Any ERPIndustry

Machinery builders + ETO ERP

AI for ERP in Industrial Machinery and Engineer-to-Order Manufacturing

Short answer

AI for an industrial machinery ERP reads project costs, engineering and manufacturing BOMs, purchase orders and milestone billing together, so a project manager can ask where a machine really stands against budget and delivery. It runs on open-weight models inside your own network, because machine designs, customer specifications and quotes are the intellectual property of an ETO business.

ERP: Infor SyteLine, Epicor Kinetic, SAP S/4HANA, Oracle NetSuite, Microsoft Dynamics 365
Industries: Industrial Machinery, Engineer-to-Order, Capital Equipment
Written for: VP Operations

Discuss your Infor SyteLine environment See use cases

A machinery builder does not run a factory the way a repetitive manufacturer does. Each order is a project: a quote built from a prior similar machine, an engineering release that changes after the customer review, long-lead castings and drives bought before the design is frozen, a build in a bay rather than on a line, then factory acceptance testing, shipment, site commissioning and a warranty tail that can last two years. The ERP holds the money and the parts, but the story of a machine is scattered across it.

The same project lives in several places at once. The engineering BOM sits in the CAD or PLM system, the manufacturing BOM in the ERP, the schedule in a project module or a spreadsheet, and the customer's change requests in email. Project managers spend a large part of every week reconciling these to answer simple questions: is the machine going to ship on the promised date, what is the cost to complete, and which open change order is about to hit margin.

Margin is the other pressure. Percent-of-completion revenue recognition under ASC 606 or IFRS 15 only works if the estimate to complete is honest, and the estimate is only honest if someone has looked at open commitments, unreleased engineering and rework since the last review. Quotes for the next machine are built from memory of the last one, and the lessons from overruns rarely make it back into the estimate.

This page describes what private AI on top of an ETO ERP can and cannot do for a machinery builder: which objects it reads, where it saves project managers and estimators real time, and why running it on your own infrastructure matters when the machine design is the product you are selling.

What usually gets in the way

The problems we hear most from vp operations teams running Infor SyteLine.

Cost to complete is a guess made once a month

Project accountants roll up actuals, open POs and engineering hours by hand, then ask the project manager for an estimate to complete. By the time the number is reviewed, a change order or a late casting has already moved it.

EBOM and MBOM drift after release

Engineering revises a frame or a guarding assembly after purchasing has ordered parts. Buyers learn about the change from a shop floor shortage, not from a release notice, and obsolete parts end up in stores.

Quotes rebuilt from the last similar machine

Estimators copy a prior quote and adjust by feel. Actual hours by work center and actual bought-out costs from finished projects rarely flow back, so the same underestimate repeats.

Long-lead items decide the ship date

Gearboxes, servo systems, castings and weldments carry lead times of months. Nobody has a live view of which long-lead lines are still unordered, unconfirmed or late for projects already promised to customers.

Commissioning and warranty knowledge stays in the field

Punch lists from factory acceptance tests, site acceptance issues and warranty claims sit in emails and service tickets. The next machine of the same model is built without that history.

Where AI earns its place in Infor SyteLine

Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.

Project cost-to-complete assistant

A project manager asks where a machine stands against budget. The assistant pulls actual cost, open commitments and remaining labor, flags lines with no estimate, and shows the query behind the number.

Touches: Project, WBS or task, project cost ledger, purchase order lines, labor transactions, change orders

Outcome: Replaces a manual month-end roll-up with an on-demand view; estimates are reviewed more often because the first pass is no longer a day of spreadsheet work.

EBOM to MBOM change impact

When engineering releases a revision, the assistant lists affected purchase orders, work orders and stock for the project and drafts a notice to the buyer and the production lead for review.

Touches: Item revision, BOM header and lines, ECN or ECO record, purchase order lines, work order components

Outcome: Buyers hear about a change before parts arrive, which cuts obsolete purchases and rushed re-orders.

Quote assistant grounded in past projects

An estimator describes a new machine; the assistant retrieves the closest prior projects, shows quoted versus actual hours and bought-out cost by area, and proposes a starting estimate with the evidence listed.

Touches: Quote, project history, routing and work center actuals, purchase history by commodity

Outcome: Starting estimates reflect real outcomes of similar builds, so repeat overruns on the same sub-assembly become visible during quoting.

Long-lead item watchlist

The agent scans open projects for long-lead lines that are unplaced, unacknowledged by the supplier or promised after the build start, and ranks them by the schedule they threaten.

Touches: Purchase requisitions, PO lines with promise dates, supplier acknowledgements, project milestones

Outcome: Moves long-lead exceptions from a weekly meeting discussion to a daily ranked list the buyer works from.

Milestone billing and retention tracking

Finance asks which milestones are complete but not yet invoiced, and which retention amounts are due. The assistant joins project progress to the billing schedule and drafts the invoice request.

Touches: Billing milestones, project progress, AR invoices, retention, deposit receipts

Outcome: Shortens the gap between a milestone being reached and the invoice going out, which matters on deals with large deposits and staged payments.

FAT and SAT punch list summarizer

Test notes and punch items are summarized by machine and by subsystem, linked to the work order or service call that closed each, with recurring issues across the same model highlighted.

Touches: Quality records, nonconformance, service orders, work orders, serial numbers

Outcome: Recurring defects on a model family are visible to engineering, so fixes go into the design instead of into the next commissioning trip.

Spare parts quotation from the as-built record

A service coordinator enters a serial number; the assistant retrieves the as-built BOM and proposes a recommended spares list and a draft quote, subject to review.

Touches: As-built BOM, serial and lot records, item cross-references, price lists

Outcome: Spares quotes start from the machine as actually shipped, not from the standard model BOM, which reduces wrong-part orders.

Reference architecture

The AI layer sits beside the ERP and any PLM or CAD data vault, reads through supported APIs or read-only database views, and answers inside the project and cost permissions the ERP already enforces. Machine designs and quotes are never sent to a public model.

1
ERP and PLM connectors
Read-only access to projects, BOMs, purchasing, work orders and the cost ledger through the ERP's REST or SOAP services or approved SQL views, plus metadata and revision data from the PLM or vault where one exists.
2
Semantic layer
Maps your project structure, work breakdown codes, commodity classes and milestone conventions to business terms, so a question about the 'frame weldment' resolves to the right assemblies on the right projects.
3
Model serving
Open-weight models from the Llama, Qwen, Mistral or Gemma families served with vLLM or Ollama on GPUs in your data center or private cloud, sized for a few dozen concurrent project and engineering users.
4
Retrieval and agents
Retrieval over live ERP queries plus indexed quotes, specifications, FAT reports and ECN documents; agents draft notices and invoices but never post transactions without a named approver.
5
Governance and audit
Every question, generated query and drafted action is logged to the requesting user with the role filters applied, so project and customer confidentiality in the ERP carries through.

Integration notes for your ERP team

Project cost and commitment data are read from the ERP's project accounting tables or services, not from exported spreadsheets, so numbers match what finance sees.
PLM and CAD vault metadata (item, revision, release state) is joined to ERP item masters through the part number and revision key, with mismatches reported rather than silently resolved.
ECN and ECO documents are indexed as text so the assistant can explain why a revision was made, not only that it was made.
Quote history needs a stable link from quote line to project to actuals; where that link is missing, discovery work creates it from project numbers and customer order references.
Serial-number and as-built BOM structures vary by ERP; the connector is mapped to your configuration of serial, lot and configuration records.
Write-backs are limited to drafts: a notice, an email, an invoice request or a requisition for approval through the ERP's own workflow.
Document sources such as FAT reports and test certificates are read from the file share or document system with the permissions of the requesting user.

Deployment options

Air-gapped on-prem

Builders of defense, nuclear, energy or semiconductor equipment under customer or export-control restrictions on where designs may go.

Models, indexes and connectors run on your hardware with no outbound path, and updates arrive through a controlled transfer process.

Private or sovereign cloud

Mid-size machinery builders without a GPU footprint who still must keep specifications inside their own tenant.

Dedicated GPU capacity in your cloud account or a regional provider, reached over private networking from the ERP.

Hybrid across plants

Groups with several plants or an acquired product line on a different ERP.

A shared governance layer and model service with per-ERP connectors and per-site data residency, rather than a forced data consolidation.

Compliance and data control

How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.

Customer confidentiality and NDA flow-down

Machine specifications and drawings stay on infrastructure you control, which lets you answer customer security questionnaires with a concrete network and data path description.

EU Machinery Directive 2006/42/EC and Regulation (EU) 2023/1230

The assistant can retrieve technical file elements, risk assessment references (ISO 12100) and declaration records already stored in your systems so engineers find them faster. Conformity assessment itself remains with your engineering and notified body process.

ASC 606 and IFRS 15 revenue recognition

The assistant surfaces the data behind estimates to complete and shows how a figure was built; the accounting judgment and approval stay with your finance team and auditors.

Export controls (EAR, ITAR) where applicable

On-prem or in-region deployment keeps controlled technical data inside your boundary, supporting your own compliance program. Your compliance officer remains responsible for the classification and licensing decisions.

Where Netray fits

ERPray

Project managers, buyers and finance staff get grounded natural-language answers and dashboards over live ERP data, with the query shown, read-only by default.

Custom build

Quote assistants trained on your own project history, ECN impact agents and punch-list summarizers usually need bespoke retrieval and workflow design around your engineering process.

How an engagement runs

Phase 1 . 2-4 weeks

Discovery

-Map of project, BOM, cost and billing objects in the ERP and PLM
-Ranked list of project-manager, estimator and buyer questions
-Data quality review of quote-to-actual linkage
-Deployment and security plan

Phase 2 . 6-10 weeks

Pilot

-Read-only connector on a non-production copy
-Cost-to-complete and long-lead use cases live for a pilot team
-Answers checked against finance and project reviews
-Logging and role mapping validated

Phase 3 . 4-6 weeks

Production

-Production cutover with a read-only service account
-Approval workflow for drafted notices and invoices
-Runbook and monitoring
-Training for project and estimating staff

Phase 4 . Ongoing

Scale

-Quote assistant on closed-project history
-FAT and warranty learning loop to engineering
-Additional plants or product lines
-Model and accuracy review each quarter

Questions to ask any vendor, including us

A short list that separates real Infor SyteLine AI work from a chatbot demo.

Does the system read project cost from the same ledger finance uses, or from a copy that can disagree?
How does it link a quote to the actual cost of the finished machine, and what if that link is incomplete in our data?
Where do the model and indexes run, and can you show the network path for drawings and specifications?
How are PLM revisions matched to ERP item revisions, and what happens on a mismatch?
Can the assistant ever post a transaction, or only draft one for a named approver?
How are project-level permissions and customer confidentiality enforced in answers?
What does a wrong cost-to-complete answer look like, and how will we detect it during the pilot?
What do we keep if we stop using the service: connectors, prompts, logs and indexes?

Frequently asked questions

Can AI estimate cost to complete on an engineer-to-order project?

It can assemble the inputs quickly: actuals, open commitments, remaining labor and unreleased engineering, with the query shown. It should not replace the project manager's judgment about risk. The value is that the review starts from a complete, current picture instead of a day of manual roll-up.

Does this replace our PLM or the ERP project module?

No. It reads from both and answers across them. Release control stays in the PLM, and costing and billing stay in the ERP. The assistant helps people find, compare and summarize what those systems already hold, and drafts follow-up actions for approval.

Is the ERP vendor's own AI enough for a machinery builder?

For common tasks inside one ERP it may be. A private layer makes sense when answers must span ERP, PLM and documents, when designs cannot go to a vendor cloud, or when you need the generated query and role filtering visible for audit.

How good can a quote assistant be with our history?

It depends on how well past quotes link to actual outcomes. Where that link exists it can show quoted versus actual hours and bought-out cost for similar machines. Where it does not, the first deliverable is repairing the link; the model cannot invent history your data lacks.

Can it help with CE marking documentation?

It can retrieve and summarize records you already keep, such as risk assessments, declarations and test reports, so engineers find them faster. It does not perform conformity assessment, and it does not replace your technical file process or a notified body where one is required.

What hardware do we need?

A pilot for a few dozen users commonly runs on one or two data-center GPUs with a quantized open-weight model. Sizing depends on concurrency and on how much document retrieval you want. Our sizing tools give a starting estimate before any purchase.

Which ERPs does this work with?

Any ERP with a supported API or approved read-only database access. Typical machinery builder platforms include SyteLine, Kinetic, SAP, NetSuite and Dynamics, plus industry-specific ETO systems. The semantic layer is built per customer, so the ERP brand matters less than data quality.

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