DELMIAworks + scheduling
AI for DELMIAworks production scheduling
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.
- ERP
- DELMIAworks
- Industries
- Plastics, Metal stamping, Automotive suppliers, Discrete manufacturing
- Written for
- Production Planner and Plant Manager
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.
What usually gets in the way
The problems we hear most from production planner and plant manager teams running DELMIAworks.
Replanning after every disruption
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
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
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
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
How to sequence jobs to minimize changeovers lives in an experienced planner's head. When they are away, the schedule gets worse.
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.
Why is this order late
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.
Touches: 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
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.
Touches: 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
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.
Touches: 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
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.
Touches: 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
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.
Touches: 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
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.
Touches: 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
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.
Touches: 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.
Reference architecture
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.
- 1
Source access
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.
- 2
Planning query layer
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.
- 3
What-if simulation
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.
- 4
Model serving
Open-weight model on your GPU or private cloud through vLLM or Ollama, so schedule and customer data stay in your boundary.
- 5
Controls and audit
User mapping to DELMIAworks roles, a log of questions and queries, and approval before any proposed change is entered by a person.
Integration notes for your ERP team
- 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.
Deployment options
On-prem or private server beside the DELMIAworks database
Shops that will not let job, customer, or drawing data leave the building, including ITAR and CUI work.
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
Multi-site manufacturers that want managed GPUs without sending data to a shared public AI API.
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
Teams that want to prove value on real data before committing hardware or touching production.
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 and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
IATF 16949 for automotive suppliers
The assistant supports scheduling and traceability questions with cited records. The customer's quality system defines how those records are used in audits.
ITAR and CUI for defense programs
On-prem hosting keeps program and part data within the customer's boundary. The customer maintains its own registration and controls.
ISO 13485 and FDA QMSR for medical molders
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.
Customer confidentiality
Customer-specific volumes and pricing are limited by role, and the model is not trained on them without explicit agreement and local fine-tuning.
How an engagement runs
Phase 1 . 1 to 2 weeks
Discovery and data map
- -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
Phase 2 . 2 to 4 weeks
Grounding and retrieval build
- -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
Phase 3 . 3 to 4 weeks
Pilot with a small group
- -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
Phase 4 . 2 to 3 weeks
Harden and hand over
- -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
Questions to ask any vendor, including us
A short list that separates real DELMIAworks AI work from a chatbot demo.
- 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?
Frequently asked questions
Does the AI replace the DELMIAworks scheduler?
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?
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?
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?
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?
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?
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?
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.
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Talk it through with an engineer who knows DELMIAworks
Bring one real question your team cannot answer from the ERP today. We will map the data path, the model, and where it runs, and tell you honestly if AI is the wrong tool for it.