# AI for Oracle EBS in Aerospace and Defense Manufacturing > Source: https://netray.co/erp-ai/oracle-ebs-aerospace-defense-ai > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: AI for ERP ## Short answer Aerospace and defense manufacturers running Oracle EBS's Project Manufacturing and Project Costing modules need AI that respects export control segregation, not a generic ERP copilot. A private, on-prem model grounded on project, serial, and configuration data can answer operational questions without creating a new export control exposure. ## Kind persona ## Erp Family oracle ## Erp Products - Oracle E-Business Suite 12.2 - Oracle Project Manufacturing - Oracle Project Costing ## Industries - Aerospace - Defense - Electronics ## Region United States ## Persona VP Operations ## Eyebrow EBS + ITAR-aware AI ## Intro If you run Oracle E-Business Suite in an aerospace or defense manufacturing environment, you are almost certainly using Project Manufacturing and Project Costing to track work by contract, and you are almost certainly tracking serial and lot control tightly enough to answer a customer or DCMA audit question on short notice. That combination, project-based accounting plus serialized traceability, is exactly what makes a generic, cloud-hosted AI copilot the wrong tool here, and exactly what a properly scoped private AI layer is built to handle. The operational pain is familiar to any VP of Operations in this space: a program manager needs to know where a specific serialized assembly is in the process and what its component genealogy looks like, a planner needs a status read across dozens of open project-funded work orders, and a quality engineer needs to pull configuration and traceability data fast when a supplier notifies of a potential nonconformance. All of that data lives in EBS's Project Manufacturing, Inventory, and Quality tables, but getting to an answer today usually means a call to whoever knows the right report. The constraint that changes the AI conversation here is export control. Technical data tied to a defense article or ITAR-controlled item cannot be exposed to a foreign national through an AI system any more than through any other channel, and a public or cloud-hosted model API is a plausible path for that kind of exposure if it is not designed against. That is the reason this page treats architecture and access control as inseparable from the use cases, not an afterthought bolted on at the end. This page is written for a VP of Operations weighing whether AI is realistic in an EBS environment carrying project, serial, and configuration control data under export restrictions. It focuses on what changes when ITAR and DFARS requirements are the starting constraint rather than a compliance checkbox added later. ## Pain Points ### Project and serial status questions route through too few people **Detail:** Only a handful of planners and program analysts can quickly answer 'where is this serialized unit and what is its build status,' and they are usually the bottleneck on multiple programs at once. ### Configuration and genealogy lookups are slow under audit pressure **Detail:** When a customer or DCMA auditor asks for as-built configuration and component genealogy on short notice, pulling it together from EBS Project Manufacturing and Quality tables takes hours, not minutes. ### Export control makes every new tool a review cycle before it starts **Detail:** Any proposed system touching technical data has to clear an export control review before a pilot can even begin, which correctly slows down well-intentioned but poorly scoped AI ideas. ### Supplier nonconformance response needs fast traceability, not a report queue **Detail:** When a supplier flags a potential issue on a lot or serial number, operations needs to trace every affected assembly and open order quickly, and today that means a manual cross-reference across several EBS screens. ### New program ramp-up strains the same small group of EBS experts **Detail:** Standing up AI for a new contract competes with the same limited pool of people who understand both EBS Project Manufacturing configuration and the program's specific requirements. ## Use Cases ### Serialized unit status and build genealogy **Description:** Answer where a specific serial number is in the build process and what its component genealogy looks like, without a manual cross-reference. **Erp Objects:** WIP_ENTITIES, MTL_SERIAL_NUMBERS, MTL_GENEALOGY, MTL_MATERIAL_TRANSACTIONS **Outcome:** Cuts genealogy lookup time from a multi-screen manual trace to a direct answer, particularly valuable under audit or customer inquiry pressure. ### Project work order and milestone status **Description:** Summarize open project-funded work orders and milestone status across a program for a program manager or VP Operations review. **Erp Objects:** PA_PROJECTS_ALL, PA_TASKS, WIP_ENTITIES linked by project reference **Outcome:** Gives program leadership a status read without waiting for the next scheduled program review. ### Supplier nonconformance impact trace **Description:** Given a supplier lot or serial number flagged for a potential issue, trace every affected assembly, work order, and open customer order. **Erp Objects:** MTL_LOT_NUMBERS, MTL_GENEALOGY, PO_HEADERS_ALL, OE_ORDER_LINES_ALL **Outcome:** Compresses a nonconformance impact assessment from a multi-day manual trace to a same-day answer, which matters when a stop-ship decision is on the table. ### Project cost and burn rate summary **Description:** Answer budget-to-actual and burn rate questions for a specific contract or task, grounded in Project Costing data. **Erp Objects:** PA_BUDGET_LINES, PA_EXPENDITURE_ITEMS_ALL, PA_COST_DISTRIBUTION_LINES_ALL **Outcome:** Gives program financial leads a faster read between formal earned value or cost review cycles. ### Quality nonconformance and disposition drafting **Description:** Draft the first pass of a nonconformance report or disposition note grounded in the specific unit's history and prior similar dispositions. **Erp Objects:** Oracle Quality collection elements, MTL_MATERIAL_TRANSACTIONS, PA_TASKS **Outcome:** Reduces the drafting time for a quality engineer working through a backlog of nonconformance reports. ### As-built configuration pull for audit response **Description:** Assemble the as-built configuration and traceability record for a specific unit on request, in the format an auditor or customer typically expects. **Erp Objects:** MTL_GENEALOGY, BOM_STRUCTURES, WIP_OPERATIONS **Outcome:** Shortens audit response time from hours of manual assembly to a reviewed, ready document. ### Open order and delivery schedule status for program reviews **Description:** Summarize open customer order status and delivery schedule against project milestones for internal program reviews. **Erp Objects:** OE_ORDER_HEADERS_ALL, OE_ORDER_LINES_ALL, PA_TASKS **Outcome:** Gives program management a consistent, current status view instead of a manually assembled slide the week before a review. ## Architecture **Overview:** The architecture is built around export control segregation first: technical data flagged as ITAR-controlled is scoped, logged, and never exposed to a model or user outside the cleared boundary. **Layers:** #### EBS connector layer **Detail:** A read-only database account against a Data Guard standby, scoped by responsibility and organization, with export-controlled projects and items explicitly flagged and excluded from any query path a non-cleared user or model instance can reach. #### Semantic and data layer **Detail:** A business-term mapping over Project Manufacturing, Inventory, Quality, and Project Costing tables, built to preserve the same access segregation EBS itself enforces by responsibility and project. #### Model serving layer **Detail:** An open-weight model served on GPUs physically inside the customer's controlled facility network, with no path to a public model API, matching the same boundary already used for other export-controlled systems. #### Retrieval and agent layer **Detail:** RAG grounded on the scoped semantic layer, with genealogy tracing and nonconformance impact queries as first-class agent capabilities; any write, such as a disposition note, requires human approval and is logged against the requesting user. #### Governance and audit layer **Detail:** Every query, its scope, and the requesting user's clearance and responsibility are logged, giving export control compliance officers the same kind of evidence they already require for other systems touching controlled data. ## Deployment Options ### Air-gapped on-prem **Fit:** Programs where technical data is subject to ITAR and no path to any external network is acceptable **Detail:** Model, database views, and application run entirely inside the facility network with no outbound internet path, matching how most A&D suppliers already run EBS itself. ### Segregated private cloud **Fit:** Suppliers with a compliant private cloud environment already accredited for CUI or controlled data **Detail:** The same architecture hosted in a customer-controlled, accredited cloud environment rather than a shared or public AI service, where the facility does not run its own GPU infrastructure. ### Hybrid with strict scope separation **Fit:** Organizations with both ITAR-controlled and purely commercial programs in the same EBS instance **Detail:** Export-controlled projects and their associated data are excluded from any cloud-adjacent path entirely, while unrestricted commercial data can use a broader deployment if genuinely needed; the two are architecturally separated, not just access-controlled. ## Compliance ### Projects and items flagged as ITAR-controlled in EBS are excluded from any query path that is not both on-prem and restricted to U.S. person users with a need to know, avoiding any deemed-export exposure through the AI layer itself. **Framework:** ITAR (22 CFR 120-130) ### Covered defense information referenced in EBS stays inside the same facility network and access boundary already scoped for the safeguarding clause, with the AI layer adding no new egress path. **Framework:** DFARS 252.204-7012 and NIST SP 800-171 ### Because the model, data, and query logs all stay inside the existing assessed enclave, the AI layer is designed to fall within the current CMMC scope rather than requiring a new assessment boundary. **Framework:** CMMC 2.0 Level 2 ### Genealogy and configuration queries are built to reproduce the same traceability evidence AS9100D audits already expect, sourced directly from EBS records rather than a separate, harder-to-reconcile system. **Framework:** AS9100D traceability requirements ### Query logs tied to user responsibility and project flags give the empowered official or export control officer a reviewable record of who asked what about which controlled project. **Framework:** Export control compliance program ## Integration Notes Flag export-controlled projects and items explicitly in EBS (via project attributes or a dedicated classification field) before building the semantic layer, so exclusion happens at the data layer, not after a query has already run. Use EBS's existing responsibility and organization security to scope what a given user's AI queries can see, matching how the same user's manual access is already scoped. Keep the model and all inference physically inside the facility network for any project touching ITAR-controlled technical data; do not rely on contractual terms alone with a cloud AI vendor. Build genealogy and traceability queries against MTL_GENEALOGY and related tables carefully, since these joins are often the most complex part of the EBS schema to translate correctly. Coordinate the AI project's access review with the same export control officer or empowered official who reviews other system access, rather than treating it as a standard IT security review. Log every query's scope, not just its result, so export control review can confirm what data was reachable, not only what was returned. Plan for Project Costing's expenditure and cost distribution tables separately from Project Manufacturing's WIP tables; they answer different questions and usually need different access scoping. ## Product Fit ### Custom build **Why:** The export control segregation and genealogy-specific query design this use case needs go well beyond a generic EBS connector, making this a purpose-built engagement from the semantic layer up. ### ERPray **Why:** ERPray's role-aware, query-visible architecture is the governance pattern this kind of project extends, since export control review depends on being able to see exactly what a query could and did access. ## Engagement ### Discovery **Duration:** 3-4 weeks **Deliverables:** - Export control classification review of in-scope projects, items, and technical data - Coordination with the empowered official or export control officer on access boundaries - Inventory of Project Manufacturing, Inventory, Quality, and Project Costing tables in scope ### Pilot **Duration:** 8-10 weeks **Deliverables:** - Semantic layer with export control exclusion built in from the start - Genealogy and project status Q&A agent deployed on-prem to a cleared pilot group - Query and scope logging reviewed by export control compliance ### Production **Duration:** 8-12 weeks **Deliverables:** - Rollout across cleared program teams with responsibility-based access - Audit-ready configuration and traceability reporting workflow - Documented export control review sign-off for the deployed system ### Scale **Duration:** Ongoing **Deliverables:** - Additional programs and projects brought into scope with their own classification review - Nonconformance impact tracing extended to supplier quality workflows - Periodic re-review of export control classifications as programs evolve ## Buyer Questions - How does the vendor identify and exclude ITAR-controlled projects and items before a query can reach them? - Does any inference happen outside a facility network I control, even briefly, for controlled data? - How does the AI layer's access scoping map to my existing export control access reviews? - Can my export control officer review exactly what data a given query could have accessed, not just what it returned? - How does the vendor handle EBS's genealogy and traceability tables specifically, versus generic inventory data? - What happens if a project's export control classification changes after the AI layer is deployed? - Is this covered under my existing CMMC assessment scope, or does it extend the boundary? - What is the vendor's experience with Project Manufacturing and Project Costing specifically, not just EBS in general? ## Faq ### Can AI safely touch ITAR-controlled data in Oracle EBS? **Answer:** Yes, if it is architected to keep that data and the model itself entirely inside a facility network you control, with export-controlled projects and items explicitly flagged and excluded from any path a non-cleared user could reach. It is not safe if the model or any part of the pipeline touches a public or shared cloud AI service. ### Does this require a separate export control review from my other systems? **Answer:** It should be reviewed by the same empowered official or export control officer who reviews other systems touching your technical data, using the same classification and access logic, rather than a separate, parallel process built just for AI. ### How does this handle genealogy and traceability specifically? **Answer:** By reading directly from EBS's genealogy tables (MTL_GENEALOGY and related structures) and Project Manufacturing WIP data, so a query can trace a serialized unit's build history and component genealogy the same way a manual investigation would, just faster. ### What if my EBS instance has both ITAR and non-ITAR programs? **Answer:** The architecture separates them at the data layer, not just the access layer: export-controlled projects are excluded from any query path that is not fully on-prem and restricted to cleared users, while unrestricted commercial programs can use a less restrictive deployment if that is genuinely appropriate for your environment. ### Can this help with a DCMA or customer audit? **Answer:** Yes, by assembling as-built configuration and traceability records faster than a manual pull across multiple EBS screens. The output should still go through the same review your team already applies before anything leaves the building for an auditor or customer. ### Does this fall inside my existing CMMC Level 2 assessment scope? **Answer:** It is designed to, by keeping the model, data, and logs inside the same enclave already scoped for your CMMC assessment. Whether it actually does depends on your specific system boundary documentation, which should be reviewed with your assessor, not assumed. ### What is a realistic starting point for a program like this? **Answer:** Serialized unit status and genealogy lookup for a single active program is a common starting point, since the tables involved are well defined and the value to program management and quality is immediate and easy to measure against the current manual process. ## Search terms Oracle EBS aerospace defense AI, Oracle EBS ITAR AI, on-prem AI for Oracle EBS defense, EBS Project Manufacturing AI, Oracle EBS traceability AI, Oracle EBS AI aerospace, ITAR compliant AI Oracle EBS, Oracle EBS serial genealogy AI, Oracle EBS CMMC AI, private LLM Oracle EBS defense, Oracle EBS AI government contractor, Oracle EBS AS9100 AI --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json