Manufacturing SystemsERP Platform

Manhattan Active WMS + private AI

AI for Manhattan Active WMS: Answers From Your Own Warehouse Data, Next to Your ERP

Short answer

AI for Manhattan Active WMS places a privately hosted model beside the warehouse system and the ERP, reads waves, tasks, inventory and shipment data through the platform's APIs and event feeds, and answers warehouse questions in plain English with the underlying query shown. Manhattan ships its own AI inside the product, so the case for an external layer is cross-system questions that join the WMS to ERP orders, lots and supplier data.

ERP: Manhattan Active WMS
Industries: Manufacturing, Distribution, Aerospace, Electronics
Written for: VP Supply Chain

Discuss your Manhattan Active WMS environment See use cases

Manhattan Active WMS is a cloud-native, microservices-based warehouse system, and that shapes everything about how AI can sit next to it. Unlike a classic on-prem WMS, there is no database for you to point a reporting tool at. Data leaves the platform through APIs, event streams and scheduled extracts, which is cleaner than a shared schema but means the integration design matters more than it would with a SQL-visible system.

For a manufacturer or a distributor feeding production lines, the daily questions are rarely only about the warehouse. A supervisor wants to know which open pick waves are blocking a work order release. A planner wants to know whether the inbound ASNs for a constrained component are actually received and putaway, or sitting in a dock door. Those questions need WMS facts joined to ERP facts, and neither system alone holds both.

Manhattan has been adding its own assistive and agent capabilities into the Active platform, and for questions that live entirely inside the WMS those are the first thing to evaluate. A private layer earns its place when the question crosses systems, when policy forbids sending lot, customer or export-controlled data to another vendor's AI service, or when you want one query interface across more than one WMS after an acquisition.

This page describes how that external layer is built, which Manhattan data surfaces it uses, where it genuinely helps warehouse and planning staff, and what to ask any vendor before connecting a model to a live fulfilment operation.

What usually gets in the way

The problems we hear most from vp supply chain teams running Manhattan Active WMS.

WMS answers and ERP answers live in separate screens

Whether a shortage is a receiving problem, a putaway problem or a purchasing problem takes a planner three systems and a phone call. The WMS knows where the pallet is; the ERP knows why it matters.

Exception queues grow faster than people can read them

Short picks, inventory adjustments, replenishment failures and stuck tasks pile up in work queues. Supervisors triage by habit and by who shouts loudest, not by downstream impact on production or shipments.

No database to query when the standard screens run out

A cloud-native platform exposes APIs and configured reports, not a schema. Ad hoc questions outside the shipped views turn into a request to an integrator or a vendor support ticket.

Configuration knowledge is concentrated in a few people

Wave templates, allocation rules, task prioritisation and slotting parameters are often understood by one or two admins. When they leave, nobody can say why the system behaves as it does.

Peak season exposes thin visibility

During volume peaks the cost of not knowing which orders are at risk of missing a carrier cutoff is high, and the tools to answer that question quickly are built for dashboards, not for follow-up questions.

Where AI earns its place in Manhattan Active WMS

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

Cross-system shortage explanation

Ask why a work order cannot release and get a chain of evidence: the component, its inbound ASN status, receiving and putaway state in the WMS, and the open purchase order in the ERP.

Touches: WMS inventory and receipts, ASN, ERP work order, purchase order lines

Outcome: Turns a multi-system hunt that takes a planner a good part of a morning into a question answered in seconds, with sources listed.

Wave and task exception triage

Summarise the open exception queue ranked by affected orders, carrier cutoff and linked production demand, with a drafted note for the shift supervisor.

Touches: Waves, pick tasks, short picks, shipment cutoffs, sales order lines

Outcome: Moves supervisors from reading the whole queue to reviewing a ranked shortlist; ranking quality depends on how clean the order priority data is.

Inventory discrepancy investigation

When on-hand differs between the WMS and the ERP, trace recent adjustments, cycle counts, receipts and moves for the item and location to propose a likely cause.

Touches: Inventory by location, adjustments, cycle count results, ERP item balances

Outcome: Shortens reconciliation work; the assistant proposes causes, a person approves any adjustment.

Inbound visibility for planners

Planners ask which supplier shipments are physically at the dock, received but not put away, or not yet arrived against their promised dates.

Touches: ASN, receipts, dock appointments, ERP purchase order schedule lines

Outcome: Replaces phone calls to the receiving office with a self-serve answer for routine status questions.

Configuration explainer

Read exported configuration (wave templates, allocation and task rules) and answer why a given order was allocated or released the way it was.

Touches: Wave templates, allocation strategies, task types, rule configuration exports

Outcome: Reduces dependence on one or two configuration experts; accuracy depends on how complete the configuration export is.

Customer service order status

Customer service asks where an order is, in plain English, and receives status joined from WMS fulfilment and ERP order data with a draft reply for review.

Touches: Order status, shipment, carrier tracking, ERP sales order header

Outcome: Cuts interruptions to warehouse leads for routine where-is-my-order questions.

Peak-readiness review

Compare current open order volume, labor plan inputs and dock schedule with last season's patterns to flag cutoffs at risk.

Touches: Order backlog, shipment cutoffs, labor and dock data exports

Outcome: Gives operations an early list of risks to act on; it informs, it does not replace the planning team's judgement.

Reference architecture

The AI layer reads from Manhattan Active WMS through its published APIs and event or extract feeds, lands a governed copy of the needed entities in a store you control, joins it with ERP data, and serves a private model on top. It does not write back by default.

1
WMS data feed
Pulls waves, tasks, inventory, receipts and shipment data through the platform's REST APIs and event or scheduled extract mechanisms your Manhattan team enables, using a dedicated read-only integration user.
2
ERP connector
Reads orders, work orders, purchase schedules and item masters from the ERP so that WMS facts can be joined to the demand that gives them meaning.
3
Semantic layer
Maps warehouse concepts (wave, task, LPN, ASN, location) and ERP concepts to a shared vocabulary so the same question works across both systems.
4
Model serving
Open-weight model served with vLLM or Ollama on customer GPUs or in a private cloud tenant, with retrieval over the governed copy and over exported configuration documents.
5
Governance and audit
Every question, generated query and source record is logged. Any suggested write, such as an adjustment request or a task reprioritisation, goes to a person for approval through the system's own workflow.

Integration notes for your ERP team

Manhattan Active is delivered as a cloud service, so there is no direct database access; the integration uses published REST APIs and the event or extract feeds your Manhattan team provisions.
Use a dedicated read-only integration user and scope its permissions to the entities the AI layer needs, not a broad administrative credential.
Expect to maintain a governed copy of the needed entities for analytical questions; calling live APIs for every question will hit rate and latency limits.
Join keys between WMS and ERP (item, order number, lot, ASN reference) must be agreed early; mismatched identifiers are the usual cause of wrong cross-system answers.
Treat any write-back (adjustment request, task priority change) as a drafted request that flows through the system's own approval path, never a direct API write from the model.
Check which Manhattan AI and agent features are already licensed before scoping; do not rebuild what the platform already does inside its own boundary.

Deployment options

Private cloud tenant

Most Manhattan Active customers, since the WMS itself is a cloud service.

Model, store and ERP connector run in a dedicated VPC or sovereign cloud tenant under your own agreement, pulling from Manhattan APIs over private connectivity where available.

On-prem next to the ERP

Manufacturers with an on-prem ERP and policy against placing ERP data in a public cloud.

The model and governed store sit in your data centre; only the outbound pull of WMS data from the Manhattan cloud crosses the boundary, and no ERP data goes the other way.

Hybrid

Groups running Manhattan at some sites and another WMS at others.

One query layer federates across systems, with per-site data residency preserved and role mapping kept per source.

Compliance and data control

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

Customer and export-control obligations (ITAR, EAR)

Where parts or technical data are export controlled, the architecture keeps ERP and lot data inside your boundary so your own compliance program decides who may query what. Netray does not hold export registrations; the customer's controls apply.

FDA 21 CFR Part 11 and lot genealogy (where applicable)

The layer is read-only by default and logs every query, which supports your audit trail; electronic records of record remain in the WMS and ERP.

GDPR and regional data protection

Delivery addresses and consignee names are personal data. They can be excluded or masked from retrieval by default and included only for roles that need them.

SOC 2 style downstream questionnaires

Access logging, role mapping and no third-party model egress give concrete answers to customer security reviews.

Where Netray fits

ERPray

Natural-language answers and dashboards that join WMS facts to ERP demand, with the query shown and read-only access by default.

DataRay

Useful when the WMS extracts, carrier files and exported configuration documents need to be queried together on-prem alongside ERP tables.

Custom build

Bespoke agents such as exception triage with drafted supervisor notes, built around your wave and task design.

How an engagement runs

Phase 1 . 2-3 weeks

Discovery

-Inventory of available Manhattan APIs, events and extracts
-Join-key map between WMS and ERP
-Ranked list of cross-system questions
-Deployment recommendation

Phase 2 . 6-8 weeks

Pilot

-Governed WMS copy refreshed on schedule
-3-4 cross-system use cases live for a pilot team
-Accuracy review against known answers
-Audit logging validated

Phase 3 . 4-6 weeks

Production

-Cutover with read-only service accounts
-Role mapping to existing WMS and ERP roles
-Runbook for adding questions
-Admin handoff documentation

Phase 4 . Ongoing

Scale

-Additional sites and distribution centres
-Approval-gated agents for exception drafting
-Model refresh as open-weight options improve
-Quarterly accuracy and usage review

Questions to ask any vendor, including us

A short list that separates real Manhattan Active WMS AI work from a chatbot demo.

Which questions can Manhattan's own AI features already answer inside the platform, and where do they stop?
How is WMS data extracted from the cloud service, how often, and who owns the copy?
Which identifiers join WMS records to ERP orders and lots, and how are mismatches handled?
Can you show the generated query and source records for every answer?
Where does the model run and what leaves our network?
Is the integration read-only, and how is any proposed change approved?
How does the layer behave during peak volume when the WMS is already under load?
What do we keep if we stop: connector code, prompts, logs?

Frequently asked questions

Does Manhattan Active WMS already include AI?

Manhattan has been building assistive and agent features into its Active platform, so evaluate those first for questions that stay inside the WMS. An external private layer is for questions that join the WMS to ERP data, for policies that prohibit sending data to another AI service, or for a single interface across several systems.

Can an AI layer read Manhattan data without direct database access?

Yes. Manhattan Active is a cloud service, so the integration uses its APIs and the event or extract feeds your team enables. A governed copy of the needed entities is kept in a store you control so analytical questions do not hammer live APIs.

Will the AI change inventory or tasks in the WMS?

Not by default. The layer is read-only. Where an action makes sense, such as an adjustment request or a task reprioritisation, it is drafted and sent through the system's own approval workflow for a person to confirm.

Is a private model really necessary for warehouse data?

Not always. If your data policy allows a vendor's hosted AI and the questions are WMS-only, the platform's own features may be enough. Private hosting matters for export-controlled, customer-restricted or sensitive lot and consignee data, and for joining WMS and ERP data under your own controls.

How accurate are cross-system answers?

Accuracy depends mostly on identifier hygiene between the WMS and the ERP, and on data freshness. We validate against known-correct answers during the pilot and show the query and sources so a user can check any result.

What hardware does this need?

A mid-sized open-weight model for question answering runs on a small number of GPUs, or in a private cloud tenant. The governed data store is modest by comparison. Sizing depends on concurrent users and the model class chosen during discovery.

Can this help with peak season?

It can speed up exception triage and surface orders at risk of missing carrier cutoffs. It does not replace labor planning or slotting decisions, and it works best when order priority and cutoff data are clean before the peak starts.

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