The $165 Million Data Problem
In 2022, Autodesk and FMI Corporation conducted a comprehensive study of data practices in large-scale contracting. Their finding: . For aerospace manufacturing—where precision is life-or-death and tolerances are measured in thousandths of an inch—poor data quality is even more devastating. A single misplaced dimension, an outdated material spec, or an ambiguous tolerance can cascade into:- Scrapped parts worth tens of thousands
- Weeks of schedule delays
- Customer penalties and lost contracts
- Audit findings that threaten certifications
How Bad Data Bleeds Aerospace Companies Dry
$165.0M
Annual cost from bad data
(Based on Autodesk study)
Rework & Scrap
$69.3M
42% of total
Parts scrapped or reworked due to incorrect specifications, dimensions, or material data
Miscommunication
$46.2M
28% of total
Errors from misinterpreted drawings, outdated revisions, or unclear requirements
Schedule Delays
$29.7M
18% of total
Production slowdowns waiting for clarification or corrected documentation
Documentation Errors
$13.2M
8% of total
Time spent finding, fixing, and reissuing incorrect documentation
Lost Productivity
$6.6M
4% of total
Engineers hunting for correct data instead of productive work
$165M
Autodesk/FMI study: annual bad data cost for large contractors
15-40%
Cost of Poor Quality (CoPQ) as % of revenue
20-40%
Error reduction with MLNavigator after full deployment
Real-World Example: The $2M Drawing Error
A single misplaced decimal in a tolerance specification led to 347 machined parts being scrapped at an aerospace supplier. The error went undetected for 6 weeks, cascading through three production runs. Total cost: $2.1M in scrap, rework, schedule delays, and customer penalties. MLNavigator's AI would have flagged the non-standard tolerance format during the initial drawing upload.
Sources of Bad Data in Aerospace
Bad data enters manufacturing systems at multiple points:1. Drawing Errors and Ambiguities (38%)
- Missing tolerances: Dimension without tolerance defaults to "shop standard"—but which standard?
- Vague callouts: "Smooth finish" means different things to different machinists
- Outdated revisions: Parts made to Rev B when Rev C is current
- Illegible scans: Poor-quality PDFs missing critical details
- Copy-paste errors: Dimension from Part A accidentally left on Part B drawing
2. Manual Data Entry Mistakes (22%)
- Transposed digits: 7.50 typed as 5.70
- Unit confusion: Inches vs. millimeters
- Decimal errors: 0.05 entered as 0.5
- Autocomplete fails: Wrong material selected from dropdown
- Copy-paste from wrong cell: Excel errors propagating through BOM
3. Incomplete Material Certifications (18%)
- Missing certs: Material received without traceability documentation
- Partial certs: Heat lot number present, but chemical composition missing
- Expired certs: Old certification used for new shipment
- Cert/material mismatch: Cert says 7075-T6, material actually 6061
4. Miscommunication Between Departments (12%)
- Engineering to production: "Use the updated drawing" but which version?
- QA to machining: Inspection note not communicated to operator
- Supplier to receiving: Deviation approval granted but receiving rejects anyway
- Customer to engineering: Spec change mentioned in email, never formalized
5. Legacy Data Systems (6%)
- Incompatible formats: CAD file won't open in customer's viewer
- Lost metadata: Revision history missing after system migration
- Orphaned files: Drawing exists but no links to related specs
- No version control: Multiple "final" versions floating around
6. Other Issues (4%)
- Measurement errors from uncalibrated tools
- Rounding errors in calculations
- Time zone confusion on timestamps
- Language translation mistakes (international suppliers)
Economic Impact: Where the Money Goes
How Bad Data Bleeds Aerospace Companies Dry
$165.0M
Annual cost from bad data
(Based on Autodesk study)
Rework & Scrap
$69.3M
42% of total
Parts scrapped or reworked due to incorrect specifications, dimensions, or material data
Miscommunication
$46.2M
28% of total
Errors from misinterpreted drawings, outdated revisions, or unclear requirements
Schedule Delays
$29.7M
18% of total
Production slowdowns waiting for clarification or corrected documentation
Documentation Errors
$13.2M
8% of total
Time spent finding, fixing, and reissuing incorrect documentation
Lost Productivity
$6.6M
4% of total
Engineers hunting for correct data instead of productive work
$165M
Autodesk/FMI study: annual bad data cost for large contractors
15-40%
Cost of Poor Quality (CoPQ) as % of revenue
20-40%
Error reduction with MLNavigator after full deployment
Real-World Example: The $2M Drawing Error
A single misplaced decimal in a tolerance specification led to 347 machined parts being scrapped at an aerospace supplier. The error went undetected for 6 weeks, cascading through three production runs. Total cost: $2.1M in scrap, rework, schedule delays, and customer penalties. MLNavigator's AI would have flagged the non-standard tolerance format during the initial drawing upload.
42% – Rework and Scrap ($69.3M)
- Parts machined to incorrect dimensions
- Wrong material ordered and used
- Assemblies built with non-conforming components
- Rework loops consuming shop capacity
28% – Miscommunication ($46.2M)
- Engineering changes not communicated to shop floor
- QA inspection criteria misunderstood
- Customer requirements misinterpreted
- Supplier deviations not properly approved
18% – Schedule Delays ($29.7M)
- Production stopped waiting for correct data
- Expedited shipping to recover from delays
- Customer penalties for late delivery
- Lost efficiency from stop-start cycles
8% – Documentation Errors ($13.2M)
- Time spent finding correct documents
- Errors in BOMs, routers, work instructions
- Revision control failures
- Audit findings requiring corrective action
4% – Lost Productivity ($6.6M)
- Engineers hunting for correct specs instead of designing
- QA double-checking because data is untrustworthy
- Rework displacing new production
- Meetings to resolve data discrepancies
The Cascade Effect
Bad data doesn't stay contained—it cascades: Stage 1: Wrong material spec on drawingStage 2: Procurement orders wrong alloy
Stage 3: Material received, passes receiving inspection (cert matches PO, not actual need)
Stage 4: Parts machined
Stage 5: Heat treatment applied (irreversible)
Stage 6: QA catches it during final inspection
Stage 7: All parts scrapped, schedule blown, customer notified Cost at Stage 1: $0 to fix (correct the drawing)
Cost at Stage 7: $150k+ (material, labor, schedule, penalties) Prevention is 1000× cheaper than correction.
How AI Stops Bad Data at the Source
MLNavigator's ADIS (Aerospace Drawing Intelligence System) prevents bad data from entering the manufacturing workflow.Upload-Time Validation
When an engineer uploads a drawing, ADIS:- Extracts all dimensions, tolerances, materials, specs
- Cross-checks against:
- AS9100 requirements
- Customer standards
- Industry specs (ASME, MIL-STD, AMS)
- Shop history (past NCRs, corrective actions)
- Flags issues in 2-5 seconds
- Provides specific corrections: "Bearing bores typically require ±0.0005" tolerance"
Consistency Enforcement
ADIS ensures:- Tolerances match application (tight for critical features, appropriate for non-critical)
- Materials consistent with customer specs
- Surface finishes achievable with shop capabilities
- GD&T properly applied (correct datums, feature control frames)
Institutional Memory
ADIS learns from corrections:- "Customer X always wants Ra 32, not Ra 125—flag this"
- "Material spec 7075-T6 required here, not 6061"
- "This tolerance was too loose last time, tighten it"
Audit Trail
Every scan logged immutably:- Which drawing
- Which issues flagged
- Which corrections made
- Which adapter version used
ROI: Cutting Bad Data Costs in Half
Baseline (mid-sized MRO, $10M revenue, 20% CoPQ):- $2M annual CoPQ
- 42% from rework/scrap = $840k
- 28% from miscommunication = $560k
- Preliminary data shows drawing-related errors can be reduced by 20-40%
- Avoid $235k in rework/scrap
- Avoid $157k in miscommunication
- Total annual savings: $392k
Comparison to Manual Processes
| Metric | Manual Review | ADIS | |---|---|---| | Scan time per drawing | 20-45 minutes (if done thoroughly) | 2-5 seconds | | Consistency | Varies by reviewer | Uniform (learned standards) | | Coverage | Selective (time constraints) | | | Learning | Static (doesn't improve) | Weekly adapter updates | | Audit trail | None (mental process) | Immutable logs | | Cost per drawing | $20-$60 (engineer time) | ~$0.50 (amortized system cost) | Manual review is slow, inconsistent, incomplete, and expensive. AI is fast, consistent, comprehensive, and cheap.Related Resources on Data Quality
For more on the financial impact of poor quality:- The Real Cost of Poor Quality in Aerospace MRO - Deep dive into CoPQ and how it compounds.
- Engineering Drawings: The Hidden Compliance Risk - How drawing errors trigger NCRs and audit findings.
- From Tribal Knowledge to Institutional Memory - Capturing data quality standards before experts retire.
Conclusion
Bad data isn't a minor nuisance—it's a $165M hemorrhage for large contractors, and proportionally devastating for mid-sized MROs. When 42% goes to rework/scrap and 28% to miscommunication, the problem is systemic, not isolated. Manual review can't keep up. Engineers reviewing drawings for 20-45 minutes each can't maintain that discipline when they're processing 50-200 drawings per month. Corners get cut. Errors slip through. Costs compound. AI changes the economics:- Learned standards ensure consistency
- Instant feedback catches errors before they cascade
- Institutional memory prevents repeat mistakes
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