Aerospace Supply Chain

Aerospace Supply Chain

You are an expert in aerospace and defense supply chain management, aviation manufacturing operations, and MRO (Maintenance, Repair, Overhaul) logistics. Your goal is to help optimize complex multi-tier supply networks, manage long lead-time components, ensure regulatory compliance, and support both OEM production and aftermarket operations.

Initial Assessment

Before optimizing aerospace supply chains, understand:

1. Business Segment

• Industry sector? (commercial aviation, defense, space, business jets)
• OEM or supplier tier? (Tier 1, 2, 3 supplier vs. OEM)
• Product type? (airframes, engines, avionics, interiors, structures)
• New production vs. aftermarket vs. MRO?
• Government vs. commercial customers?

1. Product & Program Complexity

• Aircraft platforms served? (737, A320, F-35, etc.)
• Product lifecycle stage? (development, production, mature, legacy)
• Production rate? (aircraft per month, engines per year)
• Customization level? (standard, options, fully custom)
• Part count and BOM depth? (assemblies, components, raw materials)

1. Supply Chain Structure

• Number of suppliers by tier?
• Geographic footprint? (domestic, global, strategic regions)
• Sole source vs. multiple source?
• Vertical integration level?
• Outsourcing strategy? (make vs. buy)

1. Regulatory & Quality

• Quality standards? (AS9100, AS9110, AS9120, Nadcap)
• Certifications required? (FAA, EASA, military specs)
• Export controls? (ITAR, EAR, defense articles)
• Traceability requirements? (serialization, lot tracking)
• Counterfeit part prevention?

---

Aerospace Supply Chain Framework

Value Chain Structure

Aerospace Manufacturing Ecosystem:

Raw Materials (Titanium, Composites, Aluminum, Special Alloys)
  ↓
Tier 3 Suppliers (Basic parts, fasteners, raw stock)
  ↓
Tier 2 Suppliers (Components, sub-assemblies)
  ↓
Tier 1 Suppliers (Major systems, integrated assemblies)
  ↓
OEMs (Aircraft Manufacturers)
  ├─ Boeing, Airbus, Lockheed Martin, Northrop Grumman
  ├─ Embraer, Bombardier, Gulfstream
  └─ Pratt & Whitney, GE Aviation, Rolls-Royce (engines)
  ↓
Airlines / Military / Operators
  ↓
MRO Providers
  ↓
Parts Distributors / Aftermarket

Key Industry Players:

• OEMs: Boeing, Airbus, Lockheed Martin, Northrop Grumman, BAE Systems
• Tier 1: Spirit AeroSystems, Collins Aerospace, Safran, Honeywell
• Engines: GE Aviation, Pratt & Whitney, Rolls-Royce, CFM International
• MRO: Lufthansa Technik, ST Engineering, AAR Corp, StandardAero

---

Long Lead-Time Component Management

Critical Path Analysis & Planning

import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import networkx as nx

class AerospaceProgramPlanner:
    """
    Manage long lead-time aerospace program planning
    """

    def __init__(self, aircraft_program):
        """
        Initialize program planner

        Parameters:
        - aircraft_program: program details (platform, rate, start date)
        """
        self.program = aircraft_program

    def analyze_critical_path(self, bom_df, supplier_lead_times):
        """
        Analyze critical path for aircraft production

        Parameters:
        - bom_df: Bill of Materials with assembly tree
        - supplier_lead_times: lead times by part number

        Returns:
        - critical path analysis with longest lead time items
        """

        # Build assembly dependency graph
        G = nx.DiGraph()

        for idx, part in bom_df.iterrows():
            part_number = part['part_number']
            parent = part.get('parent_assembly', 'FINAL_ASSEMBLY')

            # Get lead time
            lead_time_weeks = supplier_lead_times.get(part_number, 52)

            G.add_node(part_number, lead_time=lead_time_weeks)
            G.add_edge(parent, part_number)

        # Calculate critical path
        critical_items = []

        for part in bom_df['part_number']:
            # Find longest path from this part to final assembly
            try:
                path_length = nx.shortest_path_length(
                    G, source=part, target='FINAL_ASSEMBLY'
                )

                # Cumulative lead time
                path = nx.shortest_path(G, source=part, target='FINAL_ASSEMBLY')
                cumulative_lt = sum(G.nodes[p].get('lead_time', 0) for p in path)

                critical_items.append({
                    'part_number': part,
                    'lead_time_weeks': G.nodes[part].get('lead_time', 0),
                    'levels_to_assembly': path_length,
                    'cumulative_lead_time_weeks': cumulative_lt,
                    'is_critical_path': cumulative_lt > 52  # >1 year
                })
            except:
                pass

        critical_analysis = pd.DataFrame(critical_items).sort_values(
            'cumulative_lead_time_weeks', ascending=False
        )

        return critical_analysis

    def calculate_procurement_schedule(self, production_schedule, critical_items):
        """
        Calculate when to order long lead-time components

        Parameters:
        - production_schedule: aircraft delivery schedule
        - critical_items: parts with cumulative lead times

        Returns:
        - procurement schedule by part
        """

        procurement_schedule = []

        for idx, aircraft in production_schedule.iterrows():
            delivery_date = aircraft['delivery_date']
            tail_number = aircraft['tail_number']
            quantity = aircraft.get('quantity', 1)

            for _, item in critical_items.iterrows():
                part_number = item['part_number']
                cumulative_lt_weeks = item['cumulative_lead_time_weeks']

                # Add safety buffer (typically 4-8 weeks)
                safety_buffer_weeks = 8

                total_lt_weeks = cumulative_lt_weeks + safety_buffer_weeks

                # Calculate order date
                order_date = delivery_date - timedelta(weeks=total_lt_weeks)

                procurement_schedule.append({
                    'tail_number': tail_number,
                    'part_number': part_number,
                    'delivery_date': delivery_date,
                    'order_date': order_date,
                    'lead_time_weeks': cumulative_lt_weeks,
                    'safety_buffer_weeks': safety_buffer_weeks,
                    'days_until_order': (order_date - datetime.now()).days,
                    'urgency': self._classify_urgency(
                        (order_date - datetime.now()).days
                    )
                })

        return pd.DataFrame(procurement_schedule).sort_values('order_date')

    def _classify_urgency(self, days_until_order):
        """Classify procurement urgency"""
        if days_until_order  0:
            # Rate increase - need more inventory
            transition_plan['inventory_actions'] = [
                'increase_safety_stock_by_25pct',
                'advance_long_lead_orders_by_4_weeks',
                'negotiate_supplier_capacity_increases',
                'pre_build_inventory_for_critical_parts',
                'dual_source_bottleneck_components'
            ]

            # Calculate inventory investment needed
            avg_part_value = 50000  # Placeholder
            parts_per_aircraft = 500000
            months_of_buffer = 2

            additional_inventory_value = (
                (target_rate - current_rate) * months_of_buffer *
                parts_per_aircraft * avg_part_value / parts_per_aircraft
            )

            transition_plan['inventory_investment_usd'] = additional_inventory_value

        else:
            # Rate decrease - reduce inventory
            transition_plan['inventory_actions'] = [
                'reduce_safety_stock_gradually',
                'defer_non_critical_orders',
                'negotiate_supplier_volume_reductions',
                'assess_excess_and_obsolete_risk',
                'slow_down_receipts_timing'
            ]

            # Calculate potential excess
            months_of_excess = abs(rate_change_pct) * 6

            excess_inventory_risk = (
                (current_rate - target_rate) * months_of_excess * 100000
            )

            transition_plan['excess_inventory_risk_usd'] = excess_inventory_risk

        return transition_plan

# Example usage
program = {'name': 'Narrow_Body_Program', 'platform': 'A320', 'rate_per_month': 50}

bom = pd.DataFrame({
    'part_number': ['WING_ASSY', 'FUSELAGE', 'ENGINE_1', 'LANDING_GEAR'],
    'parent_assembly': ['FINAL_ASSEMBLY', 'FINAL_ASSEMBLY', 'FINAL_ASSEMBLY', 'FINAL_ASSEMBLY']
})

lead_times = {
    'WING_ASSY': 78,  # 18 months
    'FUSELAGE': 52,   # 12 months
    'ENGINE_1': 104,  # 24 months
    'LANDING_GEAR': 65  # 15 months
}

planner = AerospaceProgramPlanner(program)
critical_path = planner.analyze_critical_path(bom, lead_times)

print("Critical Path Analysis:")
print(critical_path[['part_number', 'lead_time_weeks', 'cumulative_lead_time_weeks', 'is_critical_path']])

---

AS9100 Quality Management

Quality & Certification Management

class AerospaceQualityManager:
    """
    Manage AS9100 quality and aerospace certification requirements
    """

    def __init__(self):
        self.quality_standards = {
            'AS9100': 'Quality management for aviation/space/defense',
            'AS9110': 'Quality for MRO organizations',
            'AS9120': 'Quality for distributors/stockists',
            'Nadcap': 'Special process accreditation (heat treat, NDT, etc.)'
        }

    def assess_supplier_quality(self, supplier_details):
        """
        Assess supplier quality capabilities and certifications

        Parameters:
        - supplier_details: supplier info and certifications

        Returns:
        - quality assessment and approval status
        """

        required_certs = supplier_details.get('required_certifications', [])
        actual_certs = supplier_details.get('current_certifications', [])

        assessment = {
            'supplier': supplier_details['name'],
            'supplier_type': supplier_details.get('type', 'component'),
            'certifications_required': required_certs,
            'certifications_held': actual_certs,
            'gaps': list(set(required_certs) - set(actual_certs)),
            'quality_score': 0,
            'approved': False
        }

        # Check certifications
        if 'AS9100' in required_certs:
            if 'AS9100' in actual_certs:
                assessment['quality_score'] += 40
            else:
                assessment['gaps'].append('AS9100_required')

        # Check special process accreditations
        special_processes = supplier_details.get('special_processes', [])

        for process in special_processes:
            if process in ['heat_treat', 'welding', 'NDT', 'plating']:
                if 'Nadcap' in actual_certs:
                    assessment['quality_score'] += 20
                else:
                    assessment['gaps'].append(f'Nadcap_{process}')

        # Check performance history
        ppb_defects = supplier_details.get('ppb_defects', 0)  # Parts per billion

        if ppb_defects = 95:
            assessment['quality_score'] += 20
        elif otd_pct >= 90:
            assessment['quality_score'] += 10

        # Approval decision
        if assessment['quality_score'] >= 70 and len(assessment['gaps']) == 0:
            assessment['approved'] = True
            assessment['status'] = 'approved'
        elif assessment['quality_score'] >= 50:
            assessment['approved'] = False
            assessment['status'] = 'conditional_pending_improvements'
        else:
            assessment['approved'] = False
            assessment['status'] = 'not_approved'

        return assessment

    def manage_first_article_inspection(self, part_details):
        """
        Manage First Article Inspection (FAI) process per AS9102

        Parameters:
        - part_details: part specifications and requirements

        Returns:
        - FAI checklist and requirements
        """

        fai_requirements = {
            'part_number': part_details['part_number'],
            'drawing_revision': part_details['drawing_revision'],
            'supplier': part_details['supplier'],
            'inspection_required': True,
            'as9102_forms': ['Form 1', 'Form 2', 'Form 3'],
            'inspection_plan': []
        }

        # Form 1: Part number accountability
        fai_requirements['inspection_plan'].append({
            'form': 'AS9102_Form_1',
            'description': 'Part number accountability and traceability',
            'items': [
                'Verify part number matches drawing',
                'Verify drawing revision',
                'Document material certifications',
                'Record manufacturing lot/batch'
            ]
        })

        # Form 2: Product accountability
        fai_requirements['inspection_plan'].append({
            'form': 'AS9102_Form_2',
            'description': 'Product accountability',
            'items': [
                'List all characteristics from drawing',
                'Identify critical and key characteristics',
                'Document measurement methods',
                'Record actual measurements'
            ]
        })

        # Form 3: Characteristic accountability
        characteristic_count = part_details.get('characteristic_count', 50)

        fai_requirements['inspection_plan'].append({
            'form': 'AS9102_Form_3',
            'description': f'Characteristic verification ({characteristic_count} characteristics)',
            'items': [
                'Measure all dimensional characteristics',
                'Verify material properties',
                'Confirm surface finish requirements',
                'Validate special processes (heat treat, plating, etc.)',
                'Photograph critical features'
            ]
        })

        # Additional requirements for critical parts
        if part_details.get('flight_critical', False):
            fai_requirements['additional_requirements'] = [
                'Witness inspection by customer',
                'Non-destructive testing (NDT)',
                'Material test reports from mill',
                'Certificate of Conformance',
                'Special packaging requirements'
            ]

        return fai_requirements

    def track_quality_metrics(self, supplier_performance_data):
        """
        Track aerospace supplier quality metrics

        Parameters:
        - supplier_performance_data: delivery and quality records

        Returns:
        - quality scorecard
        """

        metrics = {}

        for supplier_id, data in supplier_performance_data.items():
            # Calculate PPB (parts per billion defects)
            total_parts = data['parts_delivered']
            defective_parts = data['parts_rejected']

            ppb = (defective_parts / total_parts * 1_000_000_000
                  if total_parts > 0 else 0)

            # OTD (on-time delivery)
            otd_pct = (data['on_time_deliveries'] / data['total_deliveries'] * 100
                      if data['total_deliveries'] > 0 else 0)

            # Corrective actions
            open_cars = data.get('open_corrective_actions', 0)

            # Overall score
            quality_score = (
                (100 - min(ppb / 10, 50)) * 0.4 +  # PPB component (max 50 pts)
                otd_pct * 0.4 +  # OTD component (max 40 pts)
                (10 if open_cars == 0 else 0) * 0.2  # CAR component (max 20 pts)
            )

            metrics[supplier_id] = {
                'ppb_defects': ppb,
                'otd_performance_pct': otd_pct,
                'open_corrective_actions': open_car

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [kishorkukreja](https://github.com/kishorkukreja)
- **Source:** [kishorkukreja/awesome-supply-chain](https://github.com/kishorkukreja/awesome-supply-chain)
- **License:** MIT

Install and usage instructions live in the source repository linked above.