# AI for InfinityQS SPC: Explain Out-of-Control Signals Using ERP Lots, Orders and Suppliers

> Source: https://netray.co/erp-ai/infinityqs-spc-ai-erp
> Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems.
> Section: AI for ERP

## Short answer

AI for InfinityQS reads SPC characteristics, subgroups, control chart signals and capability indices, and joins them to ERP lots, production orders and supplier receipts so quality engineers can see what changed when a chart goes out of control. A private model runs in the customer's environment, reads data under existing roles, and cites the records behind each answer.

## Kind

platform

## Erp Family

mfg-systems

## Erp Products

- InfinityQS

## Industries

- Manufacturing
- Automotive
- Food and Beverage
- Medical Devices

## Region

Global

## Persona

Quality Manager

## Eyebrow

InfinityQS SPC + ERP

## Intro

InfinityQS is a statistical process control platform used to collect measurements, plot control charts, and monitor capability. Its data model centres on characteristics with specification limits, subgroups of measurements, and rules that flag assignable-cause signals. Depending on edition, it has been deployed on-prem or delivered as a cloud service.

An out-of-control point is only the start of the work. The quality engineer then asks which lot, supplier, operator, tool or material change coincided with the shift. That evidence is in the ERP and often a quality or MES system, so the investigation is manual: export subgroup times, look up orders, check receipts, check changeovers.

Quality teams in regulated settings also need defensibility. Any assistant must show where each statement came from, must not invent a cause, and must keep SPC records unchanged. It should speed the evidence gathering while leaving the root-cause judgement with a person.

This page explains how a private AI layer works with SPC data, what it can honestly add, and where InfinityQS' own reporting or capability analysis is the better tool.

## Pain Points

### Signals without context

**Detail:** A rule violation on an X-bar chart tells you something changed, not what. The candidate causes are in ERP lots, receipts and orders.

### Manual evidence gathering

**Detail:** Engineers export subgroup timestamps and search ERP by hand to find which order, lot and supplier were involved.

### Capability reporting is repetitive

**Detail:** Cpk and Ppk reviews for customers and audits take recurring effort assembling the same charts and commentary.

### Many characteristics, few eyes

**Detail:** Plants monitor hundreds of characteristics. Prioritising which signals deserve attention today is itself a judgment call.

### Cloud AI is a hard sell in quality

**Detail:** SPC data on customer parts often falls under confidentiality terms, so quality teams hesitate to paste it into public AI tools.

## Use Cases

### Signal investigation pack

**Description:** For a flagged subgroup, assemble the production order, material lots, supplier receipts and recent changeovers around that time.

**Erp Objects:** Production orders, lot and serial records, goods receipts, routing operations; SPC subgroups

**Outcome:** Evidence gathering reduced from hours to a reviewable pack; the engineer decides the cause.

### Supplier-linked variation

**Description:** Check whether capability shifts align with changes of supplier lot or vendor.

**Erp Objects:** Purchase receipts, vendor lots, inspection results

**Outcome:** Candidate supplier issues identified earlier and supported with evidence for a supplier corrective request.

### Capability review drafts

**Description:** Draft customer-ready summaries of Cpk and Ppk trends by part and characteristic, with sample sizes shown.

**Erp Objects:** Part master, customer part numbers; capability indices

**Outcome:** Less time on assembling reviews; engineers verify statistics and wording.

### Signal prioritisation

**Description:** Rank current out-of-control and drifting characteristics by risk, using criticality flags from control plans.

**Erp Objects:** Control plan characteristics, part criticality, open orders

**Outcome:** A daily short list so attention goes first to critical characteristics.

### NCR and CAPA drafting

**Description:** Prepare a nonconformance draft from a confirmed signal, with affected lots and quantities.

**Erp Objects:** Nonconformance and CAPA records, lot where-used, shipments

**Outcome:** Faster containment scoping; the quality owner reviews and submits.

### Data collection gap detection

**Description:** Find characteristics with missing or late measurements against the control plan frequency.

**Erp Objects:** Control plan frequencies, production order schedule

**Outcome:** Fewer unnoticed sampling gaps ahead of audits.

### Plain-language chart explanations

**Description:** Explain which Western Electric or Nelson-type rule fired and what it typically indicates, for operators.

**Erp Objects:** Rule configuration, chart signals

**Outcome:** Better operator response with consistent wording; no automatic corrective action.

## Architecture

**Overview:** A read-only connector pulls characteristics, subgroups and signals from InfinityQS, a second read-only path pulls ERP evidence, and a private model assembles explanations. The statistics are computed by deterministic code, not by the model.

**Layers:**

#### SPC data access

**Detail:** Read-only access to InfinityQS data through the interfaces your edition supports, such as supported exports, reporting views or an API; confirm with the vendor for your version.

#### Deterministic statistics

**Detail:** Capability and rule checks are computed by tested code and compared to InfinityQS values; the model narrates, it does not calculate.

#### ERP evidence

**Detail:** Read-only lots, orders, receipts and changeovers matched to subgroup times and part numbers.

#### Private model

**Detail:** Open-weight model hosted on-prem or in a private cloud so measurement data and customer part numbers stay in the customer's control.

#### Audit trail

**Detail:** Each answer lists the records used; drafts for NCR or CAPA need human approval before entering the QMS.

## Deployment Options

### On-prem next to SPC and ERP

**Fit:** Regulated and NDA-bound manufacturers

**Detail:** Model, connector and read views inside the plant network.

### Private cloud tenancy

**Fit:** Companies using cloud SPC with a private AI footprint

**Detail:** Model in the customer's own cloud account, pulling through supported interfaces.

### Report-only mode

**Fit:** Teams starting cautiously

**Detail:** Periodic exports processed offline to produce investigation packs, with no live connection.

## Compliance

### Capability calculations follow customer-specified methods; the assistant uses validated code and records the method used.

**Framework:** IATF 16949 / AIAG SPC

### Evidence for monitoring and measurement is retrieved read-only and linked to records; corrective actions remain under the QMS.

**Framework:** AS9100

### AI is a drafting aid outside the validated record system; approvals and signatures stay in the controlled system.

**Framework:** FDA 21 CFR Part 820 / ISO 13485

### Private hosting keeps part and measurement data off public model services.

**Framework:** Customer confidentiality terms

## Integration Notes

- Confirm which InfinityQS edition and interfaces you have, as export and API options differ by edition and version.
- Map SPC part and characteristic identifiers to ERP item numbers and operations explicitly.
- Align subgroup timestamps with ERP order and lot times, handling time zones and shift boundaries.
- Compute Cpk, Ppk and rule checks in deterministic code and reconcile with InfinityQS outputs before trusting any narrative.
- Keep the assistant read-only on SPC data; never edit measurements or limits.
- Store generated investigation packs with the data snapshot used, so they can be reproduced for an audit.

## Product Fit

### Custom build

**Why:** InfinityQS is a quality system, so this is a custom connector with deterministic statistics and private model serving.

### ERPray

**Why:** Provides the lot, order and supplier evidence from the ERP with queries shown and roles respected.

## Engagement

### Scoping

**Duration:** 1-2 weeks

**Deliverables:**

- Edition and interface review
- Priority characteristics
- ERP key mapping
- Validation approach

### Build

**Duration:** 2-4 weeks

**Deliverables:**

- Read-only SPC access
- Deterministic stats module
- ERP evidence views
- Investigation pack template

### Pilot

**Duration:** 3-5 weeks

**Deliverables:**

- Real signals investigated with engineers
- Accuracy review versus manual work
- Audit trail
- Feedback loop

### Rollout

**Duration:** 2-4 weeks

**Deliverables:**

- More parts and lines
- Runbook
- Quality sign-off on usage rules
- Maintenance plan

## Buyer Questions

- Which InfinityQS interface do you use to read data, and is it supported for our edition?
- Are capability numbers computed by tested code and reconciled with InfinityQS?
- How do you match subgroup times to ERP lots and orders?
- Can the assistant ever change SPC data or limits?
- Where does measurement data go, and which model sees it?
- How are generated NCR drafts controlled before entering the QMS?
- How do you handle customer-specific capability methods?
- When is InfinityQS reporting alone sufficient?

## Faq

### Can an LLM calculate Cpk reliably?

**Answer:** Not on its own. Language models make arithmetic and statistical errors, so capability indices and rule checks should be computed by deterministic, tested code and reconciled with InfinityQS results. The model's role is to retrieve evidence and explain results in plain language, with the numbers coming from the validated calculation.

### Will it tell us the root cause of a signal?

**Answer:** No, and it should not claim to. It assembles candidate evidence such as the lots, suppliers, orders and changeovers around the time of the signal. A quality engineer judges cause. Presenting correlation as cause would be a quality-system risk, so outputs are worded as candidates for investigation.

### Does this work with cloud InfinityQS?

**Answer:** Possibly, depending on the interfaces your edition supports, such as exports or an API. The model and ERP connection can stay in your private environment even when the SPC source is cloud-hosted. Confirm supported data access with the vendor, since options vary by product and version.

### Is this a validated system for FDA or AS9100 use?

**Answer:** The assistant is a drafting and evidence-gathering aid, not a replacement for your controlled quality records. Approvals, signatures and records remain in your QMS. If your quality procedures require validation of tools, scope and document that usage explicitly, and keep human review in every output.

### How does it link SPC data to ERP lots?

**Answer:** By mapping part and characteristic identifiers to ERP items and operations, then matching subgroup timestamps to production order and lot activity. Clock differences and batch boundaries create ambiguity, so the pack lists matches with a confidence note rather than asserting a single answer.

### Can it edit control limits or measurements?

**Answer:** No. Access is read-only by design. Changing limits or data would belong inside InfinityQS with its own controls. The assistant can suggest that limits look stale, but the quality owner makes and records any change through the normal process.

### Is it worth it for a small SPC deployment?

**Answer:** If you monitor a few characteristics and the engineers already know the process, probably not. It pays off when there are hundreds of characteristics, multiple lines and suppliers, and engineers spend significant time assembling evidence after each signal.

## Search terms

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