InforUse Case

Infor M3 + planning AI

Infor M3 Supply Chain Planning With AI That Explains the MRP Output

Short answer

AI for Infor M3 supply chain planning reads item, warehouse balance, planning order and purchase order data through M3 APIs and answers planner questions such as why a shortage exists and which supplier or work order is driving it. It does not replace the M3 planning engine; it explains and triages its output, and any change to a planned order or purchase order stays behind human approval.

ERP: Infor M3, Infor M3 Cloud Edition
Industries: Discrete Manufacturing, Distribution, Process Manufacturing
Written for: Supply Chain Planner

Discuss your Infor M3 environment See use cases

A typical M3 planner opens the planning workbench each morning facing hundreds of action messages: release this planned order, pull in that purchase order, push out another. Most are correct, many are noise caused by stale lead times or unrealistic safety stock, and a handful are the genuine shortages that will stop a line next week. Separating those groups is skilled work that depends on remembering why each item is parameterised the way it is.

The data needed to make that call lives in several places. The item master (MITMAS) and item-warehouse parameters (MITBAL) hold lead times, order policy, safety stock and lot sizing. Open supply sits in purchase order lines (MPLINE), manufacturing orders and planned orders. Demand comes from customer order lines (OOLINE), forecasts and dependent demand from bills of material. A planner who can read all of that together beats any single report, but doing it by hand for every exception does not scale.

An AI layer built on those objects is useful in one specific way: it assembles the evidence for a single exception in seconds. Ask why item X is short in warehouse Y in three weeks and it can list the demand lines consuming supply, the purchase orders that are late against their confirmed dates, and the parameter that made the planning engine behave as it did. The planner still decides.

This page describes what that looks like on Infor M3 specifically, how it connects through the ION API Gateway and M3 APIs, and where it is the wrong tool. If your problem is that the planning parameters themselves are wrong, no assistant fixes that; it will, however, make the wrong parameters much easier to find.

What usually gets in the way

The problems we hear most from supply chain planner teams running Infor M3.

Action message overload

Planning runs produce far more messages than a planner can read. Without a way to rank them by real consequence, the loudest items get attention and the quietly critical ones surface only when a customer order is already at risk.

Parameter drift in MITBAL

Lead times, safety stock and order policies set at item introduction are rarely revisited. The engine faithfully plans against numbers that no longer match supplier behaviour, and the output looks authoritative while being wrong.

Shortage root cause takes too long

Tracing a shortage across demand lines, pegged supply, late purchase orders and component availability means jumping between several M3 panels and exports. Each trace takes a planner many minutes.

Supplier promise dates live in email

Confirmed delivery dates change by phone and email while the purchase order line still shows the original date. Planning then runs on stale supply data.

Multi-facility visibility is fragmented

Groups with several facilities and warehouses cannot easily see where surplus in one location could cover a shortage in another without a planner running transfers by hand.

Where AI earns its place in Infor M3

Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.

Shortage explanation on demand

A planner asks why an item is short in a given facility and week. The assistant lists the demand lines and supply lines for that item and highlights the late or unconfirmed supply.

Touches: MITBAL, MITMAS, OOLINE, MPLINE, manufacturing order lines

Outcome: Replaces a multi-screen trace with a single grounded answer the planner can verify against the cited records.

Action message triage

Planning messages are ranked by customer order exposure and days to stock-out, so the planner starts with consequential items instead of alphabetical ones.

Touches: Planned orders, action messages, OOLINE promised dates

Outcome: Typically shifts planner time from reading low-value messages to resolving the few that carry real risk.

Parameter review for chronic exceptions

Items that generate repeat messages are flagged with the parameter likely responsible, such as a lead time shorter than actual receipt history or a safety stock that never gets consumed.

Touches: MITBAL planning parameters, MPLINE receipt history

Outcome: Produces a short, evidence-backed list of parameters worth correcting, reviewed by a planner before any change.

Supplier delay summaries

Open purchase order lines past their confirmed date are grouped by supplier with the affected customer orders, so the buyer can chase with context.

Touches: MPHEAD, MPLINE, OOLINE

Outcome: Gives buyers a prioritised chase list instead of a flat overdue report.

Cross-facility surplus matching

The assistant looks for surplus balances in other facilities that could cover a shortage and drafts a transfer proposal for review.

Touches: MITBAL by facility and warehouse, distribution orders

Outcome: Surfaces transfer options that are easy to miss when each facility plans in isolation.

Forecast versus actual review

Compares forecast and consumption history by item family to point out where forecast bias is inflating planned supply.

Touches: Forecast records, issue transaction history, MITBAL

Outcome: Gives the demand planner a defensible list of families to re-forecast.

S&OP meeting preparation

Assembles a pre-read from open supply, constraints and late orders across facilities, with each statement linked to its source query.

Touches: Customer orders, manufacturing orders, purchase orders, capacity data

Outcome: Reduces the manual spreadsheet assembly that usually precedes a monthly S&OP meeting.

Reference architecture

The AI layer reads M3 through its API programs (MI programs exposed via the ION API Gateway) or a governed reporting copy, applies the same facility and role scoping as M3, and returns answers with the underlying query visible. It sits alongside the M3 planning engine and never writes to planning tables directly.

1
M3 data access
Reads through M3 API (MI) programs and ION API Gateway with a dedicated service identity, or through Infor Data Lake and Compass SQL where that is already deployed for reporting.
2
Planning semantic layer
Maps items, facilities, warehouses, planned orders, purchase orders and customer order lines to the vocabulary planners use, including your own order policies and item groups.
3
Model serving
An open-weight model served with vLLM or Ollama on customer GPUs or a private cloud, so item, cost and supplier data are not sent to a public AI endpoint.
4
Retrieval and agents
Answers are grounded in live queries; agents that propose parameter changes or transfers produce drafts for approval rather than executing changes.
5
Audit and role scoping
Every question, query and answer is logged against the M3 user and their facility authorities.

Integration notes for your ERP team

Uses M3 API (MI) programs through the ION API Gateway with OAuth-based service credentials; no direct write to M3 tables.
Item and balance reads cover MITMAS, MITBAL and MITFAC so facility-specific parameters are not averaged away.
Customer order demand is read from OOHEAD and OOLINE, supply from MPHEAD, MPLINE and manufacturing order structures.
Where Infor Data Lake is deployed, heavy analytical questions can use Compass SQL to avoid loading the transactional system.
Infor Process Automation can be used as the approval workflow for any proposed change, keeping approvals inside Infor tooling.
Custom fields and customer-specific item attributes are discovered during a mapping phase, not assumed.

Deployment options

On-prem next to M3

Manufacturers running M3 on-premises with strict data residency or customer NDA constraints.

Model, retrieval and connector run on customer hardware with no outbound path; M3 is reached over the internal network.

Private cloud for M3 Cloud Edition

Companies on Infor's multi-tenant cloud who still do not want planning data sent to a public model.

A customer-controlled tenant calls the M3 APIs through ION API Gateway with scoped credentials and keeps retrieval and model serving under the customer's own agreement.

Hybrid

Groups with a mix of on-prem and cloud M3 instances after acquisitions.

A central governance layer connects to each instance separately, preserving per-site data residency.

Compliance and data control

How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.

ITAR and EAR data handling

Where item and demand data reveal controlled programs, running inference inside the customer's own boundary lets the customer apply its own access and export controls. Netray does not claim registration on the customer's behalf.

Customer NDAs and flow-downs

Forecast and bill of material data from OEM customers stays on infrastructure the manufacturer controls.

SOX change control

Planning parameter changes proposed by the assistant are drafts with a logged rationale; the actual change follows the customer's existing approval process.

GDPR

Supplier and customer contact data stays inside the customer's environment, simplifying transfer analysis.

Where Netray fits

ERPray

Natural-language answers and planner dashboards over live M3 data, read-only by default with the query shown.

Custom build

Exception-triage agents, parameter-review logic and S&OP packs usually need to be tailored to your planning policies.

How an engagement runs

Phase 1 . 2-3 weeks

Discovery

-Review of planning set-up, facilities and item policies
-Ranked list of planner pain points
-API and data access plan
-Deployment recommendation

Phase 2 . 6-8 weeks

Pilot

-Connector against a test environment
-Shortage explanation and triage for one facility
-Accuracy review against planner-verified answers
-Role scoping validated

Phase 3 . 4-6 weeks

Production

-Production cutover with read-only credentials
-Audit logging live
-Runbook for new questions
-Admin handover

Phase 4 . Ongoing

Scale

-Additional facilities
-Approval-gated agents for transfers and parameters
-Model refresh
-Quarterly accuracy review

Questions to ask any vendor, including us

A short list that separates real Infor M3 AI work from a chatbot demo.

Does the system read M3 through supported APIs, or does it need direct database credentials?
How does it respect facility and warehouse authorities already configured in M3?
Can you show the query behind each shortage explanation so a planner can verify it?
Does anything write to planning data, and if so what approval step sits in front of it?
How are custom fields and our own order policies handled in the semantic layer?
Where does the model run, and can we confirm no planning data leaves our environment?
What happens to answers after an M3 upgrade or a move to the cloud edition?
How is accuracy measured against what our planners already know to be true?

Frequently asked questions

Does AI replace the M3 planning engine?

No. The M3 planning run still generates planned orders and action messages. The AI layer reads that output and the data behind it to explain shortages, rank exceptions and draft proposals. Any change to a planned order, purchase order or parameter goes through human approval and your normal M3 transactions.

Can it run if we are on M3 Cloud Edition?

Yes, through the ION API Gateway and M3 APIs with scoped credentials. The model and retrieval layer can run in a customer-controlled private cloud so data is not sent to a public AI service. Direct database access is not available in the multi-tenant cloud, so the design relies on supported APIs and Data Lake.

Is Infor's own AI enough for planning?

If your needs are covered by Infor's built-in analytics and forecasting, start there. A custom layer makes sense when you need grounded answers across your own parameters, policies and custom fields, or when data residency rules prevent using vendor-hosted AI. Compare on a real shortage trace.

How does it find which parameter caused a bad plan?

It compares planning parameters in MITBAL, such as lead time and safety stock, with observed behaviour from receipt and consumption history. Where they diverge consistently, it flags the item with the evidence. A planner reviews the finding and makes the change through the normal M3 process.

What accuracy should we expect?

Accuracy depends mostly on how well the semantic layer matches your set-up. During a pilot, answers are checked against cases planners have already resolved. We report where answers were wrong and fix the mapping rather than quoting a headline percentage.

Can it work across several facilities?

Yes. It reads balances and demand by facility and warehouse, and it respects each user's authorities. Cross-facility surplus matching produces transfer proposals for review rather than creating distribution orders automatically.

How long does a first version take?

A read-only pilot for one facility typically takes roughly two to three months including discovery, depending on API access, data quality and how many custom fields need mapping. Agents with approval workflows add time after that.

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