proALPHA + private AI
AI for proALPHA: grounded answers that respect TISAX-level controls
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.
- ERP
- proALPHA ERP, proALPHA PPS, proALPHA APS
- Industries
- Automotive Supply, Machinery & Equipment, Plastics, Metalworking
- Written for
- CIO
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.
What usually gets in the way
The problems we hear most from cio teams running proALPHA ERP.
EDI/JIT exceptions require manual cross-checking
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
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
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
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
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.
Where AI earns its place in proALPHA ERP
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
EDI/JIT call-off exception assistant
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.
Touches: 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
Let planners and managers ask direct questions about open orders, inventory, and work-in-process without a new custom report for every variant.
Touches: 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
Draft 8D report sections and PPAP-adjacent documentation from inspection, lot, and prior corrective-action data already in the system.
Touches: 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
Summarize in plain language why an advanced planning and scheduling run reprioritized the shop calendar, for supervisors and customer-facing planners.
Touches: 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
Draft vendor follow-up communications for late or at-risk POs, referencing line-level detail and vendor history.
Touches: 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
Summarize which open orders, inventory, and in-process jobs are affected before an engineering change is released.
Touches: 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
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.
Touches: Shop floor transaction data, quality hold records, open job status
Outcome: Reduces information lost between shifts on active exceptions.
Reference architecture
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.
- 1
ERP connectors
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.
- 2
Data and semantic layer
A documented mapping of customized fields and configuration into consistent business terms, built from configuration review and staff interviews.
- 3
Model serving
Open-weight models served with vLLM or Ollama on customer-owned GPUs or a private, single-tenant cloud environment sized to actual load.
- 4
Retrieval and agents
Retrieval-augmented generation over the semantic layer plus structured query tools, with any write-back routed through a human approval step.
- 5
Governance and audit
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.
Integration notes for your ERP team
- 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.
Deployment options
Air-gapped on-prem
Automotive and defense-adjacent suppliers under strict OEM or TISAX data-boundary expectations
Model, retrieval layer, and connector run entirely inside the customer network with no outbound path for production or quality data.
Private or sovereign cloud
proALPHA customers without in-house GPU capacity who still need EU-resident, single-tenant infrastructure
Runs in a customer-controlled cloud tenancy rather than a shared multi-tenant AI service.
Hybrid
Multi-plant proALPHA estates with mixed sensitivity across sites
Sensitive plants run on-prem while others use private cloud, unified by a shared semantic layer and governance model.
Compliance and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
TISAX
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.
GDPR / DSGVO
Keep personal data inside the same jurisdiction and access controls as the source proALPHA environment, with minimal default retention.
Works council (Betriebsrat) co-determination
Involve the Betriebsrat early on any tool touching shop-floor or staff activity data, and design logging around process improvement rather than individual monitoring.
ISO 27001
Fit the AI layer's controls inside an existing ISMS scope rather than standing up a parallel, ungoverned system.
IATF 16949 audit-trail expectations
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.
How an engagement runs
Phase 1 . 2-3 weeks
Discovery
- -Inventory of PPS/MRP/APS customizations in scope
- -API and connectivity plan
- -TISAX-aligned security review of the proposed architecture
- -Prioritized use-case shortlist
Phase 2 . 6-8 weeks
Pilot
- -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
Phase 3 . Ongoing
Production
- -Hardened connector and monitoring
- -Full audit logging in place
- -User training and rollout plan
- -Change-management process for configuration changes
Phase 4 . Ongoing
Scale
- -Additional use cases added to the same platform
- -Additional plants or sites onboarded
- -Periodic model and infrastructure sizing review
Questions to ask any vendor, including us
A short list that separates real proALPHA ERP AI work from a chatbot demo.
- 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?
Frequently asked questions
Can AI on proALPHA meet TISAX expectations?
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?
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?
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?
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?
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?
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?
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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Talk it through with an engineer who knows proALPHA ERP
Bring one real question your team cannot answer from the ERP today. We will map the data path, the model, and where it runs, and tell you honestly if AI is the wrong tool for it.