AI-Powered Autonomous Factory Blueprint: Practical Guide for General Manufacturing V1.0

Executive Summary

General discrete manufacturing — characterized by high-mix, low-volume, make-to-order production — has long relied on manual experience, single-machine automation, and traditional MES systems. Such fixed-program automation only repeats predefined operations; any material variation, tooling change, or process adjustment requires engineer intervention, resulting in poor flexibility and difficult replication. The whitepaper defines an AI Fully Automated Factory not as a lights-out facility but as a supervised autonomous factory : a unified industrial data foundation supports industrial agents and large models that close the perceive–analyze–decide–execute–feedback loop under human oversight. Routine conditions run autonomously (scheduling, parameter tuning, defect detection, predictive alerts); exceptions, safety interlocks, first-article approvals, and major process changes remain under human authority.

Core conclusion: the biggest bottlenecks are not robots, compute, or large-model hardware but unified master data, standardized processes, clear human–AI responsibility boundaries, and a sustainable MLOps operating mechanism. Brownfield sites should prioritize data connectivity and high-ROI pilots before scaling; greenfield sites must pre-embed sensing, compute, networking, and digital-twin foundations during planning to avoid costly retrofits.

Chapter 1: Industry Background, Characteristics & Pain Points

1.1 Macro Context & Sector Traits

General manufacturing includes machining, metal parts, structural assembly, stamping, rubber/plastic molding, pumps/valves, and automation components. Key traits:

Mixed order modes: predominantly MTO with some MTS, extreme batch-size volatility.

Frequent BOM and process-version changes; high customization.

Mixed-model production lines with heavy changeover effort.

Heterogeneous equipment brands, legacy machines and PLCs lacking standard data interfaces.

Process knowledge tacitly held by senior craftsmen; turnover causes quality/capacity swings.

Long supply chains; external delivery variability propagates into internal scheduling.

Despite widespread ERP, MES, WMS, SCADA, vision, and robot deployments, most enterprises remain at digitalization and point-automation stages. Siloed AI projects cannot drive plant-wide collaborative decisions. Market demands for shorter lead times, batch consistency, full traceability, and rapid changeovers are rising, while labor-intensive growth models show diminishing returns.

1.2 Business-Level Pain Points (Owner Perspective)

Structural labor shortage: Aging operators, high turnover, long training cycles, night-shift quality risks, rising labor costs.

Unstable delivery: Scheduling depends on planner experience; equipment failures, incoming defects, and bottlenecks cascade into late orders.

High quality costs: Manual first-article/patrol/final inspection lets latent defects escape; rework, repair, and claims accumulate; root-cause tracing is difficult.

Low asset efficiency: Reactive and time-based maintenance; unplanned downtime unquantified; spare-parts inventory dilemma (excess ties up capital, shortage stops lines).

Insufficient flexibility: Long changeover cycles make small-batch orders unprofitable; missed market opportunities.

Unclear digital ROI: Fragmented multi-vendor systems, disjointed interfaces, continuous spend with opaque returns.

1.3 IT/Digital Pain Points

Severe data silos: Modbus, Profinet, EtherCAT, proprietary PLC protocols coexist; legacy machines lack interfaces; retrofitting is hard and costly; no data standards.

Fragmented AI projects: Vision inspection, predictive maintenance, parameter optimization developed in isolation; duplicate labeling, compute, no unified model registry, versioning, or drift monitoring; models degrade unattended.

Real-time conflict: Control loops need millisecond response; general LLM inference latency is too high for direct cloud inference; cloud-edge architecture and compute allocation are core challenges.

Industrial AI trust risks: Hallucinations in process-parameter recommendation or defect judgment cause batch scrap; industrial decisions require traceability, explainability, one-click rollback — conflicting with black-box LLMs.

IT/OT boundary governance: AI platforms entering control networks risk intrusion or misoperation triggering physical actions; traditional IT security cannot meet low-latency, high-availability OT requirements.

Talent gap: Rare hybrid profiles spanning process, control, data, AI, MLOps; after vendor exit, internal teams cannot sustain model iteration.

1.4 Three Common Misconceptions Corrected

Misconception 1: AI factory = lights-out/zero-human factory. Correction: General discrete scenarios need supervised autonomy — AI handles routine conditions; humans take over exceptions, safety interlocks, first articles, major changes. Blind pursuit of zero-human is high-cost, low fault-tolerance, unsuitable for most.

Misconception 2: Buy robots/LLMs first; data foundation later. Correction: Without unified master data, stable collection pipelines, and standardized schemas, robots and models remain isolated points — high investment, low return. Data foundation first is prerequisite for scalable AI.

Misconception 3: AI project is one-off delivery. Correction: Industrial AI is a continuous operating system. Process drift, material batch variation, product iterations cause model drift; continuous labeling, retraining, evaluation via MLOps is long-term operational capability, not one-time development.

Chapter 2: Definition, Boundaries, Core Philosophy & Value Model

2.1 Definition & Three Autonomy Levels

AI Fully Automated Factory (Autonomous Manufacturing Factory): built on a unified industrial digital thread connecting R&D, procurement, warehousing, production, quality, equipment, and EHS. Industrial agents and layered AI models (cloud large models + edge lightweight models) perform perception, reasoning, decision, scheduling, and command automated execution units. Continuous result feedback auto-optimizes models and process strategies. Humans supervise, handle exceptions, configure rules, approve processes, and provide safety fallback — forming a continuously improving manufacturing system.

Three autonomy levels for phased benchmarking:

L1 – Assistive Intelligence (current starting point): AI only alerts, statistics, analysis, recommendations; all decisions human-confirmed (e.g., vision alerts, equipment failure warnings, scheduling suggestions).

L2 – Conditional Autonomy (core target): Under stable routine conditions, AI auto-executes, auto-adjusts parameters, auto-dispatches AGVs, auto-triggers maintenance work orders; preset exception rules trigger auto-pause and human escalation.

L3 – Full-Domain Autonomy (long-term vision): Multi-agent collaboration across full flow; autonomous re-planning and process-route adjustment under complex disturbances; humans only high-level supervision and major change approval. Most general manufacturers can capture sufficient commercial value at L2.

2.2 Human–AI Responsibility Boundaries (Must Be Documented Upfront)

AI authority: Routine parameter micro-adjustment, defect classification, equipment health assessment, standard work-order dispatch, standard material pull, auto-report generation, compliance alerts.

Human authority: First-article confirmation, process version changes, tooling changeover approval, batch non-conformance disposition, safety interlock reset, special customer order review, model threshold adjustment, major exception handling.

Red-line rules: Safety-related interlocks, special equipment actions, high-hazard operations — AI may only alarm, never auto-execute.

2.3 Value Model (Direct, Indirect, Strategic)

Direct Quantifiable Value (for ROI calculation):

Equipment OEE improvement: 8–20%

Product defect rate reduction: 15–40%; rework/repair cost reduction

Unplanned equipment downtime reduction: 20–45%

Product changeover time reduction: 25–50%

On-time delivery rate improvement: 10–25%

Safety incident reduction via early hazard identification

Safety stock & spare-parts inventory capital reduction: 10–30%

Indirect Value:

Process experience digitized, reducing reliance on individual experts

End-to-end one-item-one-code traceability for customer audits and claims defense

Flexible order-taking capability enabling high-value small-batch business

Production transparency giving management real-time global visibility

Long-Term Strategic Value:

Build proprietary industrial data assets

Create standardized, replicable line-transformation templates for multi-plant rollout

Establish differentiated manufacturing capability as competitive moat

Chapter 3: Overall Technical Architecture (Core Chapter for IT Leads)

Five-layer architecture: Equipment Perception Layer → Edge Computing Layer → Unified Data Foundation Layer → AI Intelligence Platform Layer → Business Application Layer , complemented by unified security, MLOps operations, and master data governance, using cloud-edge collaboration to balance real-time control and global optimization.

3.1 Layer 1: Equipment Perception & Execution

Includes CNC machines, machining centers, injection molding, stamping, PLCs, robots, AGV/RGV, vision cameras, sensors (vibration, temperature, current, pressure, acoustic), barcode/RFID readers, access control, EHS sensors. Goal: multi-source data collection + receive upper-layer execution commands. IT focus: protocol adaptation, legacy retrofit assessment, collection point mapping, frequency definition, sensor selection, signal isolation to avoid interfering with existing control loops. Two device categories: native Ethernet-enabled new equipment; legacy no-interface equipment retrofitted via non-invasive external modules/sensors, preferably without modifying original PLC programs to reduce downtime risk.

3.2 Layer 2: Edge Computing Layer (Industrial Real-Time Decision Core)

Deploys industrial edge gateways, edge compute boxes, local edge servers. Responsibilities:

Protocol parsing, data cleansing, local caching, pre-processing.

Low-latency AI inference (vision defect detection, real-time equipment anomaly judgment, closed-loop process parameter adjustment).

Local interlock control, offline autonomy: when cloud network fails, edge nodes maintain basic line autonomy without immediate line stop.

Filter high-value anomaly data and sample data for cloud upload to reduce bandwidth.

Latency requirements: control-loop inference ≤ 10–100 ms; statistical/analytical tasks can be relaxed. Deployment strategy: lightweight detection/control models on edge; large models, complex scheduling, process optimization, knowledge Q&A in cloud.

3.3 Layer 3: Unified Industrial Data Foundation (System Bedrock)

Many projects fail by skipping this layer and jumping straight to AI applications. Foundation components:

Time-series database: equipment vibration, temperature, current signals.

Relational database: work orders, BOMs, operations, personnel, materials, quality records.

Graph database / knowledge graph: relationships among materials, tooling, equipment, processes, defects (digital thread).

File & object storage: drawings, process documents, inspection images, videos.

Message middleware: standardized message bus as integration hub for all systems.

Master Data Management (MDM): unified standards for materials, equipment, tooling, operations, suppliers — resolving one-item-multiple-codes and one-code-multiple-items. Core principle: single source of truth — ERP owns material master, PLM owns process/drawing data, MES owns execution data, AI platform consumes only, never maintains independent master data.

3.4 Layer 4: AI Intelligence Platform (Industrial Brain)

Four modules:

Industrial LLM Foundation: On top of general LLM, apply RAG + fine-tuning with enterprise process docs, SOPs, historical failure records, quality reports for domain Q&A, root-cause analysis, process-change assistance. Prohibit direct use of public LLMs for sensitive process/order data.

MLOps Platform: Dataset management, labeling tools, model training, versioning, A/B testing, drift monitoring, performance dashboards, one-click rollback.

Agent Engine: Vertical agents (scheduling, quality, maintenance, warehousing pull) collaborate via business rules + model inference to auto-execute work orders, material pulls, repair tickets across systems.

Digital Twin Engine: Virtual-physical mapping, 3D line visualization, simulation scheduling, changeover simulation, anomaly rehearsal for solution pre-validation and management dashboards.

3.5 Layer 5: Business Application Layer

Role-based apps: Production MES, WMS, EAM, QMS, APS, EHS, Procurement Collaboration, Executive BI, Process Assistant, Mobile Patrol App.

3.6 Integration Architecture Key Points (IT Lead Focus)

Prefer standardized APIs and message bus; minimize hard-coded point-to-point integrations.

Strict zoning: OT control zone, edge zone, data platform zone, office IT zone; unidirectional isolation, least privilege.

Deployment options: on-premise, edge + private cloud hybrid; general manufacturers with process confidentiality prefer private/hybrid over full public cloud.

Chapter 4: End-to-End Business Scenarios (R&D, Production, Quality, Equipment, Warehouse, Supply, EHS)

4.1 R&D & Process (PLM + AI Process Agent)

Scenario 1 – AI Process Generation & Verification: Input drawings, materials, tolerances; retrieve historical similar processes; generate preliminary routing, tool parameters, fixture suggestions; engineer review confirms into library, shortening NPI process authoring cycle.

Scenario 2 – Process Change Impact Analysis: Knowledge graph auto-links affected BOMs, work orders, inventory, WIP, inspection specs; outputs change impact checklist, reducing missed-change risk.

Scenario 3 – Simulation Parameter Optimization: Combine historical machining data; AI recommends cutting parameters, molding temperatures, hold times to improve takt and reduce energy while guaranteeing quality.

4.2 Planning & Scheduling (APS + Intelligent Scheduling Agent)

Traditional APS is rule-based with weak disturbance handling. AI-enhanced APS:

Dynamic scheduling incorporating real-time equipment health, shift rosters, material kitting status, fixture availability.

On equipment failure or material delay, agent auto-replans, presents multiple delivery-date scenarios for planner selection.

Small-batch mixed-line optimization: auto-merge similar work orders to reduce changeovers.

4.3 Production Execution (MES + Line Autonomy)

Autonomous Changeover Guidance: On product switch, AI pushes SOP, parameter templates, inspection standards to terminals; guides operator confirmation; parameters auto-download to machines.

Autonomous Operation Flow: AGV agents dispatch materials and tooling per work-order progress and station load.

WIP Tracking: Auto-bind batch, equipment, operator to build complete digital thread.

4.4 Quality Intelligence (QMS + Vision + Multimodal Inspection)

High-ROI priority scenarios:

Vision appearance defect detection & dimensional assist inspection; edge real-time inference; auto-classify: pass / minor flaw (release tag) / fail (auto-isolate).

Quality root-cause analysis: on batch defects, AI correlates equipment parameters, material batches, ambient temp/humidity, tool life to pinpoint suspect factors.

Quality prediction: predict potential defect risk from machining parameters; preemptively adjust parameters — shift from post-inspection to pre-prevention.

Auto-generate inspection reports; auto-initiate non-conformance disposition tickets.

4.5 Equipment Intelligent Maintenance (EAM – Predictive Maintenance, Top ROI Scenario)

Equipment health portrait: collect vibration, current, temperature, load; AI models assess health score.

Early failure warning: bearing wear, motor anomaly, hydraulic leak alerts; shift from reactive repair to predictive maintenance.

Auto-trigger maintenance work orders & spare-part requests.

Remaining Useful Life (RUL) prediction to optimize PM intervals, avoid over-maintenance waste.

Failure knowledge base: auto-record symptoms, parameters, resolutions into searchable repository for new-hire reference.

4.6 Warehouse & Logistics (WMS)

Material kitting verification agent: pre-check inventory and in-transit quantities before work-order release; early shortage alerts.

JIT material pull: line consumption triggers auto-replenishment request; AGV scheduling delivers.

Obsolete material identification: AI analyzes issue rhythm to flag long-stagnant items; pushes procurement/warehouse action.

4.7 Supply Chain Collaboration

Supplier delivery risk scoring based on historical on-time rate, price volatility, external signals; early contingency planning. Standardized RFQ and reconciliation document auto-generation.

4.8 Safety, Environment, Energy (EHS)

Video AI: missing PPE, unauthorized entry into hazardous zones, fire/smoke detection. Energy anomaly detection: identify idling/ineffective equipment for energy optimization.

Landing Recommendation: Start with predictive maintenance, vision inspection, material kitting alert as first pilots — quick wins, easy to capture business metrics for budget justification.

Chapter 5: Industrial Data Governance, Industrial LLMs & MLOps Operating System

Many firms treat MLOps as a training tool; in industrial settings, MLOps is a continuous operating system — the key differentiator from one-off AI projects.

5.1 Industrial Data Governance

Data quality is the first prerequisite. Common issues: missing, spikes, noise, label errors, missing operating-condition labels. Governance flow:

Master data standardization first: unified coding standards.

Collection pipeline quality monitoring: auto-alert on collection interruption or signal anomalies.

Labeling standards: distinguish normal, defect-type, failure samples; process engineers must verify labels.

Dataset layering: baseline sets, incremental samples, anomaly negative sets; continuous accumulation, not one-off labeling.

Data permissions & masking: tiered control for customer info and critical process parameters.

5.2 Industrial LLM Deployment Essentials

Prohibit direct connection of general LLMs to production control loops. Industrial LLMs serve as knowledge assistant, analysis assistant, root-cause assistant, document assistant — not direct control executors. Path: private vector store ingesting SOPs, drawings, failure reports, quality history; RAG retrieval augmentation; fine-tune base model on high-quality proprietary data if needed; outputs require mandatory human review for decision support.

5.3 Complete MLOps Flow

Data Ingestion → Cleansing & Labeling → Dataset Versioning → Model Training → Offline Validation → Canary Release (alert-only, no auto-execute) → Full Release → Drift Monitoring → Sample Feedback & Retraining → Version Archival & Rollback. Key monitoring metrics: precision, recall, drift indicators (data distribution shift, performance decay); weekly model-effectiveness review cadence.

Chapter 6: Phased Implementation Roadmap — Pilot → Scale → Plant-Wide Autonomy

Two distinct tracks: Brownfield retrofit (most common) and Greenfield new build.

Track A: Brownfield Retrofit (Recommended for Most)

Phase 1 – Foundation & Point Pilot (3–6 months, controlled budget): Target one bottleneck line; deploy 1–2 high-value scenarios (prefer predictive maintenance / vision inspection); build minimum data foundation & edge gateways; establish pre-reform baseline (OEE, defect rate, downtime); deliver quantified benefit report. Exit criteria: pilot KPIs met; standardized retrofit template, collection specs, labeling specs produced. Principle: no plant-wide rollout; start with bottleneck line.

Phase 2 – Horizontal Replication & System Integration (6–18 months): Replicate validated model templates to similar equipment/lines; integrate MES, ERP, WMS via message bus; launch APS intelligent scheduling, quality root-cause analysis; build unified MLOps platform; establish basic multi-agent collaboration. Exit: cross-process data interoperability; AI recommendations embedded in daily workflows, not standalone dashboards.

Phase 3 – Plant-Wide Autonomous Closed Loop (18–30 months): Complete digital thread; multi-agent collaborative scheduling; stable-condition conditional autonomy; standing operations team; mature digital twin; unified security & permission framework. Exit: factory gains self-iteration capability; new lines/products onboard quickly into AI system.

Track B: Greenfield New Build

During plant layout & line planning phase, co-design: sensing points, edge compute layout, network zoning, cabling, master data standards, digital twin foundation; pre-embed collection interfaces to avoid costly post-install sensor additions and rewiring. Equipment procurement specs must mandate open communication protocols; intelligence requirements written into technical tender documents.

Project Governance Rules

Each phase has explicit gate reviews — no progression without passing; reviews must include business owner, IT lead, process lead.

Chapter 7: Investment Estimation, KPI System & ROI Model (Owner Focus)

7.1 Investment Composition

Hardware: edge gateways, compute servers, cameras, sensors, network retrofit, OT isolation gear.

Software platforms: data platform, MLOps, industrial AI platform, digital twin, interface development.

Implementation services: consulting, data governance, model development, integration, commissioning, training.

Annual recurring ops: ops staff, model iteration, compute fees, maintenance, labeling costs.

Note: Legacy protocol adaptation and non-invasive collection are often hidden high-cost items in brownfield; budget must reserve contingency.

7.2 Core KPI Library (Business KPIs vs. IT Technical KPIs)

Business KPIs (executive tracking): OEE, defect PPM, unplanned downtime, changeover time, on-time delivery, inventory turns, quality cost, safety incidents.

IT Technical KPIs (digital lead tracking): Collection success rate, data latency, model precision/recall, model drift rate, system availability, interface success rate, security alerts.

7.3 ROI Calculation Logic

Annualized Benefit = Downtime Reduction Value + Defect Reduction Value + Labor Savings + Inventory Capital Interest Savings + Order Uplift from Delivery Improvement Net Investment = One-time Capex + Annual Opex Static Payback Period = Net Investment / Annualized Net Benefit

Industry reference: Predictive maintenance & vision inspection point solutions — excellent projects achieve 6–12 month static payback; plant-wide systemic build typically 2–4 years. Important: Do not overestimate “direct headcount reduction”; AI value in general manufacturing manifests more in capacity release, quality loss reduction, delivery capability uplift than pure headcount cuts.

Chapter 8: Organizational Change, Talent & Project Governance

AI factory is not a pure IT project but a business transformation; insufficient organizational backing is a top-2 failure cause. Establish cross-functional task force:

Project Sponsor: Owner/GM — resources, budget, cross-department arbitration.

Business Leads: Production Director / Quality Manager — define business targets, acceptance criteria, process rules.

IT/Digital Lead: Architecture, integration, cybersecurity, platform ops.

Process Experts: Provide process knowledge, sample labeling review, model result evaluation.

Ops/Operations Role (permanent): MLOps engineers, data governance specialists — responsible for continuous model iteration.

Two talent paths: internal upskilling + external vendor partnership. Contracts must mandate knowledge transfer so internal team gains model iteration and new-scenario development capability — avoid “vendor leaves, project stalls.” Supporting policies: data asset management, AI model approval-for-release, human–AI permission matrix, exception handling process, model change approval flow.

Chapter 9: OT/IT Converged Security, Data Compliance, Model Trust & Reliability

9.1 Network Segmentation & Isolation

Strict zones: Office, IT Business, Data Platform, Edge, OT Control. Unidirectional gateways / firewalls; prohibit office PCs from direct PLC control network access. AI platform command downlink uses command whitelist + operation audit logs.

9.2 Data Compliance

Customer data, drawings, process recipes are trade secrets. External collaboration (subcontractors, supply chain) requires data NDAs. Follow classification/grading; mask sensitive data. Retain operation logs & model inference logs for quality traceability & audit.

9.3 Model Trustworthiness & Reliability

Phased Release: Start in observation mode (alert only, no auto-execute); after stability verification, enable auto-execute.

Threshold Guards & Hard Interlock Fallback: AI decisions cannot override existing safety hard interlocks.

Explainability: Prefer explainable AI for industrial models; retain evidence for defect judgments & failure warnings.

One-Click Rollback: New model version anomalies → rapid cutover to stable previous version.

Audit Trail: Full log retention for every AI decision and issued command.

9.4 High Availability Assurance

Edge offline autonomy, server cluster redundancy, regular DR drills. OT safety-first principle: any AI system failure defaults to safe shutdown or maintains last stable state; never triggers hazardous actions.

Chapter 10: General Parts Manufacturing Case Study

Mid-sized mechanical parts supplier for construction machinery; high-mix low-volume, 28 machining centers, frequent changeovers, night-shift quality control difficulty, recurring spontaneous spindle failures, OEE stuck ~62%. Approach: Brownfield retrofit; pilot on 4 bottleneck machining centers. Phase 1 Investment: Edge gateways, vibration sensors, compute nodes, predictive maintenance models, vision inspection units; 4-month cycle. Baseline: Spindle unplanned stops 22/year; defect PPM 1,100; avg single-machine changeover 110 min. Phase 1 Results: Spindle warning accuracy 89%; unplanned stops down 41%; vision defect detection rate 95%; pilot line OEE lifted to 73%; static payback ~10 months. Phase 2: Horizontal replication to all machining centers; integrate MES & APS; launch process parameter recommendation agent; establish internal MLOps squad (process + IT co-ops); gradually extend to warehouse kitting, energy optimization. Key Success Factors: Sustained owner sponsorship; deep process expert involvement in labeling & rule review; no rush to full lights-out — tackle bottleneck pain points first; budget reserved for long-term ops headcount.

Chapter 11: Common Pitfalls, Failure Causes & Avoidance Guide

Treating AI factory as automation equipment procurement led by hardware vendors, lacking deep IT/process engagement.

Chasing flashy dashboards & digital twins while underlying data quality is poor — dashboards become inaccurate vanity projects.

Contracts only specify go-live delivery, not post-launch model iteration & knowledge transfer.

Permissions opened too fast — AI directly controls hazardous operations without human fallback.

Metrics only track technical indicators; no pre-defined business baseline → value unevaluable at end.

Underestimating legacy retrofit difficulty; severe budget overruns.

Avoidance Principles: Value-first, baseline-first, gate-reviews, human-fallback, continuous-ops.

Chapter 12: Summary & Next Steps

Building an AI fully automated factory in general manufacturing is essentially digitizing craftsmen’s tacit process experience and converting manual point decisions into data-driven closed-loop autonomy. It is not a tech showcase but a continuous business upgrade.

To Owners: Don’t be seduced by “lights-out” concepts; start from bottleneck pain points, run small pilots, calculate returns, then reinvest based on results. Treat digital as a CEO-level initiative. To IT/Digital Leads: Prioritize solid unified data foundation, master data governance, network isolation; design extensible architecture with open protocols & standard interfaces; establish MLOps as a permanent operational practice.

Standard Action Checklist:

Conduct plant current-state assessment: map bottleneck processes, equipment inventory, existing systems, collection status.

Select 1 bottleneck line + 1 high-value scenario; establish pre-reform KPI baseline.

Produce pilot plan, budget, gate acceptance criteria; launch pilot.

Form cross-functional team; simultaneously define data governance, security, permission policies.

Pilot acceptance & retrospective; codify templates; plan replication roadmap.

Appendices: Selection Self-Checklist & Acceptance Metrics

Appendix A: Vendor Selection Self-Checklist

☐ Supports private/hybrid deployment

☐ Protocol library covers plant’s existing PLC & machine brands

☐ Open APIs & message bus for ERP/MES/WMS integration

☐ Bundled MLOps: model versioning, drift monitoring

☐ Knowledge transfer, training, long-term maintenance services

☐ OT security, isolation, audit logging complete

☐ Model supports canary release & one-click rollback

☐ Distinguishes alert-only vs. auto-execute permission modes

Appendix B: Pilot Acceptance Metrics

Data: Collection success rate ≥ 95%; latency meets business needs.

Model: Per-scenario agreed precision/recall.

Business: Pre/post comparison of OEE, defect rate, downtime, changeover time.

System: Availability ≥ 99.5%; all AI actions auditable & traceable.

Documentation: Collection specs, master data specs, model specs, ops manual, permission matrix delivered.