Specialist ERPsIndustry

DELMIAworks + injection molding

AI for DELMIAworks in Injection Molding: Resin, Molds, Presses and Scrap From Your Own Data

Short answer

AI for DELMIAworks in an injection molding plant connects a private language model to production, mold, resin and quality data so planners, process engineers and plant managers can ask plain-English questions about press scheduling, cycle-time variance, regrind use, mold maintenance and scrap, with the underlying query shown. It reads the DELMIAworks data read-only, runs on-prem or in a private cloud, and keeps customer part and pricing data inside your boundary.

ERP: DELMIAworks, DELMIAworks ERP, DELMIAworks MES
Industries: Plastics, Injection molding, Custom molders, Packaging, Automotive components
Written for: Operations Manager or Plant Manager at a plastics injection molder

Discuss your DELMIAworks environment See use cases

DELMIAworks, formerly IQMS, has long been a mainstay for plastics manufacturers because it models the way a molder actually works: presses as work centers, molds as tooling with cavities and shot counts, resin and regrind as controlled materials, and production monitoring tied to machine cycles. That fit is why many custom molders and captive molding operations run on it.

A molding plant generates a great deal of fine-grained data. Every press reports cycles, every mold has a maintenance interval, every job has a standard cycle time and a scrap rate, and every resin lot has a supplier, a drying requirement and, often, a customer-mandated virgin to regrind ratio. The questions managers ask are sharp and fast: why did press 14 miss its standard cycle on the last three shifts, which molds are overdue for preventive maintenance, which jobs are consuming the most regrind.

Answering them usually involves the scheduler, the process engineer and someone who knows the reports, and by the time the answer is assembled the shift has changed. Meanwhile customers in automotive, medical and consumer packaging want traceability to the resin lot, which adds documentation burden to every run.

This page describes what AI on DELMIAworks looks like for an injection molder: what it reads, how it connects to the Oracle-based system, where it delivers real value, and where the existing DELMIAworks reporting or a simple spreadsheet is enough. It is written for the operations, process or IT leader deciding whether a pilot is warranted.

What usually gets in the way

The problems we hear most from operations manager or plant manager at a plastics injection molder teams running DELMIAworks.

Cycle-time and OEE gaps are noticed after the shift

Presses running slower than standard, extra downtime at changeover, and rising scrap are visible in the data but are often reviewed the next morning, after the lost hours cannot be recovered.

Mold maintenance is driven by shot counts nobody tracks daily

Preventive maintenance by cycles or shots depends on someone checking which molds are approaching a limit. Missed intervals lead to flash, short shots, tool damage and unplanned press downtime.

Regrind and resin traceability is paperwork-heavy

Customers require proof of which resin lots, drying records and regrind percentages went into a job. Assembling it by hand for a complaint or audit takes hours and risks gaps.

Scheduling juggles presses, molds, materials and changeovers

A job needs the right press tonnage, the right mold, dried material and a staffed shift. Planners balance those constraints with spreadsheets and experience, and one absence can unravel the plan.

Customer data cannot go into a public tool

Part designs, resin specifications, pricing and volumes are commercially sensitive and often confidential under customer agreements, so staff cannot simply paste them into a general chatbot.

Where AI earns its place in DELMIAworks

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

Press performance versus standard

A plant manager asks "which presses ran below standard cycle on the last three shifts and on which jobs" and gets the variance with the mold, material and operator context.

Touches: Production monitoring cycle data, work orders and jobs, standard cycle times, work center (press) records, downtime events

Outcome: Recurring slow-cycle patterns are spotted within the week instead of at month-end, which is often enough to justify a process review.

Mold maintenance due list

Maintenance asks which molds are within a set percentage of their preventive maintenance shot limit and when each is next scheduled to run, so PM can be slotted into the schedule.

Touches: Tooling and mold records, shot or cycle counters, maintenance schedules, production schedule

Outcome: Fewer unplanned stoppages from overdue molds, and PM work fits into planned changeovers rather than interrupting a run.

Regrind and virgin ratio by job

Quality asks which jobs used regrind above the customer-permitted ratio and which resin lots were involved.

Touches: Bill of materials and material issues, resin and regrind lot records, job and work order records, customer specifications

Outcome: Ratio exceptions are caught early and documented, which helps both containment and customer conversations.

Resin lot genealogy for a complaint

Given a customer complaint on a delivered lot, the assistant lays out the resin lots, press, mold, shift and shipments connected to it.

Touches: Lot and serial genealogy, production records, material issues, shipment and customer records

Outcome: Complaint traces become reviewable answers in minutes instead of hours of record assembly, with quality confirming before replying to the customer.

Scrap by cause and by tool

Process engineers ask where scrap is concentrated by defect code, mold, press or material, and how it compares with last month.

Touches: Scrap and reject records with reason codes, work orders, mold and press assignments, material lots

Outcome: Scrap reduction projects start from specific, ranked evidence, and repeat offenders such as one cavity or one material lot become visible.

Changeover and schedule adherence review

Planners ask which changeovers exceeded plan and what drove it, such as mold prep, material drying or first-article approval.

Touches: Production schedule, changeover and setup records, downtime reason codes, work center calendars

Outcome: Changeover time reduction efforts target the real causes, and the schedule reflects actual setup times.

Quote and actual cost comparison

Estimators ask how actual cycle time, scrap and material usage on a recurring part compare with the quoted assumptions.

Touches: Quote and job costing records, routing standards, actual production and material usage, cost roll-ups

Outcome: Future quotes use observed performance, which protects margin on repeat jobs.

Reference architecture

DELMIAworks has historically been built on an Oracle database, with the vendor offering APIs and, depending on version and hosting, database-level reporting access. The AI layer reads production, mold, material and quality data through a read-only path into a store you control, and serves a private open-weight model beside it. Raw data and prompts remain inside your network or private cloud.

1
Data access
A read-only database account or reporting views for self-hosted systems, or the vendor's API layer where the system is hosted. A replica or scheduled extract keeps analytic queries away from the transactional database used by the plant.
2
Machine and cycle data
Production monitoring data from presses, which may come from the PLC or machine controller interfaces such as EUROMAP 63 or OPC UA for injection molding machines, is joined with ERP job and mold records so the assistant can compare cycles with standards.
3
Semantic and retrieval layer
A semantic model translates plant vocabulary (press numbers, mold IDs, resin grades, defect codes) into the underlying tables, and indexes documents such as setup sheets, process sheets and customer specifications.
4
Private model serving
An open-weight model (Llama, Qwen, Mistral, Gemma or gpt-oss class) runs under vLLM or Ollama on your GPUs or private cloud, keeping all data inside your boundary.
5
Interface and governance
Chat and dashboards for planners, maintenance and quality, with roles mirroring DELMIAworks permissions, logged queries, and no write-back unless a named user approves a defined action.

Integration notes for your ERP team

Confirm hosting and access first: self-hosted DELMIAworks allows database-level reporting access, while hosted instances may rely on the vendor's API layer.
Join machine cycle data to ERP jobs and molds with explicit keys (press, mold, job, timestamp). Cycle variance questions are only as good as that join.
Normalise reason codes. Scrap and downtime codes that differ by plant or shift make comparison unreliable, so fix the code list before building analytics on top.
Treat shot counters carefully: confirm whether counts come from the machine, a manual entry or the ERP, since mold maintenance advice depends on which one is authoritative.
Index setup sheets and process sheets with revision metadata so the assistant cites the current version, not an old copy.
Keep any scheduling change in the existing scheduler. The AI suggests and explains; a planner decides.

Deployment options

On-prem GPU server at the plant

Molders in regulated or confidential programs, or with limited bandwidth.

A GPU server and a database replica sit at the plant or HQ. Press data and ERP data are joined locally, and nothing leaves the site.

Private cloud tenancy

Multi-plant molders with central IT and existing cloud accounts.

Replicated data and model serving run in your own cloud subscription, with network rules restricting access to plant users.

Phased start with a single plant

Molders who want to prove value before committing to hardware.

A limited pilot covers one plant and a handful of presses on a private instance, with a plan to scale or relocate it to your environment.

Compliance and data control

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

ISO 9001 and IATF 16949 for automotive molders

The assistant retrieves and summarises records. Control plans, corrective actions and management review stay with your quality system, with named owners.

ISO 13485 and FDA 21 CFR Part 820 (QMSR) for medical molders

Read-only retrieval of lot and process records can support complaint and audit work. Any use inside the quality system should be assessed against your software validation procedure, and the assistant should not become a record of decision.

Customer material and traceability requirements

Customer specifications for resin, regrind ratio and lot traceability can be indexed so answers cite your approved copies.

Food-contact and material regulations

For molders of food-contact packaging, material declarations and supplier certificates can be indexed for retrieval, with compliance decisions still made by your regulatory staff.

Confidentiality and access control

Role mapping, domain separation by customer where required, and logged queries support the confidentiality customers expect around parts and pricing.

Where Netray fits

ERPray

Natural-language answers and dashboards over live job, mold, material and scrap data, with the query shown and read-only access, for the daily operations questions.

DataRay

When machine data, setup sheets and quality files live outside the ERP, DataRay lets users ask questions across them on-prem.

Custom build

A tuned agent for press performance, mold maintenance or resin traceability, integrated with your own machine data interfaces.

How an engagement runs

Phase 1 . 1 to 2 weeks

Discovery

-Hosting and access assessment for DELMIAworks and press data
-Ranked list of 10 to 15 real operations questions
-Reason code and data quality review
-Target environment decision

Phase 2 . 4 to 6 weeks

Pilot build

-Read-only data access and replica
-Semantic model for presses, molds, materials and scrap
-Join with machine cycle data where available
-Private model serving and test question set

Phase 3 . 2 to 4 weeks

Validation

-Accuracy review with planners, maintenance and quality
-Role and access checks
-Performance tuning
-Go or no-go note with measured results

Phase 4 . 2 to 4 weeks

Rollout

-Production deployment and monitoring
-User training and question guide
-Runbook for upgrades and new questions
-Backlog of next use cases

Questions to ask any vendor, including us

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

How do you connect to our DELMIAworks instance, and does that change if it is vendor-hosted?
How do you join press cycle data with ERP jobs and molds, and what if our machine interfaces are mixed?
Can you show the query and the rows behind each number?
How do you handle inconsistent scrap and downtime reason codes?
Where does the model run, and does any customer data leave our network?
Which of our questions would DELMIAworks standard reports already answer, so we do not over-engineer?
How is accuracy tested before go-live and monitored after?
What happens to the AI layer when we upgrade DELMIAworks?

Frequently asked questions

Can AI work with DELMIAworks data without moving it to the cloud?

Yes. On a self-hosted system, a read-only database path feeds a replica on your own server, and a private open-weight model runs next to it on a GPU server. Press and ERP data are joined locally, so nothing is sent to a public service. Vendor-hosted systems need a different access path, which is worth confirming at the start.

Is DELMIAworks's own reporting not enough?

For fixed, recurring reports it often is. A private AI layer helps with ad hoc questions that cross jobs, molds, materials and machine cycles, and with documents such as setup sheets. If most of your questions are routine, improving existing dashboards may be the better first step, and we will say so in discovery.

Can it advise on mold maintenance?

It can list molds approaching preventive maintenance limits and show where they sit in the schedule, provided the shot counts are reliable. It does not decide maintenance policy. Your maintenance and process teams own that, and the assistant makes the data easy to review before each changeover.

Does it help with resin and regrind traceability?

It can assemble the genealogy from resin lots through jobs, presses and shipments, and flag jobs that exceeded a regrind ratio recorded in your system. Accuracy depends on how consistently lots and regrind are recorded at the press, which is often the real constraint rather than the AI.

Can it change the production schedule?

Not by default. It can explain schedule conflicts and suggest options, such as which job to move to a different press, but the planner makes the change in the existing scheduler. If a write action is later justified, it would be limited and approved by a named user.

What about machine data from older presses?

Older presses may have no digital interface, so cycle data may be manual or partial. Joining mixed data is possible, but answers about those presses will be less reliable. A common approach is to start with presses that already report cycles, then extend as interfaces are added.

How long does a pilot take and what does it need?

Usually six to twelve weeks, depending on access, data quality and how many presses report digitally. It needs a named process engineer, a planner and an IT contact, a read-only data path, and a short list of real questions. Results are strongest where reason codes and lot records are consistent.

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