AI Agents in Manufacturing

Business Role: Continuously monitors production schedules, predicts future delays, evaluates alternative scenarios and automatically recommends the most resilient production plan before disruptions occur.

A cold-rolled steel producer running cold rolling, annealing, pickling, galvanizing and folding as separate work centers knows a schedule can look solid on paper and still fall apart the moment one work center starts running behind. 

The real question is not whether the plan is optimal. It is whether the plant can actually execute it. DELMIA's approach to this works in stages: first evaluating schedule performance before execution, delivery performance and resource utilization by item and work center, then tracking adherence once the schedule is live, scheduled versus actual end dates, scheduled versus actual quantities, broken down by resource. 

The predictive layer goes further still, flagging which operations are likely to run late, scoring each prediction with a confidence level and explaining the influencing factors behind it rather than raising a bare alert. Manufacturers running this kind of scheduling intelligence have reported roughly 20% gains in both delivery reliability and delivery performance, alongside a 75% cut in the time spent building and rebuilding the schedule, because the plant is anticipating a disruption instead of discovering it after a work order is already late.

Business Role: Combines market signals, ERP orders, customer demand, promotions and external events to continuously refine demand forecasts and proactively adjust production plans.

A metals producer supplying cut-to-length or coil products to automotive and construction customers usually sets its production schedule around a sales forecast that was already a few weeks stale by the time it reached the plant, with sales, procurement and production each working off a slightly different version of demand. 

Scheduling intelligence work in this industry has focused less on the forecast itself and more on visibility: surfacing cross-KPI correlations between demand signals, resource utilization and delivery performance, so a shift in one shows up against the others instead of sitting in a single department's report. 

A Demand Intelligence Agent extends that same logic upstream, keeping the forecast itself moving as new order and market signals arrive rather than resetting on a fixed monthly cycle, so production planning works from a current picture of demand instead of a stale one.

Business Role: Optimizes production, inventory and logistics decisions while explaining the rationale, trade-offs and business impact behind every recommendation to planners and executives. 

Ask scheduling and supply chain leaders across the largest metals producers in North America why optimization solutions are sometimes underutilized and the answer is remarkably consistent: the optimizer delivers an excellent mathematical solution, but planners often struggle to understand the reasoning behind it or determine whether it is practical to execute on the shop floor. 

The issue is not the optimizer, it remains the foundation for solving complex scheduling and supply chain problems. 

The opportunity is to complement it with an AI Decision Intelligence Agent that explains, validates and contextualizes every recommendation. Instead of presenting a schedule as a black box, the AI agent provides a clear rationale, highlighting the constraints, trade-offs and operational factors that influenced the outcome. Planners can ask questions such as Why was this work order prioritized?, Which resource is constraining the schedule? or What happens if demand changes tomorrow? and receive immediate, contextual answers grounded in enterprise data. Optimization determines the best possible solution; the AI agent makes that solution understandable, trustworthy and actionable. Together, they transform advanced planning from a mathematically optimized process into an intelligent, collaborative decision-support capability that increases planner confidence, accelerates execution and drives greater business value.

Business Role: Automatically transforms engineering BOMs into manufacturing-ready BOMs while validating manufacturability, availability and production constraints.

A heavy equipment manufacturer releasing a new engineering change usually has a process engineer manually rebuild the manufacturing bill of materials from the updated engineering BOM, checking part availability and manufacturability by hand against several different systems. That downstream rework, not the change itself, is what makes engineering changes expensive. A Manufacturing BOM Agent generates the manufacturing BOM directly from the engineering BOM, checking manufacturability and part availability against real production constraints as it goes, rather than after a human has already built and submitted a BOM that then bounces back for correction. The engineer still reviews and signs off, but the correction happens while the change is still on a screen, not three weeks after it reached the shop floor.

Business Role: Creates optimized manufacturing process plans, selecting operations, resources, tools and sequencing based on production objectives.

A machined-parts supplier onboarding a new part family typically has a process planner build the routing, pick tooling and sequence operations from memory and a handful of similar past jobs, which means the result depends heavily on which planner happened to be free that week. That is precisely the kind of work that is too dependent on one person's specialist knowledge to scale well. A Process Planning Agent proposes a candidate process plan against the shop's full history of prior plans rather than one planner's personal experience, sequencing operations, tooling and resources around the actual production objective and the planner still checks and adjusts it before release. New part introduction moves faster because the starting point is the shop's collective knowledge, not whoever happened to be at their desk that week.

Business Role: Produces role-specific digital work instructions using engineering data, manufacturing knowledge, and enterprise best practices.

A discrete manufacturer launching a new product usually has a technical writer manually assemble work instructions from CAD, the process plan and whatever tribal knowledge the writer can track down from senior operators, work that takes days per instruction and often falls behind the production schedule. An instruction still being written after the line is already running has already cost something. A Work Instruction Agent generates role-specific instructions directly from the engineering and process data already sitting in the system, tailored to what a specific operator or station actually needs to see and someone still reviews it before it reaches the floor. The instruction also updates automatically when the process changes, instead of quietly going stale the way a manually written one tends to.

Business Role: Translates work instructions, technical documentation and manufacturing knowledge into multiple languages while preserving engineering intent.

A global manufacturer running plants across several countries usually has each site's work instructions translated by a local contractor, or worse, left to an operator's own rough reading of an English original, with engineering terms often lost or subtly changed along the way. A mistranslated torque value or tolerance callout is not really a language problem. It is a safety and quality problem wearing a language problem's clothes. A Manufacturing Knowledge Agent translates work instructions and technical documentation while preserving that specific engineering intent, rather than rendering it as generic prose and a local reviewer still checks the result before it goes to the floor. Plants get a verified translation in days instead of the weeks it takes to commission and check a contractor site by site.

Business Role: Guides operators through assembly, maintenance and inspection tasks using AR while validating every step and automatically recording execution.

An aerospace assembly plant with a highly variable, low-volume build relies heavily on paper travelers and a supervisor's spot checks to confirm every step got done correctly, which leaves plenty of room for a missed step to go unnoticed until final inspection. 

A checklist someone might forget to sign is not really quality control. It is paperwork standing in for it. A Connected Worker Agent guides the operator through each assembly or inspection step in augmented reality, comparing what is physically being built against the digital definition, confirming the right part is present and the assembly is complete, using visual recognition for things like bolt verification and flagging what is right or wrong in a way the operator can act on immediately. 

The operator still does the work and decides what to do about anything flagged, but the mistake gets caught at the step where it happened, not at final inspection once it is far more expensive to fix.

Business Role: Automatically generates optimized CNC toolpaths based on machining objectives such as cycle time, surface finish, tool life, and energy consumption.

A precision components machine shop cutting titanium aerospace brackets keeps its CAM work bottlenecked on the two or three programmers who know the software cold, because a capable CAM environment still expects the user to already know where to start. 

The friction is rarely the physics of cutting. It is picking the right operations for a given surface out of a deep menu of options and getting to a sensible first set of parameters without hunting through defaults. A Machining Optimization Agent removes that first step: the programmer selects a surface, the system proposes the operations that fit that geometry and suggests a reasonable starting point for feeds, speeds and tooling. 

Nothing about it replaces the programmer's judgment on the finished parameters. What changes is how fast a newer programmer reaches a usable starting point and how much less the shop depends on the handful of people who currently carry that knowledge only in their heads.

Business Role: Creates, validates, and continuously optimizes robot programs for new products using knowledge learned from previously deployed robotic operations.

An automotive body-in-white plant introducing a new vehicle variant usually reprograms welding robots close to from scratch, even though most of the weld sequence closely resembles the last few vehicles built on the same line. Much of that rework is not new engineering. It is redoing motion that already exists somewhere in the plant's history. A Robotics Programming Agent works from the actual engineering context, seams, fasteners, process geometry, to accelerate task creation, generate collision-free paths and propose efficient robot configurations rather than starting motion planning cold. 

The engineer still validates reach, cycle time and collisions before anything runs on the line, but validating a proposed program takes a fraction of the time that building one from a blank sequence does.

Business Role: Evaluates operator posture, workstation layout, repetitive motion and accessibility, then recommends safer and more productive workplace designs.

A furniture or appliance assembly line with manual stations usually only discovers an ergonomics problem after an operator files a repetitive strain claim, well after the workstation design has already been locked in and tooled up. 

By then, fixing it means reworking a station that is already built, not editing a drawing. A Human Ergonomics Agent checks posture, reach and repetitive motion against the digital workstation model before a single part is built and flags where a fixture height or part presentation angle is going to cause a problem down the line. The designer still makes the final call. Catching it on screen costs an afternoon. Catching it on the floor costs a rebuild.

Business Role: Monitors additive manufacturing processes in real time, detects print anomalies, predicts defects before completion and recommends corrective actions.

A medical device manufacturer 3D printing titanium implants cannot afford to find out about a porosity defect after a multi-day print finishes, since the part and the machine time are both gone at that point. 

Waiting until the part is finished to check it is really the same mistake as checking a build only at final inspection, just spread over several days instead of one shift. An Additive Quality Agent watches melt pool and layer data during the build itself and flags an anomaly as it starts, sometimes early enough to abort a print before it wastes the remaining days of build time. The process engineer still decides whether to stop the build, but that decision now happens on day two instead of after the print has already finished and failed.

Business Role: Automatically designs and evaluates alternative factory layouts that maximize throughput, safety, flexibility and material flow.

A logistics operator planning a new distribution center, or an automotive plant reworking a line for a new model, usually starts a layout from a blank canvas or an old drawing that no longer matches what is actually on the floor. A Factory Layout Agent lets an engineer describe the requirement in plain language, a robot line with a defined reach envelope, a spacing rule between stations, a tool that has to sit within an operator's reach and generates a first-pass arrangement directly from that description, or from a rough sketch or scan of the existing space. 

The engineer still reviews, corrects and simulates the result before anything is built; the agent does not remove that judgment. What changes is the starting point: instead of drafting the first version of a layout from nothing, engineering starts from something already roughed out and spends its time validating and refining rather than building from a blank page.

Business Role: Simulates production disruptions, equipment failures, workforce shortages and demand changes to recommend the optimal response before execution.

A plant considering a new robot cell, a re-routed conveyor, or a revised line sequence cannot test that change on the live line without risking a shift's worth of output if it goes wrong. 

What actually makes a suggested change worth anything, a robot path, a layout tweak, a new sequence, is whether it can be checked inside the virtual twin before it touches the physical line. A Scenario Simulation Agent runs the proposed change against the plant's digital model: a robot path checked for collisions and reach, a layout checked against space constraints and material flow, a sequence change checked against cycle time and safety, before anyone commits to it on the floor. 

Engineers still make the call on what to approve. What the agent removes is the blind spot where a plant only discovers a change does not work after it has already been built, instead of while it is still a proposal on screen.

Business Role: Continuously monitors production KPIs, equipment utilization, quality and energy performance, issuing proactive alerts before deviations affect production.

A packaging plant tracking overall equipment effectiveness off an end-of-shift report finds out about a slow bleed in line efficiency, a full shift after it started, by which point a full day's output has already been affected. 

A report that arrives after the shift is over is not really monitoring. It is a postmortem. An Operations Monitoring Agent watches the same KPIs continuously and raises a flag the moment something starts trending wrong, a fill line running five percent under its own baseline, rather than waiting for the shift summary to say so. 

Catching that drift within the hour instead of at day's end is usually the difference between a quick adjustment and a full shift of lost output.

Business Role: Dynamically configures production logic, workflows and automation sequences based on changing production priorities without manual programming.

A contract manufacturer switching a flexible line between product families used to mean an automation engineer manually rewriting PLC logic and workflow sequences for each changeover, work that ties up a scarce specialist for days at a time. 

That is exactly the kind of repetitive, high-effort work between an idea and a working line that keeps engineers stuck in setup instead of doing anything new. An Automation Orchestration Agent reconfigures the production logic and sequencing directly against the new priority, without an engineer hand-writing fresh automation code for every changeover, though someone still checks and approves the result before it runs. 

Changeover moves from days of manual reconfiguration to hours of review, which frees up the one person on site who actually understands the automation for the changes that genuinely need a person.

Business Role: Predicts emerging quality issues before defects occur, identifies probable root causes using production history and recommends corrective and preventive actions to eliminate recurrence.

A pharmaceutical or medical device manufacturer investigating a non-conformance usually has a quality engineer spend days combing through batch records, equipment logs and operator notes by hand to find the root cause and by the time the investigation closes, several more batches may have run with the same undetected issue. 

That gap, between when a problem starts and when someone notices, is where the real damage happens, not in any single batch. A Quality Intelligence Agent watches production data for the early signature of a deviation before it becomes a full non-conformance and when one does occur, narrows the root cause search against production history rather than starting from a blank page, though the quality engineer still makes the final call on disposition. Fewer batches run before the pattern is caught and that is often the difference between a contained deviation and a recall.

Virtual companions are a new generation of AI agents embedded in the 3DEXPERIENCE platform of Dassault Systèmes that understand industrial intent, reason within context, and act directly in the flow of work to help organizations achieve better outcomes. Grounded in scientific laws, engineering principles, manufacturing standards, and industrial know-how, they deliver trusted, explainable intelligence directly within enterprise workflows.

Access runs through blue tokens, the consumption currency 3DS uses across its platform. You purchase a token pool and draw down from it as you use the Companions, so cost tracks actual usage instead of a flat seat fee. You can refer to the information on tokens at 3ds.com/store/blue-tokens. If you already run DELMIA today, your account team can help size the right token pool based on how your teams plan to use the Companions week to week.

No. Virtual Companions sit on top of your current DELMIA license, not inside it. If you're a DELMIA customer, the setup is straightforward since your platform access is already in place, but you still need to provision the licenses for the Virtual Companions. 

Product cost is dependent on a few variables. Please contact your DELMIA representative to discuss the usage and specific virtual companion scope to ascertain the proper cost.