Role-Based Programme
RB1302

AI for Production & Manufacturing Operations

Process Intelligence, Productivity & Operational Excellence

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

Programme Objectives

  • Develop practical AI capabilities for production planning, shop-floor operations, process monitoring, quality, maintenance coordination, and performance improvement.
  • Apply AI to analyse production data, downtime, bottlenecks, cycle times, defects, capacity, resource utilisation, and recurring operational issues.
  • Use AI to strengthen root-cause analysis, production reporting, SOP creation, shift handovers, issue tracking, and decision support.
  • Improve manufacturing productivity through structured process analysis, waste reduction, quality improvement, capacity optimisation, and continuous improvement.
  • Apply responsible AI practices related to operational data, safety, confidentiality, accuracy, technical validation, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchProduction Data AnalysisProcess AnalysisRoot-Cause AnalysisDowntime AnalysisCapacity & Throughput AnalysisQuality IntelligenceMaintenance SupportSOP & Knowledge ManagementOperational ReportingExecutive Summarisation

Who should attend

  • Production Managers
  • Manufacturing Managers
  • Plant Operations Managers
  • Production Engineers
  • Manufacturing Engineers
  • Production Supervisors
  • Shift Managers
  • Shop-Floor Supervisors
  • Operations Engineers
  • Process Engineers
  • Industrial Engineers
  • Production Planning Professionals
  • Operational Excellence Professionals
  • Continuous Improvement Professionals
  • Plant & Manufacturing Operations Leaders

Prerequisites & Participant Readiness

  • Experience in production, manufacturing, plant operations, process engineering, or operational excellence
  • Familiarity with production KPIs, workflows, quality, downtime, capacity, and shop-floor processes
  • Basic familiarity with Generative AI tools is recommended
  • Ability to interpret production and operational information
  • No programming knowledge required

TOC Modules

Concepts
  • Understanding Generative AI, reasoning models, multimodal AI, and AI assistants
  • Exploring AI applications across production, quality, maintenance, reporting, and process improvement
  • Understanding how AI differs from ERP, MES, SCADA, BI, and automation systems
  • Recognising hallucinations, unsafe recommendations, and limitations in manufacturing decisions
Practical activities
  • Mapping production and manufacturing activities to practical AI use cases
  • Identifying high-value versus high-risk AI applications
  • Comparing traditional and AI-assisted manufacturing workflows

Scenarios

Production Loss to Throughput Improvement Plan

Production Data → Bottleneck Identification → Downtime Analysis → Root-Cause Hypotheses → Capacity Review → Corrective Actions → KPI Tracking → Improvement Plan

Participants use AI to analyse a simulated production line with declining output, identify major losses and constraints, and develop a structured plan to improve throughput and productivity.

Rising Defects & Downtime to Manufacturing Recovery Strategy

Quality Data → Defect Trends → Breakdown History → Process Analysis → Root Causes → Maintenance & Quality Actions → SOP Updates → Recovery Roadmap

Participants use AI to investigate a simulated increase in defects and equipment downtime, identify likely operational causes, coordinate quality and maintenance actions, and develop a structured manufacturing recovery roadmap.

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