# AI for NCR, CAPA, and 8D Drafting in AS9100 and ISO 9001 Quality Systems > Source: https://netray.co/erp-ai/ai-quality-ncr-capa-erp > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: AI for ERP ## Short answer AI drafting for nonconformance reports, corrective action plans, and 8D reports pulls the facts already in your ERP, such as SyteLine QCS nonconformance records, SAP QM notifications, or Infor LN quality sessions, and produces a structured first draft for the quality engineer to review and finalize, not a system that closes an NCR on its own. It cuts drafting time on routine nonconformances while keeping AS9100 and ISO 9001 sign-off exactly where it already sits, with the quality engineer. Netray deploys this on-prem or in a private cloud so nonconformance detail involving customer or program data never leaves your network. ## Kind use-case ## Erp Family multi-erp ## Erp Products - SAP S/4HANA - Infor SyteLine - Infor LN - IFS Cloud - Oracle E-Business Suite ## Industries - Aerospace - Defense - Electronics - Manufacturing ## Region Global ## Persona Quality Director ## Eyebrow Faster paperwork, same standard ## Intro If you run quality for an aerospace or defense supplier, your engineers likely spend a disproportionate share of the week writing up routine NCRs and CAPA documentation rather than doing root cause investigation or supplier audits, and an AS9100 auditor expects complete, consistent documentation every time, not a best effort. A growing open-NCR count or slow CAPA closure is exactly what an AS9100 surveillance audit finds and flags, and it is rarely because engineers do not know the root cause; it is because writing the record up to standard takes time they do not have between shop-floor calls and supplier issues. AI drafting changes what that writeup time looks like. It reads the nonconformance data already captured in the ERP, such as part number, work order, defect code, and disposition, along with the engineer's notes, and produces a structured first draft, a problem statement, containment section, root cause hypothesis, and proposed CAPA, in the format the customer's or standard's template expects, for the engineer to verify and finalize. In the target state, a quality engineer opens a drafted NCR or CAPA that already has the ERP facts filled in correctly, spends the saved time on root cause and verification instead of formatting and data entry, and the record closes faster without cutting a corner AS9100 clause 10.2 would flag. ## Pain Points ### Drafting time crowds out root cause work **Detail:** Quality engineers spend hours per week on the mechanical parts of NCR and CAPA writeups, restating what is already in the ERP record, instead of investigating why the defect happened. ### Inconsistent documentation quality **Detail:** NCR and CAPA quality varies by which engineer wrote it, which is exactly what an AS9100 or customer supplier-quality audit will surface as a finding. ### Slow CAPA closure inflates audit risk **Detail:** A growing average CAPA cycle time is a common AS9100 surveillance audit observation, and it usually traces back to writing bandwidth, not investigation difficulty. ### Data re-entry between systems **Detail:** Part number, work order, and defect data already live in the ERP's quality module, but the engineer re-types much of it into a separate CAPA form or spreadsheet. ### 8D drafting for customer-facing nonconformances **Detail:** A customer-requested 8D report follows a fixed structure, D1 through D8, and assembling it from ERP and shop-floor data by hand under a deadline is a recurring source of late or thin submissions. ## Use Cases ### NCR draft from work order and inspection data **Description:** An engineer opens a draft with part, operation, defect code, and quantity already populated. **Erp Objects:** SyteLine qcs_nc_main/qcs_nc_detail, SAP QM notification QMEL/QMFE, Infor LN quality session tdsls **Outcome:** Engineer starts from a populated draft instead of re-keying data already captured in the ERP. ### CAPA draft with proposed root cause categories **Description:** A first-pass CAPA structure and a shortlist of likely root cause categories are drafted from prior similar nonconformances. **Erp Objects:** Nonconformance history for the same part or process, prior CAPA records **Outcome:** Engineer gets a starting structure and shortlist to confirm or reject, instead of starting from a blank page. ### 8D report assembly for a customer complaint **Description:** D1 through D8 sections populate from the ERP and shop-floor records already tied to the complaint. **Erp Objects:** Customer complaint record, affected work orders, logged containment actions **Outcome:** Cuts the manual assembly time for a customer-facing 8D, particularly under a tight response deadline. ### Trend narrative across NCRs by part, supplier, or defect code **Description:** A written summary highlights which supplier or process is driving NCR volume in a given period. **Erp Objects:** Nonconformance history **Outcome:** The quality director gets a written summary instead of building the pivot table by hand. ### Supplier corrective action request drafting **Description:** A SCAR draft is populated from the receiving inspection NCR that triggered it. **Erp Objects:** Supplier quality records, receiving inspection nonconformances **Outcome:** Reduces turnaround from defect discovery to supplier notification. ### AS9102 First Article Inspection discrepancy summary **Description:** Nonconforming characteristics are summarized against the ballooned drawing for engineering review. **Erp Objects:** FAI records, inspection results **Outcome:** Replaces a line-by-line manual comparison with a summary engineering can review quickly. ### CAPA effectiveness-check reminder and draft **Description:** The system flags CAPAs due for effectiveness verification and drafts the verification record from the original corrective action plan. **Erp Objects:** CAPA due dates, closed CAPA records **Outcome:** Reduces missed effectiveness checks, a common item auditors cite in surveillance findings. ## Architecture **Overview:** The system reads nonconformance and related shop-floor data, drafts the narrative sections against the customer's approved template, and leaves disposition and sign-off exactly where AS9100 and ISO 9001 already require them, with the quality engineer. **Layers:** #### ERP and QMS connectors **Detail:** Reads nonconformance, work order, inspection, and supplier quality records from SyteLine QCS, SAP QM, Infor LN quality sessions, or a standalone QMS if quality is not run inside the ERP. #### Data and semantic layer **Detail:** Maps defect codes, disposition types, and part and revision data consistently, so drafting logic works the same whether a record originated on the shop floor, receiving inspection, or a customer complaint. #### Model serving **Detail:** An open-weight LLM drafts the narrative sections, problem statement, root cause hypothesis, and proposed corrective action, against the approved template, served on customer GPUs or a private cloud. #### Retrieval **Detail:** Pulls similar prior nonconformances for the same part or process to suggest root cause categories and prior corrective actions, without auto-applying them. #### Governance and audit **Detail:** Every draft is attributed to the AI assist and requires engineer review and sign-off before it becomes a controlled quality record, preserving the approval chain AS9100 clause 10.2 expects. ## Deployment Options ### Air-gapped on-prem **Fit:** Aerospace and defense suppliers where nonconformance data references controlled technical data, program names, or customer-restricted information. **Detail:** The model and drafting engine run entirely inside the customer's network, with no outbound calls to a public model API. ### Private or sovereign cloud **Fit:** Commercial manufacturers under ISO 9001 without ITAR-level data restrictions, wanting faster deployment. **Detail:** The same architecture runs in the customer's own single-tenant cloud environment. ### Hybrid **Fit:** Teams piloting drafting for internal NCRs in a private cloud, then moving to on-prem before extending to customer-facing 8D content that references program-specific detail. **Detail:** A staged path that keeps the more sensitive customer-facing content on-prem from the start. ## Compliance ### Every draft is attributed to the AI assist and still requires engineer review and disposition sign-off, so the nonconformance and corrective action controls the standard requires stay in place unchanged. **Framework:** AS9100D clauses 10.2 and 8.7 ### The same approval chain applies for ISO-only sites, with drafting assist noted where the customer's documentation standard requires it. **Framework:** ISO 9001:2015 clause 10 ### Drafting output is generated against the customer's exact required 8D or SCAR template, not a generic one, so flowdown formatting requirements are met by design. **Framework:** Customer flowdown quality requirements ### Where nonconformance records reference controlled technical data, on-prem deployment keeps that data inside the customer's existing controlled environment. **Framework:** ITAR/EAR ### Drafted and finalized records are stored under the customer's existing retention policy, typically tied to program life plus a fixed number of years. **Framework:** Record retention ## Integration Notes - SyteLine: reads qcs_nc_main/qcs_nc_detail and related work order data via IDO; drafted CAPA content is written back as a linked document or note, not as a change to the disposition itself. - SAP QM: reads QMEL notifications and QMFE defect data via OData or BAPI; the draft is attached to the notification, and the engineer completes the standard QM workflow to close it. - Infor LN: quality sessions in the tdsls family are read via BOD or OData where available; drafting output is stored as an attached document against the session. - Prior-nonconformance retrieval is scoped to records the requesting engineer already has permission to see under existing QMS role security. - No NCR or CAPA is auto-closed; disposition, sign-off, and closure remain manual steps inside the existing QMS or ERP workflow. - Where an AS9100-mandated template or a customer-specific 8D format is in use, the draft is generated against that exact template, not a generic one. ## Product Fit ### Custom build **Why:** Drafting logic and templates are specific to a customer's AS9100 or ISO documentation standards and customer flowdown requirements, which is typically a custom build. ### ERPray **Why:** ERPray covers the trend-narrative and natural-language Q&A use cases layered on top of the same nonconformance history. ## Engagement ### Discovery **Duration:** 2-3 weeks **Deliverables:** - Review of current NCR, CAPA, and 8D templates and customer-specific format requirements - Audit of average drafting and closure time by nonconformance type - Sample of recent AS9100 audit findings related to documentation - Quality role and permission mapping ### Pilot **Duration:** 6-8 weeks **Deliverables:** - Drafting live for one nonconformance category, such as receiving inspection NCRs - Engineer review workflow with side-by-side before-and-after drafting time - Accuracy review against a sample of engineer-finalized records ### Production **Duration:** Ongoing **Deliverables:** - Rollout to remaining NCR and CAPA categories - SCAR and 8D drafting added - Trend narrative reporting for quality director and management review ### Scale **Duration:** Ongoing **Deliverables:** - Supplier-facing SCAR automation - Effectiveness-check reminders - Extension to additional plants or business units under the same quality system ## Buyer Questions - Does the AI ever close or disposition an NCR or CAPA on its own, or only draft it for engineer review? - Can it generate output against our exact customer-specific 8D or SCAR template, not a generic one? - Where does nonconformance data go, especially if it references program or customer-restricted information? - How does prior-nonconformance retrieval respect our existing QMS role permissions? - Can our AS9100 auditor review the trail showing which parts of a record were AI-drafted versus engineer-written? - How does the system handle a nonconformance type it has not seen examples of before? - What is the retraining or template-update process when our customer changes their required 8D format? - Does this integrate with our QMS if quality is run outside the core ERP? ## Faq ### Will an AI-drafted NCR or CAPA hold up in an AS9100 audit? **Answer:** The quality record itself still goes through your normal engineer review and sign-off, so the audit trail and approval chain AS9100 clause 10.2 expects is unchanged. What changes is how much of the mechanical drafting the engineer has to do by hand; the auditor is still assessing a human-approved record, with drafting assist noted if your documentation standard requires it. ### Does this replace our quality engineers? **Answer:** No. It handles the repetitive drafting work, restating ERP facts into the required document structure, so engineers spend more time on root cause investigation, containment verification, and supplier audits, which is where their judgment actually matters. ### Can it determine root cause on its own? **Answer:** It suggests root cause categories based on prior similar nonconformances in your own history, as a starting point for the engineer's investigation, not a conclusion. Root cause determination, whether 5-Why, fishbone, or whatever method your quality system specifies, remains the engineer's call. ### Our NCR data references program names that are export-controlled. Is this safe? **Answer:** That is the case on-prem deployment is built for. Nonconformance records involving controlled technical data should not be processed by a public AI service; keeping the model and the document store inside your network keeps that data where it already has to stay, though the specific ITAR/EAR classification is still your export control officer's determination. ### How much faster is drafting with this in place? **Answer:** It depends heavily on nonconformance complexity and how much of the required detail already lives in structured ERP fields versus free-text notes. The pilot phase measures actual before-and-after drafting time on your own NCR types rather than relying on a general estimate. ### Does this work if our quality system is a standalone QMS, not inside the ERP? **Answer:** Yes, provided the QMS exposes an API or database access for nonconformance and inspection records; the same drafting logic reads from a standalone QMS the same way it reads from SAP QM or SyteLine QCS, the connector is what changes. ### What happens to CAPA effectiveness checks, which are a common audit gap? **Answer:** The system can flag CAPAs approaching their effectiveness-check due date and draft the verification record from the original corrective action plan, which addresses one of the more common AS9100 surveillance audit findings, missed or late effectiveness verification, though the actual verification activity is still performed and signed off by the engineer. ## Search terms AI NCR drafting, AI CAPA ERP, 8D report AI, AS9100 AI quality, on-prem AI for quality management, SyteLine QCS AI, AI quality management ERP, SCAR drafting AI, AI nonconformance report, quality AI aerospace manufacturing, ISO 9001 AI corrective action, AI root cause analysis ERP --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json