Any ERPIndustry

Building products + AI

AI on Your ERP for Building Products Manufacturers

Short answer

AI for a building products ERP answers plain-English questions about configured orders, project quotes, material yield, delivery routes and product certifications from live data. It runs on infrastructure you control, so project pricing, dealer terms and specifications stay private, and drafts need human approval.

ERP: Any ERP, Epicor Prophet 21, Microsoft Dynamics 365, Infor CloudSuite, SAP S/4HANA
Industries: Building Products, Construction Materials
Written for: VP Operations

Discuss your Any ERP environment See use cases

Building products covers a wide range: windows, doors, cabinets, trusses, insulation, roofing, flooring, gypsum, cement and engineered wood. What they share is a link to the construction schedule. Orders are tied to projects, deliveries are tied to job sites, and a late truck can idle a crew.

Many products are configured to order. A window has dozens of options for size, glass, finish, grids and hardware, each driving a BOM and a cut list. Quoting from architectural schedules is a skilled job, and errors reach the job site as wrong-size units that cannot be returned.

Yield and waste matter as much as in any process industry: lumber, glass, aluminum extrusion and sheet goods are cut from stock, and nesting efficiency drives margin. Commodity price swings and seasonal demand make planning and pricing harder still.

Add certifications such as ICC-ES evaluation reports, NFRC ratings, fire and wind testing and environmental product declarations, and the data an estimator needs is spread across ERP, CPQ and document files. This page covers where private AI helps, and where it should not be trusted alone.

What usually gets in the way

The problems we hear most from vp operations teams running Any ERP.

Configured products multiply SKUs

Each option combination creates a BOM and routing. Pricing and lead time rules live in the CPQ or in the heads of a few estimators.

Quoting from project schedules is slow

Reading door and window schedules from drawings and turning them into quote lines is manual and error-prone.

Yield loss is invisible until month end

Cut-to-stock waste from glass, extrusion, lumber and sheet goods shows up in cost variances well after production.

Delivery windows drive customer satisfaction

Dispatchers juggle production completion, truck capacity and job site readiness with limited visibility into any one of them.

Certification records are scattered

Listing which products carry which test reports, ratings and declarations for a given project submittal means searching several folders.

Where AI earns its place in Any ERP

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

Quote line drafting from schedules

Estimators upload a door or window schedule and the agent drafts configured quote lines for review, flagging ambiguous or invalid option combinations.

Touches: Item configurations, CPQ rules, price lists, customer or project master, quote lines

Outcome: Cuts data entry on large schedules and leaves the estimator to resolve exceptions.

Order status and promise date lookup

Customer service asks where a project's units are in production and what ship date is realistic, getting an answer from live orders and work center load.

Touches: Sales orders, production orders, work center capacity, shipping schedules

Outcome: Reduces calls to the plant for status and gives consistent promise dates.

Cut-to-stock yield review

Compare planned and actual material use per job and flag orders with unusual offcut loss for engineering to review nesting rules.

Touches: Cut lists, material issues, scrap transactions, stock sizes

Outcome: Surfaces yield problems weekly rather than at month end.

Delivery planning assistant

Dispatchers ask which projects have complete product ready for shipment this week and how loads could be grouped by route and truck type.

Touches: Shipment staging, load planning, delivery addresses, order completeness flags

Outcome: Fewer partial deliveries and returned trips, depending on how well completeness is tracked.

Certification and submittal pack finder

Given a project and product list, retrieve the applicable test reports, ratings and declarations and list any gaps.

Touches: Item certification attributes, document attachments, project submittal requirements

Outcome: Shortens submittal preparation and reduces missing-document rework.

Commodity cost and price impact

Show which open quotes and price lists are most exposed to a material price move such as aluminum, lumber or resin.

Touches: Material costs, BOMs, quotes, price list rules, purchase contracts

Outcome: Helps sales and finance decide where to reprice or add escalation clauses.

Warranty and claim pattern analysis

Group warranty claims by product, supplier lot and installer region to give quality engineers leads on recurring failures.

Touches: Warranty claims, serial or lot records, service orders, supplier receipts

Outcome: Moves claim review from anecdote toward patterns that engineering can verify.

Reference architecture

The AI layer reads from your ERP, CPQ and document stores with read-only accounts, runs open-weight models on your infrastructure, and routes any drafted quote or order through human review before it is saved or sent.

1
Read-only connectors
Access to orders, BOMs, configurations, inventory, production and shipping via ERP APIs or reporting views, plus the CPQ's rule data where exposed.
2
Building products semantic layer
Maps terms like schedule, opening, configuration, cut list, yield and submittal to your tables and option codes.
3
Private model serving
Open-weight models on your hardware or private cloud so project pricing and dealer terms never reach a public service.
4
Retrieval and drafting agents
Grounded retrieval over ERP data and certification documents, with quote drafting agents that require an estimator's approval.
5
Governance
Role-mapped access, logging and versioned prompts and pricing rules.

Integration notes for your ERP team

Read configuration rules and option tables from the ERP or CPQ so drafted quote lines respect valid combinations.
Extract door and window schedules from PDFs or spreadsheets with a document pipeline and check them against item masters before drafting lines.
Join cut lists and stock sizes to material issues for yield analysis.
Use load planning and staging data for delivery questions; confirm completeness flags are reliable first.
Index certification documents with product family, standard, expiry and region attributes.
Keep quote drafting in a review queue; estimators save to the ERP or CPQ.
Use a reporting replica for large configuration queries.

Deployment options

On-prem at the plant or HQ

Manufacturers with strict dealer or contractor pricing confidentiality.

Models and indexes run locally with no outbound path for ERP content.

Private cloud tenant

Companies with cloud ERP and CPQ already in a controlled tenant.

Dedicated tenant under your own agreement.

Hybrid multi-plant

Groups with several plants serving regional markets.

Local retrieval per plant with central governance.

Compliance and data control

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

Building codes and evaluation reports (ICC-ES, local approvals)

The assistant retrieves the reports applicable to a product and project; engineering confirms suitability for the jurisdiction.

NFRC ratings, fire and wind testing, and EPDs

Rating and declaration documents are indexed with product and expiry metadata so gaps and renewals are visible.

Safety and environmental rules (OSHA, silica, VOC limits)

Safety data and regulatory documents are searchable, while compliance determinations stay with EHS staff.

Customer and contract confidentiality

Project pricing and specifications stay inside your boundary with logged, role-based access.

Where Netray fits

ERPray

Grounded questions and dashboards over orders, production, yield and delivery data, respecting ERP roles.

DataRay

Useful when schedules, drawings and certification files sit on file shares outside the ERP.

Custom build

Bespoke agents for schedule-to-quote drafting or yield analysis matched to your option rules.

How an engagement runs

Phase 1 . 3-4 weeks

Discovery

-Map of ERP, CPQ and document sources
-Review of configuration and pricing rule structure
-Ranked use cases by estimating and dispatch pain
-Deployment recommendation

Phase 2 . 6-10 weeks

Pilot

-Connectors against a non-production copy
-Order status and quote drafting use cases for pilot users
-Accuracy test against past quotes
-Logging and access review

Phase 3 . 4-6 weeks

Production

-Production cutover with read-only accounts
-Estimator approval workflow
-Runbook for new products
-Handoff to IT and sales operations

Phase 4 . Ongoing

Scale

-Additional product lines and plants
-Yield, delivery and claim analysis
-Accuracy review against quote outcomes
-Model refresh

Questions to ask any vendor, including us

A short list that separates real Any ERP AI work from a chatbot demo.

How does the system respect our configuration rules and invalid option combinations?
How reliable is schedule extraction from PDFs, and how are errors surfaced?
Who reviews a drafted quote before it is saved or sent?
Where does the model run, and is dealer and project pricing kept private?
How does it handle products configured in a separate CPQ?
Can it show the data behind a promise date?
How are certification documents kept current?
What do we keep if we stop?

Frequently asked questions

Can AI read our door and window schedules?

It can extract lines from PDFs or spreadsheets and match them to your configurable items, flagging anything ambiguous. Scanned drawings with poor quality need human checking, and every drafted quote is reviewed by an estimator before use.

Will it replace our CPQ?

No. The CPQ or ERP configurator stays the rules engine. The AI layer drafts inputs into it and answers questions around it, so invalid combinations are still caught by your existing logic.

Can it help with yield and waste?

Yes, by comparing planned and actual material use per job and highlighting outliers. Fixing nesting or cut rules is engineering work, but the analysis points to where to look.

How are promise dates produced?

The assistant reads production load, open orders and shipping schedules and explains a realistic date with the data behind it. It does not commit the date; customer service or planning does.

Does it handle commodity price swings?

It can show which quotes, price lists and contracts are most exposed to a material price change. Repricing decisions stay with sales and finance.

Is this practical for a regional manufacturer?

Yes, if orders and BOMs are reasonably clean. Start with order status or quote drafting on modest hardware. Value depends more on data quality than on company size.

Is our dealer pricing safe?

Running models locally keeps pricing and project data inside your boundary. Access follows ERP roles, and logs show who asked what, which supports your own confidentiality commitments.

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