# AI for DELMIAworks production scheduling > Source: https://netray.co/erp-ai/delmiaworks-production-scheduling-ai > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: AI for ERP ## Short answer AI on DELMIAworks reads work orders, routings, work center load, material availability, and the planning and scheduling board so a planner can ask why an order is late and what moving it would displace. It does not replace the finite scheduler; it explains its output and finds the exceptions faster. Access is read-only and can be hosted on-prem. ## Kind use-case ## Erp Family other ## Erp Products - DELMIAworks ## Industries - Plastics - Metal stamping - Automotive suppliers - Discrete manufacturing ## Region Global ## Persona Production Planner and Plant Manager ## Eyebrow DELMIAworks + scheduling ## Intro DELMIAworks, formerly IQMS, is built for repetitive and process-oriented discrete manufacturers, especially plastics, stamping, and automotive suppliers. Scheduling in these plants is rarely a clean plan. A press goes down, a mold is not ready, resin is late, and the planner is rebuilding the next three days by hand while the sales team asks for a ship date. The scheduling data is rich. Work orders, routing steps, work center capacity, tooling, material requirements, and the schedule itself all live in the system, with real-time production feeds if the MES side is in use. What is missing is a fast way to ask a question across them. Why did this order slip? Which orders depend on this mold? What happens if we run the rush order first? An AI layer on top of DELMIAworks can answer those questions from the data in front of the planner. It reads the schedule, shows how it derived the answer, and proposes options. The planner remains the decision maker, and any change to the schedule is made in DELMIAworks by a person. This page covers how that works with DELMIAworks specifically, what the standard scheduler already does well, and where an assistant is worth adding. We also cover hosting, since many DELMIAworks customers supply automotive, defense, or medical programs and do not want production data in a shared cloud AI. ## Pain Points ### Replanning after every disruption **Detail:** Machine downtime, a late resin delivery, or a mold change moves the whole sequence. Planners rebuild priorities in spreadsheets because it is faster than navigating several screens. ### Ship date promises without visibility **Detail:** Customer service asks when an order will ship. The honest answer depends on load, tooling, and material, and getting it requires a planner's time. ### Tooling and mold constraints **Detail:** A mold or die that is shared between jobs, or out for maintenance, is a hidden constraint. It is visible in the data but not on the first screen. ### Schedule versus shop floor drift **Detail:** Actual production counts and cycle times differ from the routing standards. The schedule is wrong in a consistent way that nobody has quantified. ### Knowledge held by one planner **Detail:** How to sequence jobs to minimize changeovers lives in an experienced planner's head. When they are away, the schedule gets worse. ## Use Cases ### Why is this order late **Description:** The planner names a work order and the assistant traces the cause: waiting for material, queued behind another job, tooling unavailable, or a work center down. It cites the data for each factor. **Erp Objects:** Work orders, scheduled operations, work center calendars, material requirements and receipts, mold and tooling assignments **Outcome:** Root-cause lookup that took several screens takes a single question. Accuracy depends on how current the shop floor data is. ### What-if on a rush order **Description:** The assistant lists which jobs would slip, by how long, and which customers they belong to if a rush order is inserted on a given machine, based on current schedule data. **Erp Objects:** Scheduled operations by work center, due dates, customer orders, changeover times **Outcome:** The planner sees the cost of a rush decision before making it. The schedule itself is changed in DELMIAworks. ### Changeover reduction suggestions **Description:** For a work center, the assistant proposes a sequence that groups similar materials, colors, or molds, and shows the estimated change in changeover time versus the current order. **Erp Objects:** Scheduled operations, item attributes such as material or color, mold and tooling IDs, standard changeover times **Outcome:** A suggested sequence for the planner to accept or reject. Benefits vary with how much product mix creates changeover cost. ### Material shortage early warning **Description:** The assistant checks upcoming scheduled work orders against on-hand and inbound purchase orders and lists the ones that will be short, with the expected shortage date. **Erp Objects:** Work order material requirements, inventory on-hand, purchase order due dates, supplier lead times **Outcome:** Buyers hear about shortages days earlier, which gives time to expedite or reschedule. ### Promise date answers for customer service **Description:** Customer service asks for the likely ship date for an order. The assistant answers from the current schedule and flags the assumptions and risks behind the date. **Erp Objects:** Sales orders, work orders, scheduled completion, finished goods inventory **Outcome:** Fewer interruptions for the planner and more consistent answers to customers. ### Cycle time versus standard review **Description:** Across recent production, the assistant lists items where actual cycle time or yield differs from the routing, ranked by volume, so planners can fix standards that skew the schedule. **Erp Objects:** Production reporting, routing standards, shop floor data collection records **Outcome:** More realistic schedules after standards are corrected. Updates follow your normal change process. ### Shift handover summary **Description:** At shift change the assistant generates a summary of what ran, what is behind, what is blocked, and what the next shift should start with, drawn from the schedule and production data. **Erp Objects:** Production counts, down time records, scheduled operations, open work orders **Outcome:** A consistent handover instead of a verbal one. The supervisor edits it before it is used. ## Architecture **Overview:** The assistant reads schedule and production data through an approved path, uses tested queries for common planning questions, and shows its working. The DELMIAworks scheduler stays the system of record for the schedule. **Layers:** #### Source access **Detail:** A read-only account on the DELMIAworks database (Oracle in the usual deployment) limited to approved views, or the API surface your version exposes. Confirm which applies in discovery. #### Planning query layer **Detail:** Tested queries for late orders, load by work center, material shortage, and tooling conflicts, parameterized so the model does not write free-form SQL on production. #### What-if simulation **Detail:** Simple, transparent sequencing logic that works on a copy of the schedule data and returns a comparison. It is an aid for the planner, not a second scheduler. #### Model serving **Detail:** Open-weight model on your GPU or private cloud through vLLM or Ollama, so schedule and customer data stay in your boundary. #### Controls and audit **Detail:** User mapping to DELMIAworks roles, a log of questions and queries, and approval before any proposed change is entered by a person. ## Deployment Options ### On-prem or private server beside the DELMIAworks database **Fit:** Shops that will not let job, customer, or drawing data leave the building, including ITAR and CUI work. **Detail:** An open-weight model (Qwen, Llama, Mistral, or gpt-oss class) is served with vLLM or Ollama on a single GPU server on the plant network. Retrieval and query tools run on the same box. A read replica of the Oracle database avoids any query load on the production scheduler. ### Sovereign or private cloud tenant **Fit:** Multi-site manufacturers that want managed GPUs without sending data to a shared public AI API. **Detail:** The model and the vector index run in a private cloud account or a regional sovereign provider, reached from the plant over a private link. Data stays in a tenant you control and you choose the region. ### Read-only pilot against a replica **Fit:** Teams that want to prove value on real data before committing hardware or touching production. **Detail:** A nightly restore or replica of the DELMIAworks data feeds a small model and a handful of approved questions. Nothing writes back. Most of the learning is about data quality and which questions people actually ask. ## Compliance ### The assistant supports scheduling and traceability questions with cited records. The customer's quality system defines how those records are used in audits. **Framework:** IATF 16949 for automotive suppliers ### On-prem hosting keeps program and part data within the customer's boundary. The customer maintains its own registration and controls. **Framework:** ITAR and CUI for defense programs ### Read-only access with logging means the assistant does not alter records that sit under document control. Changes are made by authorized staff in DELMIAworks. **Framework:** ISO 13485 and FDA QMSR for medical molders ### Customer-specific volumes and pricing are limited by role, and the model is not trained on them without explicit agreement and local fine-tuning. **Framework:** Customer confidentiality ## Integration Notes - DELMIAworks commonly runs on an Oracle database. Table names such as ARINVT for items, WORKORDER for work orders, and STANDARD for routings are typical in IQMS lineage, but verify names and relationships against your version before building queries. - If you use the real-time production monitoring side, press and machine counts can enrich the schedule with actual progress. Include that data deliberately. - The finite scheduler output is the reference. Reproduce a known schedule view with the assistant before trusting new answers. - Tooling and mold assignments are often stored as item or routing attributes. Map them early because they drive many of the useful answers. - Use a read replica or a reporting schema where possible to keep analytic queries off the transactional database. - Plan for upgrades. Version changes can alter tables and views, so keep the query layer in version control and test it after each upgrade. ## Product Fit ### ERPray **Why:** Questions, dashboards, and a daily late-order and shortage list over live DELMIAworks data, read-only with the query shown. ### Custom build **Why:** Suited to a bespoke what-if sequencing agent or a gated workflow that proposes schedule changes for planner approval. ## Engagement ### Discovery and data map **Duration:** 1 to 2 weeks **Deliverables:** - Inventory of the DELMIAworks tables, views, and reports behind scheduling and work orders - Ranked list of 15 to 25 real questions from the people who do the work - Decision on hosting, access method, and which roles may see which data - Written read-only boundary and approval rules for any later write-back ### Grounding and retrieval build **Duration:** 2 to 4 weeks **Deliverables:** - Read-only connection with a service account scoped to the approved objects - Curated query layer and document index for scheduling and work orders - Evaluation set of questions with checked answers from your own data - Visible query or source citation on every answer ### Pilot with a small group **Duration:** 3 to 4 weeks **Deliverables:** - Pilot with 5 to 15 estimators, planners, supervisors, or quality staff - Weekly review of wrong or unhelpful answers and fixes - Usage log showing which questions were asked and which saved time ### Harden and hand over **Duration:** 2 to 3 weeks **Deliverables:** - Role mapping, audit logging, and backup of the AI stack - Runbook for model updates and index refresh after DELMIAworks upgrades - Go or no-go note on any gated write-back actions ## Buyer Questions - Which DELMIAworks version and database do we run, and is the schedule data accessible through views we can approve? - Does the assistant replace the finite scheduler or only explain and compare? - How does it handle molds, dies, and other shared tooling constraints? - Can it reproduce a known schedule view before we rely on it? - Who sees which customers and programs? - How is the stack tested after a DELMIAworks upgrade? - Is any write-back planned, and who approves it? - Where is the model hosted and what leaves our network? ## Faq ### Does the AI replace the DELMIAworks scheduler? **Answer:** No. The scheduler remains the source of truth. The assistant explains its output, finds exceptions, and compares simple what-if sequences. Any change is made in DELMIAworks by a planner. ### Can it help with changeover reduction? **Answer:** Yes, as a suggestion. It groups jobs by material, color, or mold and shows an estimated change in changeover time. The value depends on your product mix, and a planner decides whether to accept the sequence. ### Is DELMIAworks data sent to a public AI service? **Answer:** Not in our deployments. The model runs on your hardware or private cloud, with read-only access to approved data. That is important for automotive, defense, and medical programs with confidentiality requirements. ### What data does the assistant need? **Answer:** Work orders, routings, work center calendars, material requirements, inventory, purchase order dates, and tooling assignments. If you use real-time production monitoring, counts and downtime add useful context. ### How good will the answers be if routings are out of date? **Answer:** They will reflect the data. A common first finding is that standards are stale, which is itself useful. The assistant can rank the worst offenders so planners can fix the standards that distort the schedule most. ### What is a realistic pilot? **Answer:** Eight to twelve weeks: map data, build the planning queries, then run a small group of planners and customer service staff on real questions. Success is measured on answer correctness and time saved on rework of the schedule. ### Do we need a separate APS tool? **Answer:** Not necessarily. If the DELMIAworks scheduling module fits your process, adding an assistant may be enough. If you need advanced optimization across many constraints, evaluate a dedicated planning system and then connect AI to it. ## Search terms DELMIAworks scheduling AI, DELMIAworks production scheduling, IQMS scheduling AI assistant, AI for DELMIAworks planners, DELMIAworks late work order analysis, DELMIAworks finite scheduling, plastics scheduling AI ERP, DELMIAworks changeover optimization, DELMIAworks natural language query, on-prem AI DELMIAworks, DELMIAworks shortage report AI --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json