# AI for proALPHA: grounded answers that respect TISAX-level controls

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

## Short answer

Adding AI to proALPHA means building read access into its PPS, MRP, and APS modules through its API and web services layer, then grounding a model on that data with the same information-security discipline automotive OEMs already expect of their suppliers under TISAX. proALPHA's customer base is heavily German Mittelstand automotive and machinery suppliers, so data residency and audit-trail integrity matter as much as the AI itself.

## Kind

platform

## Erp Family

other

## Erp Products

- proALPHA ERP
- proALPHA PPS
- proALPHA APS

## Industries

- Automotive Supply
- Machinery & Equipment
- Plastics
- Metalworking

## Region

Global

## Persona

CIO

## Eyebrow

proALPHA + private AI

## Intro

proALPHA is a common choice among German Mittelstand manufacturers, particularly automotive suppliers, machinery builders, and plastics and metalworking companies running tight production planning and scheduling (PPS) processes against demanding OEM delivery schedules. Those suppliers live under EDI-driven just-in-time call-offs, PPAP quality documentation, and frequent OEM audits, which shapes what an AI layer actually needs to do.

The recurring pain is not lack of data inside proALPHA's PPS, MRP, and APS modules. It is that turning that data into an answer for a planner facing an EDI exception, or a quality engineer drafting an 8D report against a customer complaint, still requires manual cross-referencing across screens or a custom report request.

Because many proALPHA customers sit inside automotive supply chains assessed under TISAX (Trusted Information Security Assessment Exchange), any new system that touches production or quality data has to be designed with the same information-security posture the OEM relationship already demands, not bolted on afterward.

This page covers what actually needs building: the connector into proALPHA's PPS/MRP/APS data, the semantic layer over your customizations, deployment options that fit a TISAX-aware supplier, and the questions worth asking before starting.

## Pain Points

### EDI/JIT exceptions require manual cross-checking

**Detail:** When an OEM call-off changes or a shipment risks being late, planners cross-reference PPS, MRP, and EDI logs by hand rather than getting a single explained exception.

### Quality documentation is written from scratch each time

**Detail:** 8D and PPAP-related documentation gets drafted manually even though most of the underlying data (inspection results, lot history, prior corrective actions) already exists in proALPHA.

### Customizations are thinly documented

**Detail:** Years of scripting and configuration built by proALPHA partners capture business logic that is rarely written down anywhere staff can find it.

### TISAX expectations constrain tool choice

**Detail:** Suppliers assessed under TISAX cannot casually adopt a cloud AI tool that routes production or quality data through a third party without review.

### APS scheduling changes are hard to explain quickly

**Detail:** When advanced planning and scheduling reshuffles the shop calendar, explaining why to a customer-facing planner or a shift supervisor takes longer than the reshuffle itself.

## Use Cases

### EDI/JIT call-off exception assistant

**Description:** Explain what changed in an incoming EDI call-off, what it means for current PPS/APS plans, and what action is needed, instead of a planner reading raw EDI logs.

**Erp Objects:** EDI interface tables, PPS and APS schedule data, open order records

**Outcome:** Cuts the time to identify and act on a call-off change from a manual log review to a direct explanation.

### Natural-language query over production and inventory

**Description:** Let planners and managers ask direct questions about open orders, inventory, and work-in-process without a new custom report for every variant.

**Erp Objects:** proALPHA API / web services read access to PPS, MRP, and inventory data

**Outcome:** Reduces routine report requests to genuinely novel questions.

### 8D and quality documentation drafting

**Description:** Draft 8D report sections and PPAP-adjacent documentation from inspection, lot, and prior corrective-action data already in the system.

**Erp Objects:** QM module records, lot and traceability data, prior NCR history

**Outcome:** Shortens time to a reviewable 8D draft after a customer complaint is logged.

### APS scheduling change explanation

**Description:** Summarize in plain language why an advanced planning and scheduling run reprioritized the shop calendar, for supervisors and customer-facing planners.

**Erp Objects:** APS schedule and constraint data, work center capacity records

**Outcome:** Reduces time supervisors spend reverse-engineering scheduler decisions.

### Purchase order and vendor follow-up automation

**Description:** Draft vendor follow-up communications for late or at-risk POs, referencing line-level detail and vendor history.

**Erp Objects:** Purchasing records, vendor master data

**Outcome:** Turns a manual PO-chase task into a reviewed, ready-to-send draft list.

### Engineering change and BOM impact summaries

**Description:** Summarize which open orders, inventory, and in-process jobs are affected before an engineering change is released.

**Erp Objects:** BOM and routing tables, cross-referenced with open order data

**Outcome:** Surfaces affected orders in minutes instead of a manual cross-check.

### Shift handover assistant

**Description:** Summarize the state of open jobs, exceptions, and quality holds at shift change, grounded in actual shop-floor data rather than a verbal handover alone.

**Erp Objects:** Shop floor transaction data, quality hold records, open job status

**Outcome:** Reduces information lost between shifts on active exceptions.

## Architecture

**Overview:** A proALPHA AI deployment reads through the platform's API and web services layer, adds a semantic layer over PPS/MRP/APS customizations, serves a model on infrastructure that meets the same information-security bar as the OEM relationships driving the business, and enforces proALPHA's own permissions on every query.

**Layers:**

#### ERP connectors

**Detail:** Read access via proALPHA's API and web services layer for PPS, MRP, APS, and QM data, supplemented by a read-only database replica where needed.

#### Data and semantic layer

**Detail:** A documented mapping of customized fields and configuration into consistent business terms, built from configuration review and staff interviews.

#### Model serving

**Detail:** Open-weight models served with vLLM or Ollama on customer-owned GPUs or a private, single-tenant cloud environment sized to actual load.

#### Retrieval and agents

**Detail:** Retrieval-augmented generation over the semantic layer plus structured query tools, with any write-back routed through a human approval step.

#### Governance and audit

**Detail:** Query-level logging mapped to proALPHA user permissions, built to satisfy the same audit expectations a TISAX assessment already places on production and quality systems.

## Deployment Options

### Air-gapped on-prem

**Fit:** Automotive and defense-adjacent suppliers under strict OEM or TISAX data-boundary expectations

**Detail:** Model, retrieval layer, and connector run entirely inside the customer network with no outbound path for production or quality data.

### Private or sovereign cloud

**Fit:** proALPHA customers without in-house GPU capacity who still need EU-resident, single-tenant infrastructure

**Detail:** Runs in a customer-controlled cloud tenancy rather than a shared multi-tenant AI service.

### Hybrid

**Fit:** Multi-plant proALPHA estates with mixed sensitivity across sites

**Detail:** Sensitive plants run on-prem while others use private cloud, unified by a shared semantic layer and governance model.

## Compliance

### Design connector, storage, and logging controls to the same information-security bar assessed under TISAX, since most proALPHA customers in automotive supply chains already operate under it.

**Framework:** TISAX

### Keep personal data inside the same jurisdiction and access controls as the source proALPHA environment, with minimal default retention.

**Framework:** GDPR / DSGVO

### Involve the Betriebsrat early on any tool touching shop-floor or staff activity data, and design logging around process improvement rather than individual monitoring.

**Framework:** Works council (Betriebsrat) co-determination

### Fit the AI layer's controls inside an existing ISMS scope rather than standing up a parallel, ungoverned system.

**Framework:** ISO 27001

### Keep the AI layer read-grounded with full query logging that leaves original PPS and QM records untouched, supporting rather than complicating supplier quality audits.

**Framework:** IATF 16949 audit-trail expectations

## Integration Notes

- proALPHA's API and web services layer is the supported path for reading PPS, MRP, APS, and QM data; treat undocumented direct database access as a fallback, not the default.
- Customizations built by proALPHA implementation partners need to be inventoried and documented before the model relies on them.
- EDI interface logs and call-off history are often the highest-value data source for automotive suppliers and deserve early priority in the connector build.
- Multi-plant proALPHA installs need explicit schema reconciliation across sites before cross-site questions can be answered reliably.
- Any write-back to proALPHA should go through the platform's own validation logic via the API, not a raw database write.
- Quality and traceability data (lot history, inspection results) should be treated with the same access discipline as production data given its role in OEM audits.

## Product Fit

### Custom build

**Why:** proALPHA is not part of Netray's packaged connector set today, so integration is scoped as a custom engagement against proALPHA's API and web services layer, built to the information-security expectations its automotive supplier base already operates under.

## Engagement

### Discovery

**Duration:** 2-3 weeks

**Deliverables:**

- Inventory of PPS/MRP/APS customizations in scope
- API and connectivity plan
- TISAX-aligned security review of the proposed architecture
- Prioritized use-case shortlist

### Pilot

**Duration:** 6-8 weeks

**Deliverables:**

- Working connector for one or two use cases
- Semantic layer covering the pilot scope
- Model serving stood up in the target environment
- Pilot results reviewed against agreed success criteria

### Production

**Duration:** Ongoing

**Deliverables:**

- Hardened connector and monitoring
- Full audit logging in place
- User training and rollout plan
- Change-management process for configuration changes

### Scale

**Duration:** Ongoing

**Deliverables:**

- Additional use cases added to the same platform
- Additional plants or sites onboarded
- Periodic model and infrastructure sizing review

## Buyer Questions

- Has the vendor worked with proALPHA's PPS, MRP, and APS modules before, or are they assuming a generic ERP schema?
- Does the proposed architecture meet the information-security bar your TISAX assessment already expects, or does it introduce a new gap?
- Where does the model run, and does any production or quality data leave the customer network or tenancy?
- Is every write-back gated behind a human approval step, enforced technically rather than just by policy?
- How is access control enforced against proALPHA's own permission model?
- What happens to EDI exception handling if the connector or model is unavailable?
- Has the Betriebsrat been consulted on any tool that could touch shop-floor activity data?
- Can the customer retain the connector and semantic layer if they later part ways with the vendor?

## Faq

### Can AI on proALPHA meet TISAX expectations?

**Answer:** It can, as long as the connector, model serving, and logging are designed to the same information-security controls TISAX already assesses, rather than treated as a separate system outside that scope. This is a design decision, not something available by default in any AI tool.

### Does adding AI to proALPHA require sending production data to a cloud vendor?

**Answer:** No. Open-weight models can be served on customer-owned GPUs or in a private, single-tenant cloud, with retrieval built directly against proALPHA's API, so production and quality data never need to leave the customer's control.

### What is the highest-value first use case for an automotive supplier on proALPHA?

**Answer:** EDI and JIT call-off exception handling is usually the strongest first case, since it combines a clear daily pain point, data that already exists in the system, and a fast path to a measurable time saving for planners.

### How does this help with 8D or PPAP documentation?

**Answer:** It drafts the report sections from data already in proALPHA (inspection results, lot history, prior corrective actions) for a quality engineer to review and finalize, rather than writing them from scratch each time a complaint comes in.

### How long does a first proALPHA AI pilot take?

**Answer:** A focused pilot on one or two use cases typically runs 6 to 8 weeks after a 2 to 3 week discovery phase that inventories customizations and reviews the architecture against TISAX-aligned expectations.

### Does the Betriebsrat need to be involved?

**Answer:** Where one exists, yes, and early. Any tool that could be read as touching shop-floor or staff activity data typically falls under co-determination rights, and it is far easier to design logging around that from the start than retrofit it later.

### Is this only relevant to automotive suppliers?

**Answer:** No. Machinery, plastics, and metalworking manufacturers on proALPHA face the same underlying pattern of customized PPS data and manual exception handling, even without a direct TISAX requirement.

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