AI for Manufacturing & Production
From the sensor to the shop floor to the simulation — build the AI-driven factory that doesn't break.
An industrial flagship for plant managers, process/quality engineers, maintenance leads and Industry 4.0 teams. Work the real stack — IIoT and predictive maintenance, computer-vision inspection, process optimization, digital twins, generative/agentic AI on the shop floor and CV-based safety — on the platforms manufacturers actually buy in 2026.
- 41 hours
- 14 modules
- Cohort-based · flexible batches
Pricing is set per cohort and organisation — Talk to us.
Outcomes
What you'll be able to do
Concrete, demonstrable skills — the kind you can show in an interview or put to work on day one.
Ingest and model IIoT data for predictive maintenance, with awareness of 2026 platform retirevals
Train and deploy computer-vision quality inspection and tune it for false-accept rate
Optimize process setpoints for yield and run digital-twin what-if simulations
Build AI-driven demand forecasts and constrained production schedules
Deploy generative/agentic copilots over plant documents and historian data
Curriculum
The modules
Sequenced to build on each other. Each module is a first-class unit — the same ones our AI recommender draws on to map a personalised track.
14
modules
104.5
hours total
Industry 4.0 Foundations: IIoT, Data & the Connected Plant
The architecture of a smart plant — sensors, PLCs, edge gateways, OT/IT integration and the time-series historian — mapping raw sensor streams to decisions and the security split between OT and IT.
Fundamentals of Robotics
Robot components, degrees of freedom, classification types, industrial and collaborative robotics, the robotics ecosystem, mobile robot categories, AI-enabled robots, and building an automation strategy aligned to ROI.
Sensors, Vision, and Perception
Proximity, distance and force sensors, 2D/3D cameras, stereo vision, calibration and fusion, machine vision for detection and tracking, LiDAR, ultrasonic and tactile sensing.
Predictive Maintenance I — Sensor Data & Anomaly Detection
Build vibration/temperature/current anomaly-detection models on real equipment data and turn anomalies into work orders — covering the 2026 platform shifts (AWS Lookout for Equipment retiring Oct 2026, Azure Anomaly Detector retired) and the surviving paths.
Predictive Maintenance II — Remaining Useful Life & Reliability
Move from 'is it abnormal?' to 'how long until failure?' with remaining-useful-life regression and a reliability-centered maintenance plan that prioritizes assets by criticality and risk.
Computer Vision Quality Inspection
Train and deploy a defect-detection model on a real line — from golden units to anomaly detection — integrate it into a vision station, and make the turnkey (Cognex/Keyence) vs build (LandingLens/Roboflow) call.
Process Optimization & Yield Improvement
Find the setpoints (temperature, speed, pressure, recipe) that maximize yield and minimize scrap using ML on process history plus design-of-experiments, with a closed-loop recommendation operators trust.
Digital Twins & Simulation
Build a digital twin of a line or facility to simulate layouts, throughput, bottlenecks and what-if scenarios before touching real equipment, connected to live data so it stays in sync with reality.
Robotics Simulation
Simulation with RViz, Gazebo, RoboDK and V-REP — debugging, sensor visualization, virtual testing, launching robots in simulation and industrial simulation workflows.
Industrial Implementation of Robotics
Real-world deployment — load management, EOAT design and gripper selection, layout optimization, sequence planning, safety measures and cycle-time calculation.
Artificial Intelligence in Robotics
Integrating AI and ML with robotics — cloud and IoT, AI with ROS and edge devices, local server processing, reinforcement learning, sensor fusion and AI-driven data management.
Production Planning & Supply-Chain Optimization
Use AI/ML for demand forecasting, S&OP and production scheduling — balancing capacity, material constraints and demand variability — and integrate forecasts with the ERP.
Claude, MCP & Agentic Copilots for the Shop Floor
Build a RAG copilot over plant manuals, SOPs and the historian — query 'why did line 3 stop last Tuesday?' in plain language, auto-summarize shift logs, and scope a safe agentic workflow over OT data.
Computer-Vision Safety & EHS
Use existing CCTV plus computer vision for real-time PPE compliance, restricted-zone entry, forklift-pedestrian proximity and unsafe-behavior detection — turning cameras into safety sensors without new hardware.
Questions
Good to know
Still wondering about something? Ask us directly in the enquiry form below.
Do I need a strong math/ML background?
Comfortable Python and basic stats for the engineering modules; the strategy/ops modules are more accessible. Starter notebooks and no-code paths provided.
Discrete or process manufacturing?
Both — modules cover defect inspection (discrete) and process/yield optimization (process).
Enquire
Talk to us — we'll help you choose.
Tell us where you're starting from and what you want to build. We'll walk you through the cohort, the lab, and whether this is the right first step — or point you somewhere better.
- A real reply from our team — never a bot wall.
- Honest guidance on fit, prerequisites and timing.
- Cohort dates and how the hands-on lab time works.
Keep exploring
Programs you might pair this with
Ready to startbuilding?
Enquire about this course, book a demo, or let our AI map your perfect track. The lab is open.