Manufacturing Operations Intelligence

From production planning to intelligent factory operations.

Connect production, procurement, materials, quality, maintenance and dispatch. Move from manual coordination to rule-based automation and governed AI-agent workflows—at a pace that fits your factory.

Explore the AI shift Discuss a manufacturing workflow

A practical operating model for discrete and process manufacturers. Capabilities depend on data, integrations, controls and implementation scope.

One connected operating picture

Plan productionOrders, capacity, BOMs, routing and material availability.

Coordinate materialsProcurement, suppliers, stores, WIP and finished goods.

Manage exceptionsQuality holds, downtime, shortages and schedule changes.

Improve executionTrack output, yield, waste, lead time and delivery performance.

Production & planning

Procurement & supply

Quality & traceability

ERP-connected operations

The operating model is changing

Traditional vs automated vs agent-based manufacturing

Manufacturing will not simply replace ERP with AI. A more realistic shift combines systems of record, reliable automation and AI agents that can investigate and coordinate work within defined permissions.

Traditional operations

People coordinate the work

Teams check reports, spreadsheets, calls and separate systems to understand what is happening.

Production plans updated manually
Shortages discovered during execution
Downtime escalated through calls
Quality and supplier issues investigated separately
Reports reviewed after the event

Rule-based automation

Software follows predefined rules

Known events trigger established actions, alerts and workflows. The logic is useful but bounded by its rules.

Reorder alerts at defined thresholds
Scheduled production and stock reports
Automatic notifications for delays
Approval routing for purchase requests
Fixed checks for quality or maintenance events

Agent-based operations

AI investigates and coordinates

With suitable data and integrations, agents can assess context, compare options and coordinate authorised next steps.

Investigate why a production order is at risk
Compare shortages, supplier status and alternatives
Prepare schedule or material-plan options
Coordinate tasks across connected systems
Escalate consequential decisions for approval

The key difference: automation executes a known rule; an AI agent can work through a multi-step task toward a defined outcome. Neither should bypass safety procedures, quality controls or approval limits.

Example workflow

A production order is at risk

A critical component is short, a supplier delivery has slipped, and the production schedule may miss its commitment. Here is how the operating model can evolve.

Illustrative scenario only. It does not imply a live integration or currently deployed autonomous agent.

Traditional

A planner finds the shortage, calls procurement and stores, checks supplier updates and manually revises the schedule.

Automated

A material threshold or delayed purchase order triggers an alert and routes an exception to the responsible team.

Agent-based

An agent checks the BOM, inventory, open orders, supplier ETA and production priorities; prepares feasible alternatives; and requests approval before changing a purchase order or schedule.

Verify the outcome

The approved action is recorded, affected teams are informed, and the plan is checked against the updated supply and production status.

End-to-end manufacturing

Connect the full production lifecycle

Start with the workflow that creates the most operational friction. Connect additional areas as data quality and business value justify it.

Demand & order planning

Translate demand and customer commitments into production priorities and feasible plans.

Plan

BOM & material planning

Connect bills of materials, routing, stock, WIP and material requirements.

Materials

Procurement & suppliers

Track purchase orders, lead times, supplier commitments and shortage risks.

Supply

Shop-floor execution

Monitor work orders, machine status, shift output and production exceptions.

Execute

Quality & traceability

Coordinate inspections, non-conformance, holds, corrective actions and batch records.

Quality

Maintenance & downtime

Route maintenance requests and analyse downtime signals when reliable machine data exists.

Reliability

Warehouse & finished goods

Track raw materials, WIP, finished stock, lot locations and dispatch readiness.

Inventory

Costing & performance

Connect consumption, scrap, labour, output, variances and operational KPIs.

Improve

Potential AI agent categories

Purpose-built agents for manufacturing work

These are proposed capability areas. Each agent needs a defined task, trusted data, permissions, evaluation and an escalation path before production deployment.

Production Planning Agent

Identify schedule conflicts, capacity constraints and orders at risk; prepare rescheduling options.

Material Shortage Agent

Investigate component shortages using BOMs, inventory, reservations and open purchase orders.

Supplier Follow-up Agent

Summarise overdue commitments, draft supplier follow-ups and flag delivery risks.

Production Variance Agent

Compare planned versus actual output, consumption and cycle time; explain notable variances.

Quality Exception Agent

Organise inspection results and non-conformance records; route issues to quality owners.

Maintenance Triage Agent

Prioritise maintenance requests and summarise asset history when records are available.

Scrap & Yield Agent

Detect patterns in scrap and yield records and suggest investigations for human review.

Dispatch Readiness Agent

Check order, quality release, stock and shipping prerequisites before dispatch.

Operations Briefing Agent

Prepare shift and daily exception summaries with owners, blockers and recommended next steps.

A connected architecture

AI needs an operational foundation

Agents should work with governed business systems—not become another disconnected dashboard.

Demand & ordersSales orders, forecasts, commitments

Factory operationsBOM, routing, work orders, quality

Supply & warehousePurchasing, suppliers, stock, dispatch

Shared ERP and operations dataProducts · BOMs · Inventory · Purchase orders · Production orders · Quality · Costing · Roles and audit logs

Rules & workflowsTriggers, thresholds, approvals

AI operations layerInvestigate, compare, recommend, coordinate

Governed executionAuthorised actions, verification, escalation

The architecture is conceptual. Specific ERP, MES, PLC/SCADA, IoT, quality or maintenance integrations must be assessed and implemented for each environment.

Measure the operating outcome

Evaluate value through factory KPIs

Choose baseline metrics before a pilot. Targets should be agreed with the customer and validated from actual operating data.

Area	Example metric	What it helps reveal
Production	Schedule adherence; throughput	Whether committed work is completed on plan.
Materials	Shortage frequency; line stoppages due to material	Where supply or inventory planning interrupts production.
Quality	First-pass yield; scrap and rework rate	Where defects and rework consume capacity.
Equipment	Downtime; mean time to repair	How equipment interruptions affect availability.
Procurement	Supplier on-time delivery; purchase price variance	Reliability and cost of inbound supply.
Inventory	Inventory days; WIP; stock accuracy	Whether working capital and material flow are healthy.
Execution	Exception resolution time; manual hours	Whether connected workflows reduce coordination effort.

Built for responsible execution

Autonomy with boundaries

AI should support manufacturing teams, not bypass safety procedures, engineering sign-off, quality release or financial controls.

Role-based access

Agents can only read or change the systems and records they are authorised to access.

Human approval

Schedule changes, supplier commitments and material substitutions follow agreed approval rules.

Audit trail

Record the source data, recommendation, approver and action taken.

Verification

Confirm that approved changes succeeded and route failures or uncertainty to a person.

A practical adoption path

Start with one high-value workflow

Manufacturing AI adoption should progress in stages, based on the plant's systems, data readiness, risk and measurable value.

Phase 01

Connect & understand

Establish a reliable baseline.

Map the current workflow and owners
Review ERP/MES data and integration options
Define the KPI baseline and success criteria

Phase 02

Assist & automate

Reduce manual coordination safely.

Deploy dashboards, alerts and exception summaries
Test recommendations in shadow mode
Validate with planners, production and quality teams

Phase 03

Execute with controls

Expand only after validation.

Enable bounded actions with explicit permissions
Require approval for consequential decisions
Monitor failures, overrides and realised outcomes

Frequently asked questions

Manufacturing AI, clearly explained

Does agent-based AI replace an ERP or MES?

No. ERP and MES remain systems of record and execution. AI agents can sit above or alongside them to investigate exceptions and coordinate permitted workflows when integrations are available.

Can this work for discrete and process manufacturing?

The concepts can apply to both, but production models, traceability, quality controls, batch requirements and integrations differ. The implementation should be scoped to the plant and its operating model.

Can an agent change a production schedule or place a purchase order automatically?

Only if the integration supports it and the business explicitly authorises that action. A pilot can begin with recommendations and human approval before any bounded execution is enabled.

What data is needed to start?

A focused pilot may begin with production orders, BOMs, inventory, purchase orders and delivery dates. More advanced use cases may require machine, quality, maintenance or sensor data.

Are all the agents shown already live in Opernex?

No. The agent categories on this page describe proposed capabilities and use cases. Availability depends on product development, customer systems, integrations, testing and deployment.

Plan your first use case

Where does production lose time today?

Start with one measurable bottleneck—material shortages, schedule exceptions, supplier delays, quality investigations or downtime—and map a practical path from manual work to governed AI assistance.

Discuss your workflow