, Engineering, Supply Chain
Analysis Quality
Comprehensive
Financial + Risk + Strategic
Scenarios
1,000+
Monte Carlo simulation capability

Strategic Value: Better, Faster Capital Allocation

Enable executive team to evaluate 10x more strategic options, make risk-aware decisions with quantitative analysis, and accelerate $25M+ capital allocation decisions from weeks to hours—all while maintaining comprehensive analysis quality.

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