Someone at your shop just typed 5.70 instead of 7.50. Parts got machined. Heat treated. Inspected. Then scrapped. $42k gone because someone clicked the wrong row in a dropdown.
This keeps happening. While direct aerospace-specific data is limited, parallels from construction—another complex, regulated industry—illustrate the broader risk. Autodesk and FMI found poor data costs large contractors around $165M annually, with $7.1 million in avoidable rework . Construction accounted for 13.2% of global GDP in 2020, with bad data potentially costing the industry $1.84 trillion .
The Autodesk/FMI study attributes 14% of construction rework to bad data .
While you're likely smaller than a $1B contractor, proportionally? The data quality challenge is just as real in aerospace and defense manufacturing.
Research on bad data impact across complex industries shows consistent patterns:
Rework and scrap (42% – proportional to project size): 347 bearing housings machined to the wrong tolerance. $840k scrapped, $520k rework, plus downstream costs.
Miscommunication (28% – proportional to project size): Customer wanted Ra 32 surface finish, drawing called for Ra 125. Parts rejected. Engineer: "I thought they'd be fine." Customer: "We specified Ra 32 in the quote package." $28k rework.
Schedule delays (18% – proportional to project size): Production paused 3 days waiting for engineering to clarify ambiguous GD&T. Expedite fees and customer penalties: $45k.
Documentation errors (8% – proportional to project size): AS9100 audit found 14 instances of obsolete work instructions still in use. Major finding, 60-day corrective action, consultant fees, and lost contracts during suspension: $95k.
Lost productivity (4% – proportional to project size): Engineers spending 20% of their time tracking down correct specifications rather than designing. Annual cost for a 10-engineer team: $160k in lost output.
Note: While these breakdown percentages derive from construction industry research, they illustrate the broader risk of poor data management in complex regulated industries such as aerospace and defense.
Stage 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: $150k+) Prevention is 1000× cheaper than correction.
Where Bad Data Comes From
Bad data enters aerospace manufacturing at multiple points. Here's what actually happens:Drawing Errors and Ambiguities (38%)
An F-16 maintenance drawing specified a hole at "0.250" with no tolerance callout. Machinist A read it as ±0.010", machinist B as ±0.005". Twenty-three parts scrapped due to inconsistency. Common issues:- Missing tolerances that default to "shop standard"—but which standard?
- Vague callouts like "smooth finish" that mean different things to different people
- Outdated revisions (parts made to Rev B when Rev C is current)
- Illegible scans missing critical details
- Copy-paste errors (dimension from Part A left on Part B)
Manual Data Entry Mistakes (22%)
A supplier entered "6061-T6" instead of "7075-T6" for a bracket. Material shipped, parts machined, heat treated—then QA caught it. $42k in scrap. The culprits:- Transposed digits (7.50 becomes 5.70)
- Unit confusion (inches vs. millimeters)
- Decimal errors (0.05 entered as 0.5)
- Wrong material selected from dropdown
- Excel copy-paste from the wrong cell
Incomplete Material Certifications (18%)
An aerospace supplier used aluminum bar stock without the mill cert. Parts passed internal QA. Customer audit found the gap—$80k in parts quarantined, contract put on hold. What goes wrong:- Material received without traceability documentation
- Partial certs (heat lot present, chemical composition missing)
- Expired certs used for new shipments
- Certificate specifies one material specification, delivered material matches a different specification
Miscommunication Between Departments (12%)
Customer verbally approved a substitution during a plant visit. Engineering didn't document it. Next shipment rejected—"This isn't what the drawing calls for." $35k in rework to "fix" perfectly good parts. The gaps:- "Use the updated drawing"—but which version?
- Inspection notes that don't reach the operator
- Deviation approvals that receiving doesn't know about
- Spec changes mentioned in email, never formalized
Legacy Data Systems (6%)
A shop migrated from SolidWorks to CATIA. Hundreds of legacy drawings lost their metadata. Engineers spent 600 hours manually reconstructing revision histories for audits.Other Issues (4%)
Uncalibrated tools, rounding errors, time zone confusion, translation mistakes with international suppliers.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 estimate: 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 review.
The Cascade Effect
Bad data doesn't stay contained. Here's what happened at one shop: Stage 1: Wrong material spec on drawing (cost to fix: $0)Stage 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: $150k+) Prevention is 1000× cheaper than correction.
How to Stop It
MLNavigator scans drawings during design review and catches bad data before it enters your manufacturing workflow. During the design review process, the system:- Extracts all dimensions, tolerances, materials, specs
- Cross-checks against AS9100 requirements, customer standards, industry specs, and your shop's history
- Flags issues in 2-5 seconds
- Provides specific corrections: "Bearing bores typically require ±0.0005" tolerance"
- "Customer X always wants Ra 32, not Ra 125—flag this customer preference in the system"
- "Material spec 7075-T6 required here, not 6061"
- "This tolerance was too loose last time, tighten it to ±0.0005""
The Numbers
Mid-sized MRO running $10M revenue with 20% Cost of Poor Quality:- $2M annual CoPQ
- $840k from rework/scrap (42%)
- $560k from miscommunication (28%)
- Potential to avoid $235k in rework/scrap
- Potential to avoid $157k in miscommunication
- Target annual savings: $392k
Manual vs. Automated
| Metric | Manual Review | MLNavigator | |---|---|---| | Scan time | 20-45 minutes (if done thoroughly) | 2-5 seconds | | Consistency | Varies by reviewer | Uniform learned standards | | Coverage | Selective (time constraints) | 100% of drawings | | Learning | Static | Weekly adapter updates | | Audit trail | None (mental process) | Immutable logs | | Cost per drawing | $20-$60 (engineer time) | ~$0.50 (amortized) | Manual review is slow, inconsistent, incomplete, and expensive. Engineers reviewing drawings for 20-45 minutes each can't maintain that discipline when processing 50-200 drawings per month. Corners get cut. Errors slip through. Costs compound.Try It
Schedule a demo with 10 sample drawings. See what the system catches. If it flags errors that would have cost you money—keep using it. If not—you know. ASQ's research shows aerospace firms often report double-digit Cost of Poor Quality as a percentage of revenue , with data errors as a leading cause. The question isn't whether you have a data quality problem. If you're in aerospace, you do. The question is whether you'll fix it before it bleeds you dry.MLNavigator Begins Pilot Programs in 2026
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