AI Readiness Checklist for Manufacturing

Sensor and PLC data from production lines is collected and stored in a centralized time-series database.

Equipment communicates via OPC-UA or similar industrial protocols for standardized data exchange.

Manufacturing execution system (MES) data is validated for accuracy and completeness at point of capture.

Asset data models exist to support digital twin simulations and predictive analytics.

Supplier, logistics, and inventory data flows into a unified data platform for end-to-end visibility.

Real-time telemetry from IoT sensors feeds into a streaming pipeline for predictive maintenance models.

Defect data and visual inspection records are digitized and labeled for computer vision training.

Granular energy usage data per machine and line is available for optimization modeling.

Edge devices at production lines enable low-latency AI inference for real-time quality control.

Hybrid architecture balances edge inference with cloud-based training and analytics.

IoT gateways bridge OT and IT networks securely for AI data collection.

Camera and image processing infrastructure supports real-time visual inspection AI.

A purpose-built time-series database handles high-frequency sensor data for predictive models.

Optimized models (ONNX, TensorRT) deploy to edge devices for factory floor inference.

Secure network segmentation enables data flow between OT and IT without compromising safety.

A simulation platform supports digital twin creation and what-if scenario modeling.

Data scientists with manufacturing domain expertise understand OT systems and production processes.

Plant engineers trained in AI concepts can identify automation opportunities and validate models.

Team has access to computer vision expertise for quality inspection and safety monitoring AI.

AI projects include production, quality, maintenance, and IT stakeholders.

Machine operators receive training on AI-assisted tools and human-in-the-loop workflows.

Operations technology and IT teams collaborate effectively with shared AI goals.

A dedicated change management lead drives AI adoption across the shop floor.

Team has skills in deploying and maintaining AI models on edge devices in industrial settings.

AI systems on the shop floor meet IEC 62443 and relevant safety standards.

AI processes integrate with ISO 9001 quality management system documentation.

Policies define when AI decisions require human review, especially for safety-critical processes.

Model updates follow formal change control procedures aligned with manufacturing best practices.

AI-driven process changes are evaluated for environmental impact under ISO 14001.

AI systems in OT environments follow industrial cybersecurity governance frameworks.

AI implementations affecting workers include consultation with labor representatives.

Clear performance baselines are established before AI deployment for measurable comparison.

AI use cases targeting OEE improvement, defect reduction, and throughput optimization are identified.

Each AI project has a quantified business case with payback period and expected savings.

VP of Operations or Plant Manager actively sponsors and resources AI initiatives.

AI strategy aligns with a broader Industry 4.0 digital transformation roadmap.

AI investments target supply chain visibility and resilience as strategic priorities.

AI optimization initiatives contribute to measurable sustainability and carbon reduction goals.

AI maturity is benchmarked against industry peers and smart factory leaders.

AI is positioned to augment workers rather than replace them, with clear communication.