FAQS

Mad-Ai: Enterprise AI for Industry 4.0 Quality Management

Security & Data Governance: Zero-Leakage Architecture

To meet the rigorous standards of automotive (TISAX) and aerospace (AS9100) IP, MAD-Ai utilizes a Private-by-Design framework.

  • Isolated Data Silos: Customer data is logically isolated via multi-tenant partitioning; proprietary FMEA and 8D logic are never used to train global LLMs (Large Language Models).

  • Encryption Standards: Secured via AES-256 at rest and TLS 1.3 in transit, meeting FIPS 140-2 requirements.

  • Identity Management: Full integration with SAML 2.0/OIDC for SSO/RBAC (Azure AD, Okta, Ping Identity).

Compliance Framework: Audit-Ready AI

MAD-Ai is purpose-built to automate documentation for IATF 16949:2016 and ISO 9001:2015 workflows.

  • Traceability: Every agent-generated output includes a "Chain of Thought" (CoT) log, providing a deterministic audit trail for decision-making logic, satisfying Clause 7.5.3.2.1 document retention requirements.

  • Human-in-the-Loop (HITL): High-stakes approvals, such as PPAP (Production Part Approval Process) submissions, require multi-stage human verification to maintain QMS hierarchy and accountability.

The MAD-Ai Quality Suite: Digital Agent Directory

The suite interfaces with ERP/MES/QMS stacks (e.g., SAP, Oracle, Plex, ETQ) via RESTful APIs to automate four core domains:

Agent NameCore ResponsibilityKey Deliverables (Standard-Compliant)RICK (D-QE)Internal Quality Engineering8D Creation, r-PFMEA, Fishbone/5-Why Root Cause Analysis.ADRIANA (SQE)Supplier Quality Management8D Grading, PPAP Validation (Levels 1-5), Supplier Scorecards.AMANDA (QS)Quality Systems & AuditsIATF 16949 Gap Analysis, Audit Prep, Internal Document Control.AL (STANDARDS)Regulatory InterpretationFMVSS, ECE, CCC, VDA, and AIAG-VDA FMEA Handbook.

Performance Benchmarks: Quantifiable Efficiency

Measured against a baseline of manual engineering hours for a Tier-1 Automotive Supplier (2024 Study).

  • Training Plan Development: +9,000% Gain (Reduction from 15 hours of curriculum design to <10 minutes via automated skill-gap mapping).

  • Work Instruction Generation: +3,200% Gain (Transition from 4 hours of manual drafting to 7.5 minutes via CAD/BOM-to-text synthesis).

  • Supplier Quality Management: +2,900% Gain (8D review time reduced from 120 minutes to 4 minutes per incident).

  • Root Cause Analysis (RCA): +1,500% Gain (Analysis of historical defect logs accelerated from 8 hours to 30 minutes).

How does the MAD-Ai 8D Grader improve supplier quality?

The 8D Grader uses Natural Language Understanding (NLU) to score corrective action reports against a standardized AIAG-compliant scorecard. It identifies logic gaps in D3 (Containment) and D5 (Root Cause), preventing "symptom-only" fixes and improving First-Time-Through (FTT) acceptance rates.

Can MAD-Ai solve "Tribal Knowledge" loss?

Yes. Using Retrieval-Augmented Generation (RAG), MAD-Ai ingests unstructured data—maintenance logs, veteran engineer interviews, and handwritten notes—into a Private Knowledge Graph. This ensures expert intuition is digitized and searchable on the shop floor.

Does MAD-Ai automate Engineering Change Notices (ECN)?

Yes. MAD-Ai performs automated Impact Analysis during the ECN process, detecting mismatches between engineering changes and current inventory to predict "at-risk" stockouts and scrap costs.

What global safety standards does the AI support?

The system provides real-time interpretation for:

  • Safety: FMVSS (USA), CMVSS (Canada), ADR (Australia).

  • Certification: CCC (China), ECE (UN/Europe), GB Standards.

  • Industry: IATF 16949, VDA (Germany), ISO 9001, and JIS (Japan).