Role-Based Programme
RB1583

AI-Powered Quality Control

Intelligent Inspection, Defect Analytics & Process Quality Automation

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Duration
32 Hours
Level
Advanced
Delivery
Instructor-Led
Format
Capability Development

Programme Objectives

  • Develop advanced capability to apply AI across quality inspection, defect management, process monitoring, testing, non-conformity control, and quality reporting.
  • Use AI to analyse inspection records, test results, defect data, specifications, process measurements, rejection information, and corrective-action records.
  • Apply AI-assisted analytics to identify quality trends, abnormal variation, recurring defects, process instability, and areas requiring technical investigation.
  • Build repeatable AI-enabled workflows for inspection, exception handling, defect classification, escalation, corrective action, and management reporting.
  • Design responsible AI-enabled Quality Control workflows with appropriate controls for measurement accuracy, evidence integrity, specification compliance, traceability, and human oversight.

Tools covered

Generative AI AssistantsDocument AISpreadsheet & Data AnalysisQuality Control AnalyticsStatistical Quality ToolsVisual AIDefect Classification ToolsRoot Cause Analysis ToolsReporting & Dashboard ToolsWorkflow Automation

Who should attend

  • Quality Control Managers
  • Quality Control Engineers
  • Quality Engineers
  • Quality Inspectors
  • Quality Analysts
  • Quality Assurance Professionals
  • Process Quality Professionals
  • Manufacturing Quality Professionals
  • Testing & Inspection Professionals
  • Product Quality Professionals
  • Operations Quality Professionals
  • Supplier Quality Professionals
  • Laboratory Quality Professionals
  • Process Engineers
  • Quality Control Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of Quality Control, Quality Assurance, inspection, testing, manufacturing, or process quality
  • Familiarity with specifications, inspection records, defects, testing data, process measurements, or non-conformities
  • Basic proficiency with spreadsheets, documents, numerical data, and workplace productivity applications
  • No previous AI course attendance required
  • No programming background required

TOC Modules

Concepts
  • Understanding Generative AI, analytical AI, Visual AI, and workflow automation
  • Mapping AI opportunities across inspection, testing, defect control, process monitoring, and reporting
  • Understanding AI assistance versus qualified Quality Control judgement
  • Recognising risks from inaccurate measurements, weak evidence, hallucinated conclusions, and unvalidated outputs
Practical activities
  • Mapping an existing Quality Control workflow and identifying AI opportunities
  • Comparing manual and AI-assisted QC activities
  • Creating a Quality Control AI opportunity matrix

Scenarios

Quality Inspection Failure to Corrective Action

Inspection / Test → AI-Assisted Analysis → Defect Classification → Non-Conformity → Root Cause Investigation → Corrective Action → Verification → Closure

Participants use AI-assisted techniques to analyse a simulated Quality Control failure, organise inspection evidence, classify defects, investigate root causes, and prepare an evidence-based corrective-action plan.

Quality Control Data to Continuous Monitoring Workflow

Inspection Results + Test Data + Defects + Rework + Process Variation + Corrective Actions → AI Analysis → Quality Risk Signals → Automated Alerts → Priority Actions → Dashboard → Management Review

Participants design an AI-enabled Quality Control workflow that consolidates quality signals, detects recurring defects and process abnormalities, automates follow-up, and improves management visibility while retaining final quality decisions with authorised professionals.

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