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Tag: First Article Inspection (FAI)

AS9102 FAI Triggers: New Parts, Changes, Lapses, and Delta Requirements
In aerospace manufacturing, one of the most common quality questions is not what AS9102 first article inspection is, but when it is actually required. Teams know first article inspection matters. They know customers expect a compliant FAIR. What causes real friction is deciding whether a situation calls for a full FAI, a partial FAI, or no new FAI at all.

That decision matters because unnecessary first article work slows production, ties up quality resources, and adds documentation overhead. On the other hand, missing a valid trigger can create customer escapes, audit findings, approval delays, and serious traceability problems. In aerospace, where configuration control and product conformity carry real operational and regulatory weight, getting this right is not optional.

This article explains the most important AS9102 FAI triggers, including new part introduction, engineering changes, process changes, production lapses, and the circumstances that justify a partial or delta FAI rather than a full reset. It also looks at how aerospace manufacturers can manage these triggers more consistently using connected digital workflows.

If you want the broader foundation first, review AS9102 Software: Digital First Article Inspection for Aerospace Manufacturing.

What AS9102 FAI Is Designed to Prove

AS9102 first article inspection is a structured method for verifying that a production process can manufacture a part or assembly that fully conforms to engineering, specification, and purchase order requirements at the released configuration. It is not just a sample inspection. It is not a one-time paperwork exercise. It is a formal record that shows the part definition was interpreted correctly, the process was executed properly, and the evidence of conformity is complete and traceable.

In practice, an FAI helps answer a straightforward but high-stakes question:

Can this exact aerospace production process, at this exact released configuration, produce conforming hardware with full documented accountability?

That is why FAI sits so close to configuration control, traceability, launch readiness, supplier quality, and customer approval. It creates a documented baseline that can later support change management, resubmissions, investigations, and audits.

Why Knowing the Right Trigger Matters

Plenty of aerospace organizations understand how to complete Form 1, Form 2, and Form 3. Fewer have a disciplined internal method for deciding when a new or updated FAI is required. That is where problems begin.

If the trigger logic is weak, teams end up doing one of two things. They either over-trigger, which creates waste and slows down manufacturing, or they under-trigger, which creates risk. Neither outcome is good. The first hurts efficiency. The second hurts compliance, customer trust, and sometimes product integrity.

A clear trigger model helps quality and manufacturing teams:
- Apply AS9102 consistently across programs and part families
- Reduce unnecessary full FAIR rebuilds
- Identify when partial or delta FAI is appropriate
- Align change control with customer and contract expectations
- Protect traceability when production conditions shift
Here’s the thing. The cost of poor trigger discipline is rarely visible all at once. It shows up as late package corrections, missing evidence, confused resubmissions, duplicated work, and uncomfortable customer conversations.

New Part Introduction Is the Most Obvious FAI Trigger

The clearest AS9102 trigger is the first production run of a new part number or assembly. When an aerospace organization introduces a part into production for the first time, it needs objective evidence that the released design can be built and verified correctly using the intended production process.

This usually calls for a full FAI because there is no prior approved baseline to rely on.

What counts as a new part introduction

New part introduction typically includes:
- A newly released part number entering production for the first time
- A new assembly requiring first-time product accountability
- A part transferred from development or prototype status into controlled production
- A customer program launch where the released configuration has not yet been formally validated
In these cases, the FAIR establishes the first documented baseline for the product. That baseline matters later when changes occur, because it gives the organization something traceable to compare against.

Why aerospace treats this carefully

In aerospace, new part introduction is not just about proving that one part measured correctly on one day. It is about proving that the released configuration, manufacturing route, inspection method, material traceability, and special process chain all support conformity. That is why the first baseline FAIR often becomes an anchor record for the life of the part.

Design Changes Often Trigger Full or Partial FAI Activity

Engineering changes are one of the most common reasons organizations revisit FAI. Not every revision change means the entire FAIR must be rebuilt, but changes that affect requirements, form, fit, function, interfaces, or inspection criteria often require at least a partial or delta FAI.

Examples of design changes that may trigger FAI
- Dimensional changes to a feature on the drawing
- Tolerance changes on an existing characteristic
- Material specification changes
- Updated notes affecting finish, marking, or identification
- Changes to critical, key, or safety-related characteristics
- Revision changes affecting mating or installation conditions
The real question is not simply whether the drawing revision changed. The better question is whether the released product definition changed in a way that affects conformity or verification. If it did, the FAI baseline likely needs to be updated.

When a design change justifies a partial or delta FAI

If the change affects only certain characteristics rather than the entire part, a partial or delta FAI is often the right choice. That allows the organization to revalidate only the impacted features while preserving the unaffected baseline from the original FAIR.

This approach is especially valuable in aerospace because programs often evolve slowly through controlled revisions, and rebuilding a full FAIR every time can become needlessly expensive. Still, that efficiency only works if the company has strong revision control and can clearly identify which characteristics were affected.

Process Changes Can Trigger FAI Even When the Drawing Stays the Same

One of the biggest mistakes organizations make is assuming that if the drawing did not change, the FAIR does not need attention. In aerospace manufacturing, process changes matter because the product may be the same on paper while the route used to build it has changed in a meaningful way.

If the process changes in a way that could affect part conformity, a new or updated FAI may be required.

Common process-related FAI triggers
- New manufacturing equipment or machine replacement
- New tooling, fixtures, or program changes
- Method changes in machining, forming, assembly, or inspection
- Changes to sequence of operations that affect product outcome
- Transfer of work between facilities or production cells
- Changes in outside processing sources for controlled operations
What this really means is that aerospace FAI is not only about the part definition. It is also about the process definition behind that part. If the way the part is made changes enough to alter risk, the FAIR logic needs to catch up.

Why process changes matter so much in aerospace

Aerospace production often involves tight tolerances, special processes, controlled materials, complex routings, and customer-specific source requirements. A machine swap, tooling update, supplier change, or move to a different facility can alter process behavior even if the part number and drawing revision remain identical. That is why smart trigger discipline looks at more than engineering release history.

Material and Special Process Changes Require Careful Review

In aerospace, traceability to material and special process evidence is central to FAI integrity. Form 2 exists for a reason. If the source or nature of the controlled inputs changes, organizations need to evaluate whether a new or updated FAI is required.

Typical material and source changes that may trigger FAI
- A new supplier for a controlled alloy or raw material
- A change in material specification or condition
- A new special process source for plating, heat treatment, NDT, coating, or similar operations
- A change in approval status or scope of a special process provider
- A change in process parameters that affects product characteristics
Some of these may require only partial FAI activity. Others may justify a broader review, depending on the criticality of the change and the customer’s expectations. Either way, they should never be treated as invisible background changes. In aerospace, they are often part of the conformity story.

Production Lapses Are a Real Aerospace Trigger

Aerospace manufacturing does not always run at a steady cadence. Many parts are made intermittently. Some programs have long pauses. Some part numbers may go quiet for months or years before restarting. That makes production lapse one of the most important and most overlooked FAI triggers.

If production has been dormant long enough, organizations may need to review whether the baseline process can still be trusted without refreshed validation.

Why lapse-based triggers exist

A long production gap can introduce risk even when the part and process documentation appear unchanged. During the lapse, a lot may have shifted:
- Operators may have changed
- Tooling may have worn or been replaced
- Programs may have been updated
- Equipment may have been serviced or relocated
- Suppliers may have changed
- Inspection methods may have evolved
That is why production lapse should be treated as a process risk issue, not just a scheduling detail.

How lapse thresholds are handled

Many organizations use internal thresholds, customer requirements, or contract-specific rules to define what counts as a significant lapse. A common reference point is two years, but the right answer always depends on the customer, the product, and the organization’s quality system. The main point is that lapse-based trigger logic should be defined clearly and applied consistently.

Full FAI vs Partial FAI vs Delta FAI

One reason AS9102 remains practical in real aerospace operations is that it does not force a full restart every time something changes. Instead, it allows manufacturers to scale the response to the actual scope of impact.

When a full FAI is usually appropriate
- First production of a new part number or assembly
- Major design change affecting broad portions of the part definition
- Major process change with wide conformity impact
- No reliable baseline FAIR exists
- Customer or contract explicitly requires a complete new FAIR
When a partial or delta FAI is often the better choice
- Only selected characteristics changed
- A limited process change affected a defined subset of features
- Material or source changes affected traceability but not the full configuration
- The baseline FAIR remains valid for unaffected requirements
The discipline here is simple to say but harder to execute: revalidate what changed, preserve what did not, and document the logic clearly.

Why organizations struggle with delta FAI

Delta FAI sounds efficient, and it is, but only when the underlying data is structured well enough to support it. If characteristics are trapped in static spreadsheets, traceability is fragmented, or revision history is unclear, teams often end up redoing far more than necessary. In those environments, delta FAI becomes confusing because nobody can cleanly separate affected from unaffected requirements.

Customer-Specific Requirements Still Matter

AS9102 gives aerospace manufacturers a standard framework, but it does not erase customer-specific expectations. Many primes and upper-tier suppliers apply additional rules around when FAI is required, what counts as a significant change, how lapse thresholds are handled, and what submission format is acceptable.

That means the right internal question is never only:

What does the standard allow?

It also needs to be:

What did the customer contract, purchase order, or program requirement actually ask for?

This matters because a technically defensible partial FAI may still be rejected if the customer expects a full resubmission package, specific portal workflow, or extra supporting documentation.

Common Mistakes Aerospace Teams Make with FAI Triggers

Most FAI trigger failures come from poor process visibility rather than bad intent. Teams are busy, systems are disconnected, and changes are sometimes managed in silos.

Typical mistakes include
- Treating revision changes as administrative without checking affected characteristics
- Ignoring process changes because the drawing stayed the same
- Missing lapse-based triggers on low-volume or intermittent parts
- Failing to assess source changes for material or special processes
- Overusing full FAI because delta logic is too hard to manage manually
- Assuming one customer’s interpretation applies to every program
The result is usually one of two ugly outcomes. Either the organization creates a lot of unnecessary quality work, or it ships with weaker evidence than the customer expects. Neither is a good place to be.

How Digital Systems Make FAI Trigger Decisions Easier

Digital FAI platforms are at their best when they do more than produce forms. They should help aerospace manufacturers manage trigger logic as part of a connected quality and manufacturing workflow.

What a strong digital workflow can do
- Maintain a traceable baseline FAIR by part number and revision
- Track changes to characteristics, materials, and process routes
- Highlight which features were affected by a revision or process update
- Support partial or delta FAI generation without recreating everything
- Connect Form 1, Form 2, Form 3, ballooned drawings, and certifications in one record set
- Preserve audit history around why a given trigger decision was made
That last point matters more than people think. In aerospace, it is not enough to make the right trigger decision. You often need to show later why that decision was reasonable.

Why this matters for Connect981-style operations

Connected platforms are especially useful in regulated manufacturing because they reduce the gap between engineering changes, manufacturing process shifts, and quality documentation. Instead of waiting for someone to notice a trigger manually, the system can support earlier visibility into what changed and what evidence may need to be refreshed.

That does not replace engineering judgment. It makes that judgment more consistent, more traceable, and less dependent on memory.

How to Build a Better Internal FAI Trigger Policy

Every aerospace manufacturer should define a practical internal trigger policy that aligns with AS9102, customer requirements, and real production conditions. The best policies are not vague. They are specific enough that quality, manufacturing, and engineering teams can use them without guesswork.

A strong internal policy should define
- What counts as a new part or first production run
- What kinds of design changes trigger full, partial, or delta FAI
- What kinds of process changes require review
- How material and special process source changes are evaluated
- What lapse threshold applies by default
- How customer-specific rules override standard internal logic
- Who has authority to approve the trigger decision
- How that decision is documented for future audit or customer review
Without this, organizations tend to rely too heavily on tribal knowledge. That works until the key person is out, the program changes hands, or the customer starts asking harder questions.

Final Takeaway

AS9102 FAI triggers are not just a compliance detail. They are part of how aerospace manufacturers control change, preserve traceability, and protect confidence in the production process. New parts, engineering changes, process shifts, material source changes, and production lapses can all justify a new or updated FAIR. The real challenge is knowing when a full FAI is necessary and when a partial or delta FAI is the smarter, defensible path.

The organizations that handle this well do not treat FAI as a last-minute quality document. They treat it as part of a connected operational system that links engineering, production, inspection, and customer requirements. That is where the real efficiency shows up, and it is also where the strongest compliance posture comes from.

To go deeper into digital workflows, FAIR structure, and connected aerospace quality execution, read AS9102 Software: Digital First Article Inspection for Aerospace Manufacturing.
March 10, 2026
Building the Business Case and Measuring ROI for Digital AS9102 FAI
Building the Business Case and Measuring ROI for Digital AS9102 FAI

Aerospace manufacturers and suppliers rarely question whether they must comply with AS9102. The real question is whether they can continue to absorb the time, risk, and opportunity cost of manual first article inspection (FAI) processes.

Digital AS9102 software promises shorter cycle times, fewer errors, and cleaner audits. To win budget and executive support, those promises must be translated into a clear, defensible business case with tangible financial impact.

This guide provides a structured way to quantify the return on investment (ROI) of AS9102 software. You will learn which metrics to track, how to model savings, and how to align your case with quality, operations, and IT stakeholders. For a broader view of capabilities and architecture, see our unified AS9102 and digital FAI platform overview.

Why AS9102 FAI Is a High-Leverage Improvement Area

FAI sits at the intersection of engineering, quality, operations, and customer delivery. That makes it one of the highest-leverage processes to digitize: small improvements compound across programs, plants, and suppliers.

Engineering time consumed by manual FAIRs

In many aerospace organizations, quality and manufacturing engineers spend a surprising portion of their time on manual first article inspection reports (FAIRs):
- Hand-ballooning multi-sheet drawings with hundreds of characteristics
- Copying data into spreadsheet-based Forms 1, 2, and 3
- Chasing material certs, special process documentation, and signatures
- Reworking rejected FAIRs from customers or internal approvers
For complex parts, a single full FAIR frequently consumes 8–24 hours of engineering time. If you produce dozens or hundreds of FAIRs per year, the labor cost and capacity impact become substantial.

Impact of FAI delays on deliveries and cash flow

FAI is often on the critical path for first deliveries and engineering changes. When FAIRs run late or are rejected, the impact includes:
- Delayed shipment of first production lots
- Slippage in new program milestones and entry into service
- Deferred revenue recognition and slower cash collection
- Premium freight or overtime to recover schedule
Even if the direct cost of an engineer’s time appears modest, the downstream schedule risk can be far more expensive. A credible business case should connect FAI performance to late deliveries and the cost of schedule recovery.

Hidden costs of rejections and audit findings

Manual FAIRs are error-prone. Common issues include missed or duplicated balloons, mismatched drawing revisions, incomplete Form 2 documentation, and incorrect tolerance interpretation. These lead to:
- Customer rejections and resubmissions
- Internal quality holds while documentation is corrected
- AS9100 or customer audit findings tied to FAI and traceability
- Reduced customer confidence and increased oversight
Digital AS9102 software cannot eliminate all issues, but it can dramatically reduce documentation-related nonconformances. That reduction is a core component of AS9102 software ROI.

Key Metrics for Evaluating AS9102 Performance

Before you model ROI, you need a baseline. The following metrics create a clear, quantifiable picture of current performance and provide a way to measure improvement after implementing digital FAI.

Average time to complete full and delta FAIRs

Track both engineering and overall calendar time:
- Engineering effort per FAIR (hours of quality/manufacturing engineering)
- Elapsed cycle time from trigger to approved FAIR (days)
- Separate values for full vs. delta FAI
Suggested approach:
1. Select a representative sample (e.g., last 20–50 FAIRs across key programs).
2. Capture effort from time sheets, issue trackers, or quick engineer estimates.
3. Record dates for FAI trigger, initial submission, rejection (if any), and final approval.
This baseline is essential for modeling time savings from automated ballooning, form population, and streamlined approvals.

FAIR rejection and rework rates

Next, examine quality and completeness of your FAIRs:
- Percentage of FAIRs rejected by customers or internal approvers
- Average number of resubmission cycles per FAIR
- Primary reasons for rejection (documentation vs. product nonconformance)
Digital FAI primarily affects documentation-related rejections: missed characteristics, mismatched revisions, missing certs, or inconsistent use of AS9102 Forms 1, 2, and 3. Categorizing issues lets you estimate how much rework digital tools can realistically reduce.

Late deliveries attributable to FAI

Where possible, identify how often FAI is a direct or contributing cause of late delivery:
- Number of shipments delayed due to incomplete or rejected FAIRs
- Average days of delay when FAI is the primary cause
- Estimated cost of delay (expedite costs, penalties, or internal recovery spend)
This data is often spread across ERP notes, program reviews, and customer complaints. Even directional estimates (e.g., 5–10 orders per year delayed primarily due to FAIRs) can materially strengthen your business case.

Estimating the ROI of AS9102 Software

Once you have baseline metrics, you can model ROI using a combination of time savings, quality risk reduction, and audit efficiency. Avoid promising a single precise number; instead, build a conservative, realistic range.

Modeling time savings per FAIR and annual volume

A practical way to estimate time savings is to compare current effort with benchmark reductions from digital FAI:
- Manual baseline: 8–24 engineering hours per full FAIR is common for complex aerospace parts.
- Digital target range: Many organizations see full FAIR effort drop to 2–6 hours once ballooning and form population are automated.
Define variables for your model, for example:
- H_manual_full = average manual hours per full FAIR
- H_digital_full = expected digital hours per full FAIR
- H_manual_delta = average manual hours per delta FAIR
- H_digital_delta = expected digital hours per delta FAIR
- N_full = number of full FAIRs per year
- N_delta = number of delta FAIRs per year
- Blended_rate = loaded hourly cost for engineers (salary + benefits + overhead)
Then compute annual labor savings:
```
Labor_savings = ((H_manual_full - H_digital_full) × N_full
                 + (H_manual_delta - H_digital_delta) × N_delta)
                 × Blended_rate
```
For example, if you save 6 hours on 150 FAIRs per year at a blended rate of $80/hour, labor savings alone is roughly $72,000 annually.

Quantifying reduction in customer escapes and audit issues

Digital AS9102 software helps enforce complete characteristic accountability, correct use of Rev C forms, and traceability to materials and special processes. The impact shows up as:
- Fewer documentation-related customer rejections
- Reduced nonconformance costs linked to incorrect or missing FAIRs
- Lower likelihood of audit findings tied to FAI records
To model this conservatively:
1. Estimate your current annual cost of documentation-related FAI issues (extra engineering time, expedited shipments, minor concessions, or penalties).
2. Apply an improvement factor based on target rejection reduction (e.g., 20–40% reduction in documentation-related rejections).
3. Include the value of avoided audit findings (e.g., time for root cause analysis, corrective actions, follow-up audits).
The formula can be framed as:
```
Quality_savings = (Baseline_FAIR_issue_cost × Expected_reduction_percentage)
```
Because these numbers can be sensitive, use ranges and anonymized examples rather than suggesting guaranteed outcomes.

Factoring in training and implementation costs

To present a credible ROI, you must subtract real implementation costs:
- Software license and subscription: annual or multi-year
- Implementation services: configuration, integrations, data migration
- Training time: hours spent by engineers and inspectors learning the system
- Change management: internal project management and process updates
Define total annualized cost:
```
Total_cost = Software_fees + (Implementation_services / Amortization_years)
             + Training_cost + Internal_project_cost
```
Then calculate ROI over a chosen period (often 3 years):
```
Annual_benefit = Labor_savings + Quality_savings + Audit_savings + Schedule_risk_impact
ROI = (Annual_benefit - Total_cost) / Total_cost
Payback_period = Total_cost / Annual_benefit
```
Present ROI as a range (e.g., 40–120% over three years) to reflect uncertainty in assumptions and acknowledge that actual results depend on baseline maturity and implementation quality.

Example Before-and-After Scenarios

Different organizations start from different levels of digital maturity. The business case for AS9102 software looks a bit different if you are transitioning from paper and spreadsheets versus upgrading from a stand-alone ballooning tool.

Manual spreadsheets vs. stand-alone ballooning tools

For teams working primarily with paper drawings and Excel forms, moving to a stand-alone FAI tool typically delivers:
- Time savings: Auto-ballooning and automatic Form 3 population can cut FAIR creation time by more than half.
- Error reduction: Fewer missed characteristics and better revision control.
- Documentation consistency: Standard templates aligned with AS9102 Rev C.
A high-level scenario:
- Baseline: 10 hours per full FAIR; 100 FAIRs/year.
- After stand-alone tool: 4–5 hours per full FAIR.
- Labor savings: 5–6 hours × 100 FAIRs × blended rate.
This is often the first step for single-site suppliers with limited integration needs.

Stand-alone tools vs. integrated operations platforms

Organizations already using point tools may still struggle with disconnected data and duplicate work across ERP, MES, PLM, and QMS. Moving to an integrated aerospace operations platform that embeds AS9102 FAI alongside work instructions and in-process inspections can add:
- Reduced re-keying of part, revision, and routing data
- Direct import of CMM and measurement data into Form 3
- Unified workflows for FAIR approvals, nonconformance management, and change control
- Analytics spanning FAI, in-process, and final inspection data
Here, savings come not just from FAI creation time, but also from fewer discrepancies between systems, faster approvals, and better reuse of data for audits and continuous improvement.

Multi-site standardization and supplier collaboration gains

For OEMs and large Tier 1 suppliers, the largest ROI often appears when standardizing AS9102 processes across plants and suppliers:
- Common FAIR templates and workflows across internal sites
- Supplier portals for submitting FAIRs in a consistent structure
- Global visibility into FAI status across programs and tiers
- Centralized management of prime- or customer-specific AS9102 requirements
Benefits include:
- Higher throughput per engineer because tools, templates, and expectations are standardized.
- Lower training overhead for transfers and new hires.
- Better supplier performance through clear expectations and shared data.
- Improved audit readiness at both central and site levels.
While this level of deployment takes more upfront investment, multi-site standardization typically yields compounding benefits over time.

Aligning the Business Case with Stakeholder Priorities

A strong AS9102 software business case speaks different languages to different stakeholders. The core ROI model may be the same, but emphasis and messaging should vary.

Quality and compliance leadership perspectives

Quality leaders and compliance managers focus on:
- AS9102 Rev C conformity and correct use of Forms 1, 2, and 3
- Reduction in customer rejections and concessions due to FAIR issues
- Audit readiness for AS9100, customer, and regulatory reviews
- Traceability from ballooned drawing to measurement, material, and process records
For this audience, highlight:
- Lower documentation-related nonconformance rates
- Faster and more confident responses during audits
- Reusable FAI data for trend analysis and corrective actions
Operations and program management concerns

Operations, plant managers, and program leaders care about:
- On-time delivery and schedule adherence
- Engineering capacity to support new product introduction and changes
- Impact of FAI bottlenecks on throughput and WIP
- Cost of recovery when FAI issues delay shipments
For them, emphasize:
- Reduced cycle time for FAIR approvals
- Fewer late deliveries attributable to FAI
- More engineering hours available for process improvement and problem-solving
- Clear dashboards showing FAI status across programs
IT and digital transformation alignment

IT and digital transformation teams look for:
- Alignment with the broader digital thread and data strategy
- Integration with ERP, MES, PLM, and QMS
- Security, access control, and audit logging
- Scalability across sites and suppliers
Key talking points include:
- Standards-based data structures for AS9102 FAIRs
- Available APIs or connectors to existing systems
- Cloud and on-premises deployment options, as applicable
- Support for future capabilities like model-based definition and AI-assisted analytics
Position digital FAI as a building block in a broader smart manufacturing roadmap, not an isolated point solution.

Practical Steps to Pilot and Scale Digital FAI

Even with a compelling ROI model, many organizations benefit from a pilot to validate assumptions, build internal champions, and refine processes before full rollout.

Selecting candidate parts and suppliers

A good pilot scope is big enough to be meaningful but small enough to manage. Consider:
- Parts with medium-to-high complexity (100–300 characteristics) where time savings will be visible
- Programs with active customer engagement and upcoming engineering changes
- Sites or suppliers that experience repeated FAIR rejections or long cycle times
- Internal teams that are open to change and willing to provide detailed feedback
Include both full and delta FAI cases so you can evaluate how the software handles engineering change scenarios.

Defining success criteria and measurement plans

Before the pilot starts, agree on success metrics and how they will be measured. Common criteria include:
- Reduction in average engineering hours per FAIR
- Reduction in FAIR cycle time from trigger to approval
- Decrease in documentation-related rejections
- User adoption and satisfaction (surveys or interviews)
Set realistic targets (e.g., 30–50% time reduction in the first phase, improving further as teams gain proficiency) rather than assuming best-case numbers from day one.

Planning phased rollout across plants and programs

After a successful pilot, expand in phases:
1. Stabilize the pilot: Address lessons learned, refine templates, and finalize integrations for the initial site.
2. Extend to similar parts and programs: Roll out to adjacent product families where requirements and workflows are similar.
3. Expand to additional sites: Standardize governance, training, and configuration management so each new site ramps faster.
4. Onboard key suppliers: Provide training and support for tiered suppliers to submit FAIRs using your preferred digital process.
Each phase should have clear objectives, timelines, and owners. Use early phases to build internal case studies and testimonials that support broader adoption.

Using a Unified Digital FAI Platform as a Strategic Lever

While any move away from manual spreadsheets will improve FAI efficiency, a unified operations platform that embeds AS9102 within broader aerospace workflows can unlock additional strategic benefits:
- Consistent application of AS9102 Rev C across programs, plants, and suppliers
- Centralized control over templates, customer-specific requirements, and change histories
- Integrated nonconformance and corrective action management tied to specific FAIRs and characteristics
- Real-time dashboards for FAI throughput, bottlenecks, and audit readiness
As described in the AS9102 software: digital first article inspection for aerospace manufacturing overview, platforms like Connect981 treat FAI as one element of a connected aerospace operations environment. That broader context can strengthen your business case, especially for multi-site and prime-level stakeholders.

Building a Credible, Actionable Business Case

To summarize, a strong business case for AS9102 software should include:
- Current-state baseline for FAIR time, rejection rates, and FAI-driven delays
- Projected time savings based on realistic efficiency ranges
- Quality and audit impacts framed as risk and cost reductions, not guarantees
- Implementation and operating costs with transparent assumptions
- ROI range and payback period, ideally under 18–24 months
- Stakeholder-specific benefits for quality, operations, and IT
- Pilot plan with clear success criteria and a phased rollout strategy
By grounding your proposal in concrete metrics and openly acknowledging assumptions, you can move AS9102 software from a “nice to have” tool to a strategic investment in capacity, compliance, and customer performance.

The next step is to gather your baseline data, model a conservative ROI scenario, and design a pilot that tests both the technology and the process changes needed for sustainable improvement.
March 10, 2026
From Stand-Alone FAI Tools to Connected Aerospace Operations Platforms
From Stand-Alone FAI Tools to Connected Aerospace Operations Platforms

Digital AS9102 software platforms now sit at the center of how aerospace organizations manage first article inspection (FAI), new part introduction, and regulatory compliance. But not every company is ready to jump straight from spreadsheets to a fully integrated operations platform. Many teams start with stand-alone ballooning and FAIR tools before they connect FAI to the broader digital thread.

This article explains the digital maturity journey from paper-based FAI to point solutions and, ultimately, to connected aerospace operations platforms. It outlines the trade-offs at each stage and helps you decide which approach aligns with your programs, customer mix, and long-term digital strategy.

If you need a broader grounding in AS9102 and digital FAI before diving into architecture choices, see our AS9102 software and digital FAI overview.

The Three Maturity Levels of FAI Digitalization

Most aerospace organizations follow a recognizable path in how they manage AS9102 FAI:

Paper and spreadsheets

At the earliest stage, FAI is executed with manual tools:
- Printed drawings ballooned by hand
- Excel-based FAIR templates managed on shared drives
- Certificates and special process records collected via email
- Approvals captured through signatures on paper or static PDFs
This approach can work for low volumes, but it tends to break down when any of the following factors appear:
- Hundreds of characteristics per drawing
- Frequent engineering changes (delta FAI)
- Multiple customer-specific FAIR formats
- Multi-site operations or complex supply chains
Cycle times stretch to days or weeks, and error rates (missed balloons, wrong revisions, duplicated data entry) create repeated FAIR rejections and audit pain.

Stand-alone FAI tools

The next step is adopting dedicated point solutions that automate ballooning and FAIR creation:
- Import drawings and generate balloon numbers digitally
- Auto-populate Form 3 rows from extracted characteristics
- Standardize AS9102 Forms 1, 2, and 3 templates
- Store FAIRs in a local or cloud repository
This sharply reduces the time spent on manual ballooning and basic data entry. For single plants or small quality teams, it can be a high-ROI improvement, often cutting a typical full FAIR from 8–24 hours to a few hours or less.

The limitation: these tools often remain disconnected from ERP, MES, PLM, and broader quality workflows. FAI becomes more efficient locally but still operates as an island.

Integrated digital operations platforms

At the highest maturity level, FAI lives inside a connected aerospace operations platform, where:
- Work instructions, in-process inspections, nonconformances, and FAIRs share a unified data model
- FAI requirements are triggered automatically by part configuration, routing, and change events
- Measurement data can flow directly from CMMs or shopfloor inspection tools into Form 3
- Multi-site and supplier FAIRs are governed via common templates and workflows
The step up from stand-alone tools to platforms is about connecting FAI to everything around it: design, planning, execution, and compliance. It typically requires more upfront design and change management, but delivers compounding benefits as programs and sites scale.

Pros and Cons of Stand-Alone FAI Tools

Before committing to a full platform, many organizations evaluate or adopt stand-alone AS9102 point solutions. Understanding their strengths and constraints helps you decide whether they are a long-term answer or a stepping stone.

Speed of adoption and localized benefits

Stand-alone FAI tools are attractive because they:
- Can often be deployed by a single plant or even a single quality engineer
- Require relatively limited IT involvement compared to platform projects
- Provide quick wins in ballooning speed and standard form generation
- Are familiar to users who already work with desktop or office tools
For organizations that:
- Run a few FAIs per month
- Have predominantly single-site production
- Face moderate rather than extreme audit pressure
these tools can represent a practical and cost-effective solution.

Data silos and manual bridges to other systems

The main drawback of stand-alone FAI tools is that they often operate as a data silo. Typical pain points include:
- Double data entry: Part numbers, revisions, and order data manually copied from ERP or MES into the FAI tool
- Attachment hunting: Material certs, process records, and approvals scattered across emails and file shares
- Limited traceability: Difficult to navigate from a nonconformance or audit finding back to the originating FAIR and related shopfloor context
- Isolated analytics: FAI measurements not easily aggregated with in-process or final inspection data
Teams compensate with manual bridges—spreadsheets, copy-paste, and ad hoc reports. These workarounds tend to reintroduce errors and drag down the efficiency gains achieved by the tool itself.

Suitability for specific plant sizes and part portfolios

Stand-alone tools are usually best suited to environments with:
- One or a few manufacturing sites
- Lower volumes of FAI events
- Relatively simple customer requirements
- Limited need for cross-site reporting or standardized global governance
If your portfolio is dominated by build-to-print work with stable designs and predictable FAI demand, a point solution may remain viable for years. As soon as you face frequent design changes, multi-site collaboration, or tighter digital thread expectations from primes, the limitations become more visible.

What Connected Operations Platforms Add

Connected aerospace operations platforms treat AS9102 FAI as one component of a coordinated production and quality system. This changes both the scope and the impact of your digital FAI investment.

Unified data model for work instructions, FAI, and inspections

A platform creates a shared context for all execution and quality activities. For example:
- Each operation in a routing has linked work instructions and inspection steps
- FAI characteristics are tied to the same features and operations used for in-process checks and final inspections
- Nonconformances reference specific characteristics, work orders, serials, and suppliers
This unified data model enables:
- Automatic population of FAIR fields from existing master data
- Direct reuse of FAI characteristics for ongoing inspection plans
- Elimination of inconsistencies between what was planned, what was built, and what was inspected
Cross-site standardization and governance

For OEMs and multi-site suppliers, consistency is as important as efficiency. With a connected platform, you can:
- Define standard AS9102 templates and workflows once and roll them out globally
- Enforce common rules for full, partial, and delta FAI, including how change notices are interpreted
- Support customer-specific formats while retaining a single underlying data structure
- Govern permissions, approvals, and electronic signatures centrally
This reduces variability between plants and suppliers, which is particularly valuable when primes or regulators review FAIRs across your network.

Analytics and continuous improvement across processes

Because platforms integrate FAI with in-process inspections, NCRs, and corrective actions, you can analyze patterns that are invisible in stand-alone tools, such as:
- Characteristics that repeatedly appear in both FAI and production nonconformances
- Operations, machines, or suppliers associated with clusters of FAI issues
- Programs where delta FAI volume indicates design instability or process risk
Over time, this supports targeted improvements in design-for-manufacturability, process capability, and supplier development—turning FAI data into a strategic asset instead of a one-time compliance artifact.

Decision Factors: Which Approach Fits Your Organization?

Choosing between stand-alone FAI tools and integrated AS9102 software platforms is not only a technology decision. It is a question of timing, priorities, and organizational capacity.

Volume, complexity, and customer mix

Consider your current and future workload:
- FAI volume: How many full and delta FAIRs do you execute per month and per site?
- Part complexity: How many characteristics per drawing, and how many special processes and certs per part?
- Customer mix: Do you serve multiple primes with different FAIR formats and quality clauses?
- Supply chain structure: Are you coordinating FAIs across multiple internal plants and tiered suppliers?
Low volumes and simple programs can be well served by stand-alone tools. Complex, high-volume environments usually benefit from a platform approach that avoids duplicated work and fragmented records.

IT strategy and digital thread roadmaps

Your company’s broader digital strategy should also guide the choice:
- If you are standardizing on a digital thread connecting PLM, ERP, MES, and quality, it is usually more effective to select an AS9102 solution that can integrate natively into that architecture.
- If your IT roadmap is still emerging and budgets are tight, a stand-alone tool can function as an interim step while you design the longer-term ecosystem.
Either way, it is wise to evaluate how easily today’s choice can evolve—whether data can be migrated, and whether the vendor’s roadmap aligns with your future integration needs.

Change management capacity and timeline

Implementing a connected operations platform requires more than software installation:
- Process harmonization across plants and teams
- Training engineers, inspectors, and supervisors on new workflows
- Aligning quality, manufacturing, and IT stakeholders
If you need relief immediately for a single site under intense FAI pressure, a focused tool can stabilize the situation while you build support for a broader platform. If you already have sponsorship for digital transformation and cross-functional governance, going directly to a platform can help you avoid rework and tool sprawl.

Transitioning from Tools to Platforms Without Disruption

Many organizations will not choose between stand-alone FAI tools and platforms in a single step; they will move through a transition where both coexist. Managing that transition thoughtfully reduces risk and protects ongoing production.

Migrating historical FAIRs and templates

Historical AS9102 data has real value for audits, change analysis, and future delta FAIs. When moving to a platform, consider:
- Which FAIRs must be migrated (e.g., active programs, key customers, recent serials)
- How to convert existing Forms 1–3 into structured records that maintain characteristic accountability
- How to map old template variations into a standardized platform model without losing required customer fields
Not every legacy FAIR needs full conversion. Many teams choose a risk-based approach—migrating high-criticality programs and leaving older or low-risk FAIRs in an archived, read-only state.

Training and process harmonization

Platform rollouts are an opportunity to clean up inconsistent practices:
- Agree on standard rules for when full, partial, and delta FAI are required
- Define naming conventions for parts, revisions, and FAIR identifiers
- Align how key characteristics, critical characteristics, and special process indicators are flagged
Training should focus not only on button clicks, but on why these rules matter for auditability and traceability. This helps teams see FAI as part of an integrated quality system rather than another compliance burden.

Hybrid approaches during the transition phase

During migration, it is common to run a hybrid model:
- Some legacy programs continue using the stand-alone tool until completion
- New programs and high-visibility customers start in the platform from day one
- Bridges (e.g., CSV imports or APIs) transfer essential data between systems
Clear scoping and communication are key. Define which parts and customers are in which system, how approvals work in each, and when a given program will transition fully to the platform.

Future-Proofing Your AS9102 Digital Strategy

Choosing between stand-alone FAI tools and connected operations platforms is ultimately about future-proofing. Aerospace compliance and digital expectations will continue to evolve; your AS9102 software platforms need to keep pace.

Preparing for MBD, AI, and advanced analytics

Over the coming years, leading aerospace organizations are likely to expand:
- Model-based definition (MBD) and 3D PMI as primary design authorities
- AI-assisted inspection planning, suggesting which characteristics warrant tighter controls
- Advanced analytics correlating FAI data with process capability and field performance
A future-ready AS9102 solution should be able to:
- Handle both 2D drawings and 3D model inputs
- Expose FAI data in a way that analytics tools and data scientists can easily consume
- Support incremental automation, such as automatic anomaly detection in measurement results
Ensuring scalability for new programs and suppliers

As you win new programs or expand globally, your FAI system will need to scale without multiplying manual work. Consider:
- How quickly new sites, suppliers, and customers can be onboarded
- Whether FAIR templates and workflows are configurable without custom code
- How licensing and infrastructure models support growth across regions
Platforms are generally better positioned for this kind of scaling because they centralize governance while allowing local teams to operate within defined frameworks.

Governance and ownership of FAI data long term

Finally, clarify who owns and stewards FAI data as a strategic asset:
- Which roles are accountable for data quality and template changes?
- How are customer-specific requirements and revisions controlled?
- How is data retained for long-term regulatory and contractual obligations?
Whether you remain on a stand-alone tool or move to a connected platform, explicit governance prevents FAI from slipping back into ad hoc practices as organizations and programs evolve.

Putting It All Together

The path from manual FAI to connected operations is not one-size-fits-all. A practical way to plan your AS9102 digitalization journey is to:
1. Map your current maturity: Paper, stand-alone tool, or partially integrated platform.
2. Quantify the pain: Cycle time, rejection rates, audit findings, and late deliveries tied to FAI.
3. Align with strategy: Ensure your FAI approach fits your company’s digital thread and smart factory roadmap.
4. Design a staged plan: Stabilize urgent bottlenecks quickly, then move toward platform-level integration as capacity and sponsorship grow.
AS9102 software platforms that embed FAI into a unified operations environment offer the strongest long-term leverage—especially for organizations managing complex programs, multi-site networks, and demanding prime customers. Stand-alone tools can still play a useful role, particularly as transitional solutions or for focused use cases.

By viewing FAI not just as a compliance requirement but as a core node in your digital operations strategy, you can unlock better schedule performance, lower quality costs, and stronger customer confidence across your aerospace programs.
March 10, 2026
The Future of Digital FAI: MBD, AI, and the Aerospace Digital Thread
The Future of Digital FAI: MBD, AI, and the Aerospace Digital Thread

First article inspection (FAI) is no longer just a stack of AS9102 forms checked before releasing a new aerospace part to production. Over the next few years, FAI will sit at the intersection of model-based definition (MBD), AI-assisted analytics, and the broader aerospace digital thread that connects design, planning, execution, and in-service data. Teams that still treat FAI as an isolated paperwork exercise will struggle to keep pace with program complexity and customer expectations.

This article explores how digital FAI is evolving, and what quality, manufacturing, and engineering leaders should expect from the next generation of AS9102 software. It builds on foundational concepts from AS9102 software for digital first article inspection, and looks ahead to how MBD, AI, and connected factory systems will reshape day-to-day workflows.

From 2D Drawings to Model-Based Definition (MBD)

Most aerospace organizations still anchor FAI on 2D drawings and PDF packages, even when design authority already maintains a full 3D model. That gap creates redundant work: engineers translate 3D intent back into 2D, then FAI teams balloon the drawing and re-enter characteristic data into AS9102 forms.

What MBD and PMI Mean for FAI

Model-based definition (MBD) moves the authoritative product definition into the 3D model itself. Dimensions, geometric tolerancing (GD&T), surface finishes, and notes are captured as product manufacturing information (PMI) attached directly to model features. For FAI, that means:
- The 3D model becomes the primary source for characteristic extraction, not a downstream 2D derivative.
- Balloon numbers and Form 3 rows can be generated from PMI tags rather than from optical character recognition on a PDF.
- Design changes propagate through PLM-managed models in a controlled way, reducing the risk of using the wrong revision during FAI.
In a mature digital thread, AS9102 software consumes PMI-rich models via PLM or CAD integrations, creating structured characteristic records without manual re-interpretation of 2D views.

Extracting Characteristics Directly from 3D Models

As MBD adoption increases, the logical next step is for FAI tools to extract measurable requirements directly from 3D geometry and PMI. In practice, this looks like:
- Loading a native CAD or neutral MBD format, then parsing PMI to identify all verifiable dimensions, GD&T frames, and notes.
- Assigning unique characteristic IDs that map one-to-one to Form 3 rows and can be reused across builds, delta FAI, and future programs.
- Providing 3D navigation from each characteristic to its associated feature, making it easier for inspectors and CMM programmers to understand intent.
Compared with PDF-based ballooning, 3D extraction improves consistency and reduces interpretation errors, especially for complex structures and tight GD&T schemes. It also aligns FAI more closely with how CMM and metrology software already operate in many aerospace factories.

Challenges in Transitioning from 2D-Centric Processes

Moving FAI workflows from 2D drawings to MBD is not just a tooling change; it is an organizational shift. Common challenges include:
- Mixed-revision environments: Some parts are fully MBD, others remain drawing-centric, and FAI teams must support both simultaneously.
- Standards and customer expectations: Customers may still specify 2D drawing deliverables or have not formally approved model-based FAIRs as a primary reference.
- Skills and training: Inspectors and quality engineers may be less comfortable navigating 3D PMI than reading traditional blueprints.
A pragmatic approach is to run hybrid pilots: use MBD-derived characteristics as the internal source of truth but still generate AS9102-compliant forms and, where needed, drawing-based views for customer submission. Over time, as standards and customer practices evolve, organizations can phase out redundant 2D work.

AI and Automation in FAI Data Analysis

AI is often oversold as a push-button solution that will replace engineering judgment. In regulated aerospace manufacturing environments, that is neither realistic nor desirable. The more practical direction is AI and analytics augmenting human decision-making: guiding where to focus attention, checking FAIRs for inconsistencies, and surfacing patterns that would be hard to see manually.

AI-Assisted Risk-Based Sampling Approaches

Risk-based inspection is already established in aerospace quality systems; what changes is the data and tooling that inform those decisions. Emerging AS9102 software capabilities include:
- Using historical FAIRs and in-process inspection results to estimate process capability for families of parts and operations.
- Suggesting when 100% measurement is warranted (e.g., new suppliers, unstable processes, safety-critical characteristics) versus when statistically justified sampling is appropriate.
- Highlighting characteristics with marginal capability or frequent near-miss conditions so engineers can tighten sampling or adjust control plans.
Importantly, these AI-assisted recommendations should be transparent and overrideable. Quality leaders remain responsible for approving inspection strategies; the system provides context, not commands.

Anomaly Detection in Measurement Data

FAI results often sit in a repository until an audit or customer issue forces a review. Anomaly detection changes that by scanning results as they are recorded. Typical use cases include:
- Flagging unusual distributions, such as one dimension consistently trending toward a tolerance limit across multiple builds.
- Identifying inconsistent units, extreme outliers, or patterns that suggest transcription errors.
- Surfacing systematic offsets that hint at fixture, probe, or program issues, before they propagate across a fleet of parts.
Because AI models can misinterpret rare yet valid data, anomaly alerts should be reviewed by engineers who can confirm whether the pattern reflects a true process issue or expected variation. The value lies in earlier visibility, not automatic disposition.

Automated Validation of FAIR Completeness and Consistency

One of the most immediate AI-adjacent wins is rule-based and statistical validation of FAIRs before submission. Advanced AS9102 tools can:
- Check that every ballooned or PMI-derived characteristic appears exactly once on Form 3.
- Verify that material certs and special process records are attached for all relevant Form 2 entries.
- Confirm unit consistency, tolerance format, and revision alignment across Forms 1, 2, and 3.
Much of this can be implemented today with deterministic rules, complemented by AI models that learn typical patterns for a program or supplier and highlight deviations. The result is fewer customer rejections and less manual rework on incomplete FAIRs.

FAI as a Node in the Aerospace Digital Thread

Historically, FAI data stayed within the quality function. In a digital thread architecture, first article inspection becomes a key node linking design, process planning, production execution, and in-service performance. That shift turns FAIRs from static evidence into a rich source of engineering, sourcing, and operations intelligence.

Connecting Design, Planning, Production, and In-Service Data

In a connected aerospace manufacturing environment, AS9102 software does not operate alone. It exchanges data with PLM, MES, ERP, and maintenance information systems:
- Design: PLM supplies the authoritative model or drawing, change history, and configuration rules.
- Planning: Process plans and operation sequences flow from manufacturing engineering tools into the FAI context.
- Production: MES links FAIRs to specific work orders, machines, tools, and operators.
- In service: Maintenance and reliability systems can reference original FAI data when investigating recurring issues.
When these connections are in place, the FAIR becomes a snapshot of how a particular configuration was realized at a moment in time, fully traceable back to design intent and forward to field performance.

Using FAI Results to Refine Tolerances and Manufacturability

First article results often reveal whether a design is realistically manufacturable with the intended processes and suppliers. By aggregating FAI data across parts and programs, engineering teams can:
- Identify features that repeatedly push process capability limits or require excessive rework.
- Highlight tolerances that are unnecessarily tight relative to functional needs.
- Feed evidence-based feedback into design for manufacturability (DFM) guidelines and design standards.
This turns FAI from a compliance gate into a feedback loop: design decisions are informed by past production reality, reducing ramp-up friction on future programs.

Linking Certifications and Process Data to Maintenance Records

For long-life aerospace platforms, the ability to trace from an in-service serial number back to its initial FAI and associated certifications is increasingly important. In a robust digital thread:
- Each FAIR is indexed by part number, serial, lot, and configuration.
- Material and special process records attached to Forms 1 and 2 are stored as structured data, not just PDFs on a shared drive.
- Maintenance events in fleet management systems can link back to the original FAIR to investigate whether initial variability correlates with field performance.
This level of linkage requires disciplined configuration management and common identifiers across systems, but it pays off in faster root-cause analysis and more targeted corrective actions.

Supplier Collaboration and Real-Time Portals

Aerospace primes are increasingly pushing digital requirements into their supply base: structured FAIRs, standard templates, and near-real-time visibility into inspection status. The future of digital FAI will depend as much on supplier collaboration as on internal factory systems.

Shared FAIR Templates and Live Status Visibility

Instead of each supplier maintaining its own spreadsheet templates, modern platforms provide shared, controlled AS9102 formats via secure portals. Capabilities typically include:
- Prime-defined templates that enforce mandatory fields, revision usage, and customer-specific clauses.
- Real-time visibility into FAIR status across suppliers: not started, in progress, submitted, under review, or approved.
- Standardized data structures that make downstream analytics (e.g., across suppliers or commodity groups) feasible.
This reduces interpretation errors and ensures that when data reaches the OEM, it is already compatible with their systems and reporting needs.

Reducing Rework and Clarification Cycles with Primes

Much of the delay and friction around FAI comes from back-and-forth clarification: missing attachments, ambiguous dimension coverage, or questions about process changes. Digital collaboration environments help by:
- Embedding validation rules and checklists that suppliers must pass before submission.
- Providing structured comment threads tied to specific characteristics or documents.
- Maintaining a single source of truth for each FAIR, rather than multiple email chains and file versions.
The outcome is fewer rejected FAIRs, more predictable lead times, and better use of both supplier and OEM engineering capacity.

Security, IP Protection, and Access Control Considerations

As more design and inspection data flows through shared portals, protecting intellectual property and regulated information becomes critical. Future-ready FAI platforms must support:
- Granular access control down to part families, programs, or specific FAIRs.
- Encryption in transit and at rest, with clear segregation between customers and suppliers.
- Audit trails showing who accessed or modified data, when, and from where.
Aerospace organizations should evaluate not only functional capabilities but also how FAI tools align with IT security policies, export control requirements, and customer data handling clauses.

Preparing Your Organization for the Next Generation of FAI

Transitioning to AI-enabled, MBD-driven FAI will not happen overnight. Organizations need to understand their current maturity, set realistic priorities, and align technology decisions with standards evolution and customer roadmaps.

Assessing Current Digital Readiness

A practical first step is a structured assessment of how FAI is executed today:
- What proportion of FAIRs are created manually in spreadsheets versus via dedicated AS9102 software?
- How frequently are 3D models with PMI available, and how are they used today?
- Which systems hold critical FAI-related data (PLM, MES, ERP, QMS), and how well are they integrated?
Documenting this baseline helps identify where digital upgrades will have the most immediate impact: reducing rework, shortening lead time, or improving audit readiness.

Prioritizing Capabilities to Invest in First

Not every organization needs cutting-edge AI on day one. For many aerospace manufacturers, the highest-value early investments are:
- Reliable digital ballooning and characteristic extraction from drawings or models.
- Structured AS9102 forms with built-in validation and revision control.
- Centralized storage and search for FAIRs, certs, and supporting documents.
Once those foundations are in place, teams can layer on analytics, anomaly detection, and deeper integration with MES and PLM. Attempting advanced capabilities without a stable data foundation usually leads to frustration.

Building a Roadmap That Aligns with Standards Evolution

AS9102, AS9100, and customer-specific requirements will continue to evolve as digital practices mature. A useful roadmap:
- Maps target capabilities (e.g., MBD-based FAI, supplier portals, AI-assisted checks) against planned system upgrades and program milestones.
- Identifies standards or customer guidance that may affect when certain practices are accepted (for example, model-based submissions).
- Includes governance for how FAI processes are updated as standards or internal procedures change.
The goal is to avoid one-off tool deployments and instead build a coherent, long-term path toward connected, data-centric FAI.

Practical Steps to Experiment with Advanced FAI Capabilities

Many aerospace teams want to explore advanced digital FAI but are constrained by active programs, existing contracts, and limited engineering bandwidth. Small, well-scoped pilots can prove value without disrupting ongoing delivery.

Pilot Projects Using MBD-Derived Characteristics

For programs where the design authority already maintains MBD, consider a pilot that:
- Uses a limited set of parts to trial PMI-based characteristic extraction into the FAI system.
- Compares time and error rates against traditional 2D ballooning.
- Engages both design and quality teams to refine how PMI is structured for inspection use.
Lessons from this pilot can inform modeling practices, internal standards, and supplier training before rolling out model-based FAI more broadly.

Using Analytics on Existing FAIR Data

Even without new measurement equipment or AI models, most organizations have years of FAIRs that are underutilized. A straightforward analytics initiative might:
- Normalize existing FAIR data into a common structure, even if it began as spreadsheets.
- Visualize where FAI rejections, late approvals, or near-miss dimensions cluster by part family, supplier, or process.
- Feed those insights into process improvement projects or design guidelines.
This kind of work builds the data literacy and governance needed before deploying more advanced anomaly detection or risk-based sampling algorithms.

Partnering with Software Providers on Roadmap Features

Given the pace of change around the digital thread and AI, no single vendor will have every capability fully mature today. Aerospace manufacturers can shape solutions by:
- Participating in customer advisory boards focused on MBD, AS9102 Rev C interpretation, and supplier collaboration.
- Co-designing pilot features such as AI-assisted FAIR checks or new integration points with PLM and MES.
- Aligning contracts and deployment plans with clear milestones for advanced capabilities rather than generic promises.
For platforms like Connect981 that already embed FAI within a broader aerospace operations environment, this collaboration ensures that future enhancements match real engineering and production needs, not abstract technology trends.

The trajectory is clear: FAI is moving from static documentation toward an integrated, data-rich capability that supports faster new part introduction, tighter process control, and more effective collaboration across the aerospace supply chain. Organizations that invest now in solid digital foundations—structured AS9102 data, integration with core systems, and disciplined configuration management—will be best positioned to take advantage of MBD and AI as they mature.
March 10, 2026
Designing an AS9102 Workflow: Best Practices from Planning to Submission
Designing an AS9102 Workflow: Best Practices from Planning to Submission

Aerospace manufacturers, defense programs, and space hardware suppliers all depend on reliable first article inspection (FAI) to prove that new or changed production processes can consistently deliver conforming hardware. AS9102 defines the minimum requirements, but it does not tell your organization how to design the day-to-day workflow across engineering, quality, operations, and suppliers. That design choice determines whether FAIs move smoothly or become a recurring bottleneck.

This article describes a practical end-to-end AS9102 workflow from planning through execution, review, and submission, and then shows how to standardize it across plants and suppliers using digital tools. It complements the broader perspective on digital article inspection in AS9102 software for digital first article inspection, focusing specifically on workflow design, roles, and governance.

Planning the AS9102 FAI

A robust AS9102 workflow starts before any balloons are placed on a drawing. Planning clarifies when FAI is required, what will be inspected, and how responsibilities are divided across teams and organizations.

Determining when FAI is required

AS9102 specifies common triggers for FAI, but each aerospace organization must translate these into clear rules embedded in its quality management system (QMS) and production planning processes. Typical triggers include:
- New part introduction to production or to a particular site or supplier.
- Engineering changes that affect form, fit, or function.
- Process changes such as new equipment, tooling, or manufacturing location.
- Changes in material, source, or software that affect product characteristics.
- Production lapses exceeding the time limit defined in your QMS or customer requirements.
In a mature workflow, FAI triggers are not left to memory or tribal knowledge. Instead, they are encoded in planning rules inside ERP, MES, or an integrated quality platform so that a work order or configuration automatically indicates whether a full, partial, or delta FAI is required. That automation avoids missed FAIs and late discovery of requirements at shipment.

Defining scope and classification of characteristics

For each FAI event, the planning step must define the technical scope. This includes identifying the applicable configuration and deciding how to classify characteristics, such as:
- Standard characteristics inspected according to drawing tolerances.
- Key characteristics (KCs) that significantly affect performance, reliability, or manufacturability.
- Critical characteristics (CCs) that relate directly to safety of flight or regulatory requirements.
Classification drives the depth of evidence expected on Form 3, sampling plans, and the level of process capability analysis that may accompany the FAIR. In a digital workflow, these classifications should be stored as structured data, not handwritten notes, so that downstream inspection plans and dashboards can easily distinguish KCs and CCs across part families and programs.

Coordinating with customers on expectations and formats

Many primes and Tier 1 customers add program-specific requirements on top of AS9102, such as unique FAIR templates, additional traceability fields, or naming conventions. Effective FAI planning therefore includes explicit customer coordination:
- Confirm whether the customer requires its own template or will accept your standard AS9102-compliant format.
- Check if any additional evidence—such as capability studies, special process logs, or test data—must be bundled with the FAIR.
- Align on submission method (portal upload, EDI, email) and review timelines.
Digital systems can codify these expectations into customer-specific profiles so that each FAIR inherits the correct template and export format based on customer, part family, or contract.

Preparing Drawings, Data, and Inputs

Once an FAI is triggered and scoped, the next step is to ensure all technical inputs, documents, and digital structures are in place. Preparation quality strongly influences the cycle time and accuracy of the final FAIR.

Ensuring correct drawing revision and configuration

Configuration management is a critical risk area in aerospace production. Before ballooning or inspection planning begins, the team must verify that:
- The drawing or model being used matches the configuration defined on the contract and work order.
- All associated specifications and notes are at the correct revision level.
- Digital systems consistently reference the same revision across PLM, ERP, MES, and FAI tools.
In connected factories, this is handled by linking FAIRs directly to controlled configuration objects (e.g., engineering change orders or released models). The FAI workflow should prevent creation of a FAIR against an obsolete revision and should preserve traceability when a delta FAI is required after a design change.

Gathering material, process, and certification requirements

Beyond the drawing, AS9102 requires evidence that materials and special processes comply with design requirements. During preparation, the responsible engineer or planner should:
- Identify all material specifications and associated certificates that must be captured on Form 2.
- List all special processes (e.g., heat treatment, surface treatment, welding, NDT) and correlate them with internal or supplier process approvals.
- Clarify which process outcomes (hardness, coating thickness, conductivity, etc.) must be recorded as results versus simply documented through certificates.
Digital FAI tools can help by enforcing required attachments, linking process records and material lots to the FAIR, and flagging missing certifications before approval.

Setting up digital templates and checklists

Standardization begins at the template level. Rather than letting each engineer build FAIRs from blank forms or ad hoc spreadsheets, leading aerospace organizations maintain controlled digital templates and checklists that define:
- Fields and structure of Forms 1, 2, and 3 in alignment with AS9102 Rev C.
- Additional internal fields required by the QMS (e.g., internal routing numbers, process owner codes).
- Checklist items for planning, including trigger confirmation, rev checks, and customer-specific expectations.
Workflow-oriented platforms can automatically instantiate these templates based on part type, risk category, or customer. This reduces variability and accelerates training for new engineers or supplier quality teams.

Executing the FAI

Execution is where most effort and error risk concentrates: ballooning the drawing or model, capturing actual results, and managing issues discovered during inspection. A disciplined, digital-first workflow minimizes manual transcription and enforces characteristic accountability.

Ballooning drawings and extracting characteristics

The first visible step in execution is creating the ballooned drawing or model view. In a modern workflow, this should be done using software rather than manual markup.
- Drawings (or 3D models with PMI) are imported into FAI software that identifies dimensions, GD&T, notes, and other inspection-relevant requirements.
- Each requirement receives a unique balloon number, which becomes a persistent identifier for that characteristic.
- Engineers review and validate detected characteristics, adding any overlooked notes or process-related requirements.
The crucial principle is one characteristic, one balloon, one Form 3 line. This mapping is the backbone of traceability. Once balloons are confirmed, the system should auto-generate the Form 3 characteristic list and maintain a reliable link back to the visual representation.

Collecting measurement and test data

Next, inspection and test results are gathered for each characteristic. In an optimized AS9102 workflow, manual entry into spreadsheets is avoided wherever possible. Instead, organizations integrate multiple data sources:
- CMM programs and other metrology systems that push results directly into the characteristic fields on Form 3.
- Digital checklists or MES terminals used by operators to record in-process inspection measurements.
- Laboratory test results (e.g., hardness, conductivity, tensile) imported as structured data and linked to the relevant balloons.
Validation rules in the digital FAIR form help catch issues such as missing units, out-of-range values, or inconsistent decimal precision. When nonconformances are found, they should trigger formal nonconformance records, not just notes in the FAIR.

Managing rework and nonconformances during FAI

Inevitably, some FAIs uncover discrepancies. The workflow must define how to respond without losing traceability or compromising compliance.
- Nonconformances are logged in the QMS with a clear link back to the specific characteristic and FAIR.
- Rework is planned and executed through controlled work instructions, with repeat measurements recorded against the same balloon numbers.
- If disposition decisions (e.g., use-as-is, repair, deviation) are required, these are documented in the QMS, while the FAIR records the final accepted result.
The FAIR itself should not become a substitute for nonconformance or deviation processes. Instead, it acts as a structured summary of final, accepted results, with references to the supporting quality records.

Review, Approval, and Submission

Even well-executed inspections can fail if review and approval are inconsistent or poorly documented. AS9102 workflows should make these steps explicit, role-based, and digitally controlled.

Internal review and quality signoff

Before any FAIR is sent to a customer, an internal review ensures completeness and accuracy. Typical checks include:
- Verification that all applicable characteristics are accounted for and correctly mapped.
- Confirmation that Forms 1, 2, and 3 are coherent (e.g., part numbers, revisions, and serials match across forms).
- Checks that required supporting documents—material certs, process logs, test reports—are attached and legible.
Digital workflows can formalize these reviews using checklists, role-based tasks, and dashboards that highlight missing or inconsistent data. This reduces reliance on individual reviewer experience and improves consistency across sites.

Electronic signatures and controlled approvals

Many aerospace organizations operate in environments that require secure, auditable electronic signatures. A best-practice FAI workflow therefore includes:
- Role-based approval routing (e.g., manufacturing engineering, quality engineering, quality manager).
- Electronic signatures that are tied to individual user identities and time-stamped, with clear indication of which form or revision was approved.
- Immutable audit logs that show who changed what and when between draft and approved FAIRs.
This approach supports both AS9100 expectations and regulatory requirements, and it makes responding to customer or registrar questions significantly easier during audits.

Submitting FAIRs and responding to customer feedback

Submission is more than just sending a PDF. The workflow should clarify:
- Submission channels (customer portal, secure file transfer, or integrated interfaces).
- Required file formats (native digital format, PDF, data exchange formats) per customer or program.
- Responsibilities for tracking status, recording customer approvals, and managing rejections or clarification requests.
Digital platforms can track FAIR status across the supply chain—draft, submitted, under review, accepted, or rejected—giving program and quality leaders visibility into where FAI-related delays might affect deliveries.

Standardizing Workflows Across Sites and Suppliers

Designing a single robust AS9102 workflow is only the first step. The real challenge for aerospace companies is ensuring that the same principles are applied consistently across internal plants and external suppliers, without ignoring local constraints or customer-specific clauses.

Common templates and process maps

Standardization starts with a documented reference workflow and shared templates. Organizations often define:
- A core process map that outlines planning, preparation, execution, review, and submission steps.
- Standard digital templates for Forms 1–3 and for supporting checklists.
- Risk-based variants of the workflow (e.g., enhanced review for critical parts or programs).
These artifacts should be centrally controlled but configurable so that sites and suppliers can tailor fields or steps to satisfy local regulatory requirements or customer demands while still staying aligned with the corporate standard.

Training and competency development

Even the best-designed AS9102 workflow fails if engineers and inspectors do not fully understand the intent behind each step. Organizations should treat FAI as a core competency, not an occasional paperwork task, by:
- Providing structured onboarding for new engineers and supplier quality staff on AS9102 concepts and internal workflow expectations.
- Using real FAIR examples—including both strong and weak submissions—to illustrate acceptable practice.
- Leveraging digital tools to embed guidance into forms themselves (tooltips, in-form examples, links to procedures).
Competency assessment can be supported with periodic FAIR reviews, peer audits, or spot checks that focus on systemic understanding, not just form completion.

Governance for changes to FAI procedures

As AS9102 evolves and customers update their requirements, FAI workflows must adapt while preserving traceability. Governance mechanisms should include:
- Formal change control for FAI procedures, templates, and digital workflows.
- Impact assessment to determine which programs, sites, or suppliers are affected by a change.
- Versioning of FAIR templates and associated instructions so that past FAIRs remain linked to the procedures in effect at the time.
Digital workflow engines make it easier to implement these changes consistently and to document which FAIRs were built under each procedural version—critical evidence during audits or investigations.

Embedding Continuous Improvement into the AS9102 Workflow

FAI is often viewed as a compliance burden, but it also produces rich data about product design, process capability, and supplier performance. Organizations that treat FAI data as an improvement asset can reduce future defects and streamline new product introduction.

Capturing lessons learned from each FAIR

Each completed FAIR should feed a structured lessons-learned process. Typical topics include:
- Characteristics that were consistently close to tolerance limits, indicating potential process capability concerns.
- Design features that were difficult to inspect or that required complex setups.
- Recurring issues with particular suppliers, processes, or materials.
Digital platforms can capture these insights in a standardized way—through tags, structured comments, or dedicated review steps—and make them discoverable for future programs and engineering change assessments.

Using metrics to refine workflows and training

To understand the effectiveness of the AS9102 workflow, aerospace leaders should track quantitative metrics, such as:
- Average cycle time from FAI trigger to customer-accepted FAIR.
- Percentage of FAIRs rejected due to documentation or traceability issues.
- Number of late deliveries where FAI delays were a contributing factor.
- Frequency and impact of FAI-related audit findings.
By analyzing these metrics across programs, sites, and suppliers, organizations can identify where training, template refinement, or additional automation will produce the highest return.

Integrating FAI feedback with design and process engineering

Finally, the AS9102 workflow should connect back into the broader digital thread of aerospace product development and manufacturing. Examples include:
- Using FAI measurement data to inform design-for-manufacturability reviews and tolerance optimization.
- Feeding recurring FAI issues into formal corrective action and process improvement projects.
- Linking FAIRs to configuration-managed design and planning objects so engineering can see exactly how changes affected process capability.
Platforms like Connect981 position FAI as one node in a connected aerospace operations environment, rather than an isolated document. This perspective enables better decisions across engineering, production, and supplier management, while still respecting that each organization must tailor workflows to its own QMS and customer expectations.

When designing or refining your AS9102 workflow, treat this reference model as a guide, not a rigid prescription. Align each step with your existing QMS, regulatory context, and contractual requirements, and use digital tools to enforce consistency, improve visibility, and capture the data needed for continuous improvement across your aerospace manufacturing network.
March 10, 2026