Role-Based Programme
RB1587

AI-Powered Process Quality Management

Intelligent Process Control, Quality Analytics & Continuous Improvement

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

Programme Objectives

  • Develop advanced capability to apply AI across process quality planning, monitoring, control, analysis, improvement, and governance.
  • Use AI to analyse process data, quality metrics, defects, deviations, inspection results, process documentation, and corrective-action records.
  • Apply AI-assisted techniques to identify process variation, bottlenecks, recurring failures, control weaknesses, and quality-improvement opportunities.
  • Build repeatable AI-enabled workflows for process monitoring, exception management, root-cause analysis, corrective action, and management reporting.
  • Design responsible AI-enabled Process Quality Management workflows with appropriate controls for data accuracy, statistical validity, process traceability, compliance, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchDocument AISpreadsheet & Data AnalysisProcess Quality AnalyticsStatistical Quality ToolsProcess Mapping AIRoot Cause Analysis ToolsReporting & Dashboard ToolsWorkflow Automation

Who should attend

  • Process Quality Managers
  • Quality Managers
  • Process Quality Engineers
  • Quality Engineers
  • Quality Assurance Professionals
  • Quality Control Professionals
  • Process Engineers
  • Continuous Improvement Professionals
  • Operational Excellence Professionals
  • Process Excellence Managers
  • Manufacturing Quality Professionals
  • Operations Quality Professionals
  • Quality Analysts
  • Process Owners
  • Process Quality Management Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of Quality Management, process control, Quality Assurance, operations, or process improvement
  • Familiarity with process maps, KPIs, defects, deviations, inspections, quality controls, or corrective actions
  • 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, process intelligence, and workflow automation
  • Mapping AI opportunities across process quality planning, control, monitoring, and improvement
  • Understanding AI assistance versus qualified quality and process-owner judgement
  • Recognising data-quality, hallucination, traceability, and process-risk concerns
Practical activities
  • Mapping an existing Process Quality lifecycle and identifying AI opportunities
  • Comparing manual and AI-assisted process-quality activities
  • Creating a Process Quality AI opportunity matrix

Scenarios

Process Variation to Quality Improvement

Process Data → AI Analysis → Variation Detection → Defect Patterns → Root Cause Analysis → Corrective Action → Process Control Update → Effectiveness Review

Participants use AI-assisted techniques to investigate a simulated process-quality problem, identify unusual variation, validate root causes, implement corrective actions, and strengthen the process control plan.

Process Quality Data to Continuous Monitoring Workflow

Process KPIs + Defects + Deviations + Audit Findings + CAPA Status → AI Analysis → Quality Risk Signals → Automated Alerts → Corrective Actions → Dashboard → Management Review

Participants design an AI-enabled Process Quality Management workflow that continuously consolidates process signals, highlights recurring quality risks, automates follow-up, and improves management visibility while retaining final quality decisions with authorised professionals.

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