Role-Based Programme
RB1582

AI-Powered Quality Control

Smarter Inspection, Defect Analysis & Quality Performance Management

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Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Apply AI across Quality Control activities including inspection, testing, defect analysis, non-conformance handling, and quality reporting.
  • Use AI-assisted techniques to analyse inspection results, test data, defects, rejections, rework, and recurring quality issues.
  • Develop structured workflows for quality-control planning, defect classification, root-cause investigation, corrective action, and verification.
  • Analyse quality-control data to identify failure patterns, process variation, high-impact defects, and improvement opportunities.
  • Apply responsible AI practices covering measurement accuracy, specification control, evidence traceability, data integrity, and human oversight.

Tools covered

Generative AI AssistantsQuality Control AnalyticsDocument IntelligenceSpreadsheet AnalysisDefect ClassificationInspection Data AnalysisRoot-Cause Analysis SupportStatistical Quality AnalysisQuality ReportingWorkflow Automation

Who should attend

  • Quality Control Managers
  • Quality Control Engineers
  • Quality Control Executives
  • Quality Inspectors
  • Quality Engineers
  • Quality Assurance Professionals
  • Inspection Engineers
  • Testing Professionals
  • Process Quality Professionals
  • Production Quality Professionals
  • Quality Analysts
  • CAPA Coordinators
  • Continuous Improvement Professionals
  • Quality Management Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of Quality Control, inspection, testing, production, or process-quality activities
  • Familiarity with specifications, defects, rework, rejection, inspection records, or quality metrics is helpful
  • Basic spreadsheet and data-analysis skills
  • Basic awareness of Generative AI is helpful
  • No programming knowledge required

TOC Modules

Concepts
  • Understanding Generative AI, analytics, document intelligence, and automation in Quality Control
  • Identifying AI applications across inspection, testing, defect analysis, and reporting
  • Understanding AI assistance versus Quality Control professional judgement and accountability
  • Recognising risks related to incorrect data, specifications, measurements, and unsupported conclusions
Practical activities
  • Mapping the Quality Control lifecycle to AI-assisted activities
  • Identifying repetitive QC tasks suitable for AI support
  • Comparing traditional and AI-assisted Quality Control workflows

Scenarios

Defect Detection to Corrective Action

Inspection Data → AI-Assisted Defect Classification → Non-Conformance → Root-Cause Analysis → Corrective Action → Reinspection → Verification → Closure

Participants analyse a simulated Quality Control failure, classify the defect, validate likely causes, define corrective actions, and track the issue through controlled reinspection and closure.

Quality Control Data to Performance Improvement Plan

Inspection Results + Rejections + Rework + Defect Data → AI Analysis → Pareto Priorities → Process Weaknesses → Improvement Actions → Monitoring Plan → Management Report

Participants consolidate Quality Control information, identify recurring quality losses and priority defects, and prepare a management-ready improvement plan with actions, owners, and monitoring priorities.

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