# Infor LN AI for Engineer-to-Order Industrial Machinery Builders > Source: https://netray.co/erp-ai/infor-ln-industrial-machinery-eto-ai > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: AI for ERP ## Short answer AI for Infor LN in engineer-to-order machinery means a private model that reads project, engineering BOM, purchasing and shop floor data and answers questions such as which projects are slipping because of a late long-lead part, or what an engineering change does to committed cost. It runs on-prem next to LN, is read-only by default and shows the query behind every answer. ## Kind industry ## Erp Family infor ## Erp Products - Infor LN - Infor LN Project - Infor LN Engineering Data Management ## Industries - Industrial Machinery - Engineer-to-Order - Capital Equipment ## Region Global ## Persona VP Operations ## Eyebrow Infor LN + ETO machinery ## Intro A machinery builder that sells one-off or heavily configured lines lives on a different rhythm from a repetitive manufacturer. Each order is a project: a contract with milestones, an engineering structure that is still changing while purchasing is already committing to forgings, drives and controls, and a final assembly and factory acceptance test that depend on every one of those parts landing in the right sequence. Infor LN is a natural home for this work because Project, Engineering Data Management, Order Management, Shop Floor Control and Warehousing share one data model. The cost is that the answers a project manager needs sit across many sessions and tables. Finding out why a project is ten days behind often means opening the project budget, the planned order list, the purchase order lines and a spreadsheet kept by the buyer. Most machinery companies have two or three people who know how to navigate that. Everyone else exports data and builds a parallel tracker. That tracker is where errors, version drift and arguments in the weekly project review come from. The data is in LN already; the access path is the problem. This page describes what a private AI layer on LN does for ETO builders, which LN objects it touches, where it genuinely helps and where Infor's own tools or a good report are enough. It also covers the contract and export obligations that make many of these companies reluctant to send project data to a public cloud model. ## Pain Points ### Engineering changes land late in purchasing **Detail:** An engineering change that alters a gearbox or a control cabinet lands in the engineering BOM, but the buyer learns about it from a supplier call. Nobody can quickly see which open purchase order lines and which projects are touched. ### Project cost visibility lags reality **Detail:** Committed cost, actuals and estimate to complete live in different places. By the time the project review sees a margin problem, the material has been received and the labour spent. ### Long-lead items drive everything and are tracked by hand **Detail:** Castings, large bearings, motors and PLC hardware set the delivery date. Most teams track them in a side spreadsheet because LN planning views are organised by item or order, not by project critical path. ### Tribal knowledge about past projects **Detail:** Estimators price new quotes from memory of similar machines. The actual hours and cost of the last five comparable builds exist in LN but are not easy to retrieve and compare. ### Compliance files are assembled manually **Detail:** The technical file for CE marking, test records, material certificates and as-built BOM are gathered from several systems at shipment, usually under time pressure. ## Use Cases ### Project health Q&A for project managers **Description:** A project manager asks which of their projects have milestones at risk this month and why. The agent joins project budget, milestone dates, open order lines and receipt status, and lists the specific items driving each risk. **Erp Objects:** LN Project package, project budget and actuals, Order Management purchase order lines, Shop Floor Control production orders **Outcome:** Weekly project review preparation shrinks from hours of spreadsheet work to a reviewed answer; the risks are the same, found faster. ### Engineering change impact analysis **Description:** Given an engineering change, the agent lists affected BOM levels, open purchase orders for changed items, WIP orders already released and the projects that consume them, with committed cost exposed. **Erp Objects:** Engineering Data Management items and BOMs, tdpur400/tdpur401 purchase orders, tisfc001 production orders, project item links **Outcome:** Buyers hear about a change in hours rather than at the next supplier call, which reduces scrap and rework on changed parts. ### Long-lead watchlist by project **Description:** The agent keeps a ranked list of long-lead purchased items per project with promised date, receipt status and the assembly each one gates, and flags deviations from the project schedule. **Erp Objects:** Purchase order lines and promise dates, planned orders, project activities, tcibd001 item data **Outcome:** Late-part surprises are discovered earlier; the improvement depends on supplier promise dates being kept current in LN. ### Estimate to complete drafting **Description:** Using budget, actuals and open commitments, the agent drafts an estimate-to-complete narrative for each project and highlights the lines that moved since the last review for the project controller to confirm. **Erp Objects:** Project cost lines, commitments, labour hour registrations, budget revisions **Outcome:** Controllers spend time on judgement rather than assembly; figures remain the controller's responsibility. ### Historical analogue search for quoting **Description:** Estimators describe a new machine and the agent retrieves comparable past projects, with actual material, labour hours and delivery duration versus estimate, and the variance drivers noted in project documents. **Erp Objects:** Completed project cost history, item and BOM structure, routing hours, attached project documents **Outcome:** Quotes rest on actuals from similar builds, with ranges rather than false precision. ### Technical file assembly support **Description:** At shipment the agent gathers as-built BOM, serial and lot numbers, inspection results and certificates for the machine and produces a checklist of what is present and missing against the customer and CE file requirement. **Erp Objects:** As-built structure, serial number tracking, inspection and quality records, attached documents **Outcome:** Fewer last-day scrambles at shipment; completeness is verified by a person before release. ### Shop floor capacity versus project dates **Description:** Planners ask whether assembly and test bay capacity supports the milestone dates across all open projects and where two projects collide on the same bay or skilled team. **Erp Objects:** Work centers, routing operations, production order schedules, project milestones **Outcome:** Collisions are visible weeks ahead; the fix is still a scheduling decision by people. ## Architecture **Overview:** The AI layer sits beside Infor LN and reads through governed paths: ION APIs and BOD messages for documents, and a read-only replica or reporting copy of the LN database for analytical queries. A model served on the customer's own GPUs interprets questions, generates a query against an allow-listed semantic layer of LN tables, and returns an answer with the query shown. Writes, such as a changed promise date, go through LN business logic or APIs only after human approval. **Layers:** #### Source access **Detail:** ION API Gateway and BODs for transactional objects, plus a read-only replica of the LN database for analytical joins. No direct writes to LN tables. #### Semantic layer **Detail:** A curated map of LN tables and domains for projects, items, purchase and production orders with business names, so the model works with meaning rather than raw table codes. #### Retrieval over documents **Detail:** Project documents, supplier correspondence, test reports and certificates indexed in a private vector store, tied to the project and item identifiers in LN. #### Private model serving **Detail:** An open-weight model such as Llama, Qwen or Mistral class served with vLLM on customer GPUs or a sovereign private cloud. No prompts or project data leave the boundary. #### Control and audit **Detail:** LN roles and company scoping are mirrored; every question, query and answer is logged; any write-back requires named human approval. ## Deployment Options ### On-prem next to LN **Fit:** Builders with defense, nuclear or customer-restricted project data **Detail:** Model and index run on customer GPUs inside the same network as the LN application servers. Works with LN on-prem or in a customer-controlled private cloud. ### Private cloud or sovereign region **Fit:** Multi-site builders with EU data residency needs **Detail:** Dedicated GPU capacity in a private tenancy in the customer's region, connected over private links to the LN environment. ### Air-gapped **Fit:** Projects under export control or classified-adjacent contracts **Detail:** Fully disconnected deployment with models and updates delivered by controlled media; replica refreshed on a defined schedule. ## Compliance ### The AI can help collect and check technical file contents from LN and attached records. The manufacturer remains responsible for conformity assessment and the declaration; the tool is an assistant to the people who sign. **Framework:** EU Machinery Directive 2006/42/EC and Regulation (EU) 2023/1230 ### On-prem or air-gapped deployment keeps controlled technical data inside the customer's boundary. Access follows LN roles; the customer determines who may see controlled projects. **Framework:** Export controls (EAR, EU dual-use, ITAR where applicable) ### Query logs and answers with source references support traceability of decisions; document control stays in the customer's quality system. **Framework:** ISO 9001 documented information ### Project data is not used to train shared models. Index and logs live in the customer's environment and can be deleted on project closure. **Framework:** Customer contract confidentiality ## Integration Notes - Use ION API Gateway with a service account scoped to the minimum LN company and package access; avoid user credentials in the agent. - Prefer a read-only replica or reporting database for joins across project, purchasing and production tables to protect LN performance. - Map Project package budget, commitment and actual structures into the semantic layer before pilot; this is where most ETO questions live. - Link engineering items to purchase and production orders via item and project keys; document any customer-specific table extensions you add. - Attach documents by project and item identifiers so retrieval can cite the exact drawing, certificate or test record. - Respect LN authorizations: the agent filters by company, project and role, and never returns data the asking user could not open in LN. - Keep any write-back, such as promise date updates, as a drafted action confirmed by the buyer inside LN. ## Product Fit ### ERPray **Why:** Natural-language answers, project dashboards and agents over live LN data with the query shown and read-only by default. ### Custom build **Why:** Bespoke retrieval over project documents, estimating history and technical file checks that are specific to the builder. ## Engagement ### Discovery and data map **Duration:** 2 to 3 weeks **Deliverables:** - Inventory of project, BOM and purchasing questions asked today - LN table and extension map - Security and data-boundary review - Pilot success criteria ### Foundation **Duration:** 3 to 5 weeks **Deliverables:** - Read-only replica and ION access - Semantic layer for projects and orders - Private model environment - Document index for pilot projects ### Pilot with project managers **Duration:** 4 to 6 weeks **Deliverables:** - Project health and long-lead assistants - Answer review log with accuracy tracking - Role and company scoping tests - Pilot readout ### Extend and operate **Duration:** Ongoing **Deliverables:** - Engineering change impact agent - Technical file checklist support - Monitoring and model refresh plan - Runbook for the internal team ## Buyer Questions - Does the tool respect LN company, project and role authorizations, and how is that tested? - Can we see the exact query behind every number the AI gives a project manager? - Where do project documents and prompts live, and can they be deleted when a project closes? - What happens to answers when we upgrade LN or add table extensions? - How are Project package commitments handled when purchase orders are partially received? - Which actions can the AI take in LN, and who approves them? - How do you measure accuracy on our own historical project questions before go-live? - What load does the analytical access place on production LN? ## Faq ### Why is ETO machinery harder for AI than repetitive manufacturing? **Answer:** Because the questions are cross-object and project-scoped. A repetitive plant asks about items and lines; an ETO builder asks how a design change, a late casting and a test bay booking combine to move one project. That requires joining project, engineering, purchasing and production data correctly, which is why a semantic layer matters more than the model. ### Can AI replace the project controller's estimate to complete? **Answer:** No. It can assemble budget, actuals and open commitments and draft a narrative, which saves preparation time. The controller applies judgement about risks, supplier claims and scope changes, and signs off the number. Treat it as a drafting aid with sources, not an autonomous forecast. ### Is LN's own reporting enough for this? **Answer:** Often partly. If your questions are stable, well-built LN reports or Infor Birst dashboards may be cheaper and sufficient. AI adds value when questions are ad hoc and cross-package, such as change impact on committed cost, or when answers must combine ERP data with project documents. ### Does it work with customized LN? **Answer:** Yes, if the customization is mapped. Table extensions and custom sessions need to be documented in the semantic layer. Heavily customized estimating or cost logic should be reviewed with your LN team first so the AI uses the same definitions your project controllers do. ### How do we keep export-controlled project data safe? **Answer:** Run the model and index inside your own boundary, on-prem or air-gapped, and mirror LN access rules so only authorized users see controlled projects. Keep logs local. Your export compliance function still decides who is authorized; the architecture just avoids sending data to a third party. ### What accuracy should we expect? **Answer:** It depends on data quality and mapping. Questions that map to well-kept fields, such as promise dates or project budget lines, can be answered reliably. Questions that depend on free-text or stale data will not. We measure against your own past questions in the pilot and report the failure modes openly. ### How long before project managers use it? **Answer:** A focused pilot with a handful of projects typically takes 8 to 12 weeks from kickoff, depending on data access and how many customizations need mapping. Adoption depends on the first answers being right, so the pilot scope is deliberately narrow. ## Search terms infor ln engineer to order, infor ln industrial machinery, infor ln project manufacturing ai, eto manufacturing erp ai, infor ln engineering change, machinery builder erp ai, infor ln project cost, private ai for infor ln, infor ln on-prem llm, capital equipment erp ai, infor ln long lead items, infor ln estimate to complete --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json