Role-Based Programme
RB1301

AI for Production & Manufacturing Operations

Smarter Planning, Quality, Productivity & Operational Excellence

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Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Understand how AI can support Production and Manufacturing Operations across planning, quality, productivity, maintenance, and reporting.
  • Apply AI to analyse production data, downtime, defects, bottlenecks, material-flow issues, and recurring operational problems.
  • Use AI to improve shift summaries, SOPs, production planning, quality documentation, issue analysis, and management reporting.
  • Develop practical skills for root-cause exploration, capacity analysis, process improvement, and evidence-based operational decision support.
  • Understand responsible AI use, safety, confidentiality, data quality, governance, and human oversight in manufacturing environments.

Tools covered

Generative AI AssistantsProduction Data AnalysisShift & Capacity Planning SupportQuality Issue AnalysisRoot-Cause Analysis SupportMaintenance Insight SupportSOP DevelopmentInventory & Material Flow AnalysisPerformance ReportingContinuous Improvement Planning

Who should attend

  • Production Managers
  • Manufacturing Managers
  • Plant Operations Managers
  • Production Supervisors
  • Manufacturing Supervisors
  • Production Engineers
  • Manufacturing Engineers
  • Operations Engineers
  • Shop-Floor Managers
  • Shift Managers
  • Process Improvement Professionals
  • Operational Excellence Professionals
  • Production Planning Professionals
  • Operations Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of production, manufacturing, or plant operations
  • Familiarity with production targets, quality, downtime, shift operations, or manufacturing KPIs is helpful
  • Basic analytical, spreadsheet, and digital-tool skills
  • No programming or technical AI knowledge required
  • Prior AI-tool experience is helpful but not mandatory

TOC Modules

Concepts
  • Understanding Generative AI and its relevance to manufacturing and production operations
  • Exploring AI support across planning, quality, maintenance, documentation, and performance analysis
  • Distinguishing Generative AI from MES, ERP, automation, predictive-maintenance, and industrial analytics systems
  • Understanding AI limitations, hallucinations, data-quality risks, and operational safety implications
Practical activities
  • Identifying recurring manufacturing activities suitable for AI assistance
  • Comparing traditional and AI-assisted production workflows
  • Mapping AI opportunities across the production lifecycle

Scenarios

Production Shortfall to Recovery Plan

Production Data → Target Gap → Bottleneck Analysis → Downtime / Quality Review → Root-Cause Hypotheses → Recovery Actions → Ownership → Shift Plan

Participants use AI to analyse a sample production shortfall, identify possible operational constraints, and develop a structured recovery plan while keeping final decisions with responsible plant personnel.

Recurring Defects to Continuous Improvement Plan

Quality Data → Defect Categories → Pareto Analysis → Process Review → Root-Cause Hypotheses → Corrective Actions → Preventive Actions → Management Summary

Participants use AI to analyse sample defect and process data, identify recurring quality patterns, and prepare a structured corrective and preventive improvement plan for manufacturing review.

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