# AI for DELMIAworks (IQMS), grounded in your production monitoring data > Source: https://netray.co/erp-ai/delmiaworks-ai-integration > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: AI for ERP ## Short answer Adding AI to DELMIAworks (formerly IQMS, EnterpriseIQ) means grounding a model on the Production Monitoring, quality, and job data already sitting in your SQL Server database, then running it on your own hardware so plant data never leaves the building. It answers press-and-mold questions in plain language, drafts CAPA and 8D reports from quality records, and rolls up scrap and downtime across plants without a canned Crystal Report. ## Kind platform ## Erp Family other ## Erp Products - DELMIAworks - IQMS - EnterpriseIQ ## Industries - Plastics & Injection Molding - Packaging - Automotive Suppliers - Medical Device ## Region Global ## Persona Plant Manager ## Eyebrow DELMIAworks / IQMS + on-prem AI ## Intro If you run injection molding, packaging, or automotive component production on DELMIAworks, you already have more real-time data than almost any other ERP customer: cycle counts, downtime codes, scrap reasons, and machine states are streaming into your Production Monitoring database every shift. The problem is not a lack of data. It is that almost none of it is usable by anyone who cannot write a SQL query or wait for the next scheduled report. Plant managers ask questions like which press had the worst downtime on second shift, or which mold and material lot correlates with this week's scrap spike, and the honest answer today is usually 'let me pull that and get back to you.' By the time the report comes back, the run that caused the problem is long finished and the root cause is guesswork. Quality teams feel this even more acutely. Automotive and medical device customers on DELMIAworks are expected to close CAPA and 8D corrective actions with real traceability back to machine, lot, and operator, but drafting that documentation by hand from the quality module is slow, and most plants do not have a dedicated quality analyst who can do it full time. None of this requires moving to Dassault's cloud tools or waiting for a native AI feature to ship. A private model, grounded read-only on your existing EnterpriseIQ SQL Server data (ideally a replica, not the live transactional database), running entirely inside your plant network, can answer these questions today and leaves your production data exactly where it already lives. ## Pain Points ### Production Monitoring data is rich but locked behind BI reports **Detail:** Cycle, downtime, and scrap events stream into the database in real time, but only the people who can build a report or write SQL can actually use it. Everyone else waits for the weekly rollup. ### Root-cause questions need an answer during the shift, not next week **Detail:** By the time a scrap or downtime report reaches a process engineer, the mold, material lot, and operator combination that caused it has already moved on to the next job. ### Multi-plant groups can't see across separate EnterpriseIQ databases **Detail:** Most DELMIAworks shops run one database per plant. Getting an OEE or scrap comparison across sites usually means someone manually stitching spreadsheets together once a month. ### CAPA and 8D documentation is a manual writing exercise **Detail:** IATF 16949 and ISO 13485 customers expect structured corrective action reports tied to machine and lot history. Drafting them by hand from the quality module eats a quality engineer's afternoon. ### Small plant IT teams can't build or maintain custom BI **Detail:** DELMIAworks shops are rarely staffed with a data engineering team. There is no one to build a cube, maintain a dashboard, or keep a reporting layer current as the schema changes across versions. ## Use Cases ### Natural-language production and downtime queries **Description:** Plant supervisors and process engineers ask questions like 'which press had the most unplanned downtime on 2nd shift this week' directly, without opening a report builder. **Erp Objects:** Production Monitoring cycle and downtime records, work center/press IDs, shift and job codes **Outcome:** Answers in seconds during the shift instead of waiting for the next scheduled BI report ### Scrap and rejection root-cause copilot **Description:** Correlates scrap and reject codes against mold, material lot, machine, and operator to surface the combination actually driving a scrap spike. **Erp Objects:** Quality/SPC records, work order and job data, material lot and genealogy fields **Outcome:** Narrows a scrap investigation to the likely cause without an analyst manually pulling exports ### CAPA / 8D drafting assistant **Description:** Drafts a structured corrective action report from a quality notification plus the related machine and lot history, ready for a quality engineer to review and finalize. **Erp Objects:** Quality module nonconformance and corrective-action records, related job/machine history **Outcome:** Cuts CAPA drafting from a half day of writing to an hour of review and edit ### Preventive maintenance triage **Description:** Summarizes the open PM work order backlog and cross-references it against unplanned downtime, so maintenance planners work on what is actually costing production time. **Erp Objects:** Preventive maintenance work orders, downtime codes and history by machine **Outcome:** Maintenance effort gets pointed at the machines with the highest downtime cost, not just the oldest ticket ### Multi-plant rollup agent **Description:** Answers cross-site questions on OEE, scrap rate, and downtime by federating queries across each plant's separate EnterpriseIQ SQL Server database. **Erp Objects:** Multiple EnterpriseIQ database instances or reporting replicas, one per plant **Outcome:** Group operations gets a same-day comparison instead of a manual month-end rollup ### EDI order-exception assistant **Description:** Flags and explains automotive EDI 830/862 forecast and release changes against the current production schedule so planners see mismatches before they become a missed ship. **Erp Objects:** EDI transaction logs, sales order and scheduling records **Outcome:** Planners catch release mismatches a day or more earlier, reducing expedite freight and line-down risk ### Shift handover summary generator **Description:** Auto-drafts a plain-language shift handover note from the raw Production Monitoring event log, so the incoming supervisor gets the highlights, not a wall of timestamps. **Erp Objects:** Real-time Production Monitoring event and downtime log **Outcome:** A two-minute readable handover instead of scrolling a raw event feed at shift change ## Architecture **Overview:** The model never sits inside DELMIAworks itself. It reads from your EnterpriseIQ SQL Server database (ideally a reporting replica), builds a semantic layer over press, mold, shift, and quality vocabulary, and answers through retrieval and text-to-SQL rather than free-form guessing. **Layers:** #### ERP and MES connectors **Detail:** Read access to the EnterpriseIQ SQL Server database and the Production Monitoring feed, preferably via a replica so nothing touches the live transactional system. #### Data and semantic layer **Detail:** Maps raw table and code-table names (presses, molds, shifts, scrap reason codes) into business language, since these are heavily customized per plant. #### Model serving **Detail:** An open-weight model served on GPU hardware inside the plant network or a private VPC, with no dependency on an external API. #### Retrieval and agents **Detail:** Combines RAG over quality and maintenance documents with text-to-SQL over Production Monitoring tables, plus narrow agent tools for drafting CAPA and shift summaries. #### Governance and audit **Detail:** Read-only by default, DELMIAworks role mapping respected, and every query and answer logged for quality-system audit trail. ## Deployment Options ### Air-gapped on-prem **Fit:** Automotive and medical device plants under strict customer data rules, or shops that simply do not want plant data leaving the building **Detail:** Model and data both stay on plant hardware, with no outbound connection required for normal operation. ### Private / sovereign cloud **Fit:** Groups that prefer not to run GPUs on-site but still want a single-tenant, non-shared environment **Detail:** Data replicated into a customer-controlled private cloud tenancy; the model never touches a shared multi-tenant service. ### Hybrid **Fit:** Multi-plant groups wanting local inference for real-time questions and a central private cloud for cross-site rollups **Detail:** Inference runs at the plant for immediate answers; a central private tier handles the multi-database rollup queries. ## Compliance ### Every query and generated CAPA draft is logged, so the traceability an automotive audit expects extends to what the AI read and produced. **Framework:** IATF 16949 ### Medical device plants get the same audit trail plus validation documentation for the AI as a computerized system touching quality records. **Framework:** ISO 13485 ### Read-only default access and human review of any drafted corrective action keeps the AI inside existing quality-system controls. **Framework:** ISO 9001 ### On-prem or private-cloud deployment keeps production and quality data inside the boundary most OEM supplier agreements already require. **Framework:** Automotive OEM supplier data clauses ## Integration Notes - Read from a SQL Server reporting replica of EnterpriseIQ, not the live transactional database, so there is no risk to Production Monitoring writes. - The real-time Production Monitoring feed (machine, PLC/OPC-sourced) is the richest and least-used data source in most shops; index it separately from static order and BOM data. - DELMIAworks schema and code tables vary by version and are frequently customized per plant, so connector mapping needs a discovery pass and revalidation after upgrades. - Quality/SPC field names and reason codes are rarely standardized between plants; expect a mapping exercise per site before cross-plant rollups work. - There is no public 'AI connector' shipped by DELMIAworks or Dassault today; integration goes through direct database access or the module's existing reporting and export layer. - Any write-back, such as creating a quality notification, should route through the existing UI or a supported API with human approval, never a raw SQL insert. - Multi-plant rollups need a light federation layer, since most groups run one EnterpriseIQ database per plant rather than a shared instance. ## Product Fit ### ERPray **Why:** DELMIAworks is not a listed connector today; question-answering and dashboards over EnterpriseIQ data are built on ERPray's ERP-agnostic connector architecture. ### Custom build **Why:** The real-time Production Monitoring feed and heavily customized quality schema are typically specific enough per plant to warrant purpose-built connectors and agent tools. ### DataRay **Why:** Useful where the real question spans both Production Monitoring's structured data and unstructured quality or maintenance documents. ## Engagement ### Discovery **Duration:** 2-3 weeks **Deliverables:** - EnterpriseIQ version and schema review - Production Monitoring data model mapping - replica/reporting access plan - prioritized use-case list ### Pilot **Duration:** 6-8 weeks **Deliverables:** - on-prem model stood up on plant hardware - connector to one plant's replica - 2-3 use cases live (production query, scrap root-cause) - supervisor feedback loop ### Production **Duration:** 4-6 weeks **Deliverables:** - governance and audit logging hardened - CAPA/8D drafting assistant rolled out - role mapping aligned to DELMIAworks permissions - training for plant supervisors and quality staff ### Scale **Duration:** ongoing **Deliverables:** - rollout to additional plants - cross-site OEE/scrap rollup agent - PM triage and EDI exception use cases - quarterly review of new use-case candidates ## Buyer Questions - Does the AI ever write to our live EnterpriseIQ database, or does it only read from a replica? - Can the model run entirely inside our plant network with no outbound calls required? - Who revalidates the data mapping after a DELMIAworks version upgrade? - Who can audit exactly what the AI queried and what answer it gave? - Can it work across more than one EnterpriseIQ instance if we run multiple plants? - How does it connect to the real-time Production Monitoring feed specifically, separate from static order data? - Does the team have hands-on experience with the DELMIAworks quality and Production Monitoring schema, or are they learning it on our project? - What GPU hardware do we need to own or host for this to run fully on-prem? ## Faq ### Can AI be added to DELMIAworks (IQMS) without a Dassault cloud subscription? **Answer:** Yes. Since DELMIAworks stores its data in a SQL Server database on premises for most installs, a private model can be grounded directly on a reporting replica of that database and run entirely on your own hardware, with no dependency on any Dassault cloud service. ### What is the fastest AI win for a DELMIAworks plastics or injection molding shop? **Answer:** Natural-language queries over Production Monitoring data, such as press downtime or scrap by shift, are usually the quickest to stand up because the data is already structured and time-stamped. Most shops see a working pilot on that use case within six to eight weeks. ### Does DELMIAworks or Dassault ship a native AI copilot? **Answer:** As of this writing, DELMIAworks does not ship a broad native generative-AI copilot comparable to what SAP or Microsoft offer in their ERPs. That gap is exactly why plants add a private layer grounded on their own Production Monitoring and quality data. ### How does the AI use Production Monitoring data specifically? **Answer:** It reads the cycle, downtime, and scrap event streams from the Production Monitoring database, maps press, mold, and shift codes into plain language, and answers questions or drafts summaries from that data. It does not interfere with the live monitoring feed itself. ### Is this safe for an IATF 16949 or ISO 13485 audited plant? **Answer:** Yes, when deployed read-only with full query logging. The AI's activity becomes part of the same traceability record an automotive or medical device audit already expects, and any generated CAPA or 8D draft still goes through human review before it is finalized. ### Can it help draft CAPA or 8D reports from DELMIAworks quality data? **Answer:** Yes. Given a nonconformance record and its related machine, lot, and operator history, it can draft a structured corrective-action report for a quality engineer to review and finalize, which typically cuts drafting time from a half day to under an hour. ### What does it cost to add AI to a DELMIAworks environment? **Answer:** For a single-plant pilot covering two or three use cases, expect a range comparable to a mid-size ERP integration project, plus the GPU hardware if running fully on-prem. Multi-plant rollouts and heavier customization (custom schema mapping, EDI exceptions) scale up from there. ## Search terms DELMIAworks AI, IQMS AI integration, DELMIAworks AI integration, on-prem AI for DELMIAworks, EnterpriseIQ AI, DELMIAworks copilot, DELMIAworks generative AI, IQMS Production Monitoring AI, injection molding ERP AI, DELMIAworks natural language query, plastics manufacturing AI ERP, DELMIAworks MES AI agent, IQMS quality CAPA AI, private LLM DELMIAworks --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json