Role-Based Programme
RB1059

AI for Operations & Plant Heads

Advanced Operational Intelligence, Plant Performance & AI-Driven Transformation

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

Programme Objectives

  • Develop advanced capability to apply AI across plant operations, production planning, performance management, maintenance, quality, safety, and operational leadership.
  • Use AI-assisted analysis to identify performance gaps, recurring issues, bottlenecks, downtime patterns, resource constraints, and improvement opportunities.
  • Apply AI to support operational planning, problem-solving, process optimisation, management reporting, and cross-functional decision-making.
  • Identify and prioritise AI use cases based on operational value, feasibility, data readiness, safety, risk, governance, and measurable outcomes.
  • Build an actionable AI-enabled plant and operations transformation roadmap aligned with business, productivity, quality, safety, and reliability priorities.

Tools covered

Generative AI AssistantsAI Search & ResearchDocument IntelligenceOperational Data AnalysisProduction & Performance AnalysisPredictive Analytics ConceptsRoot Cause AnalysisAI-Powered ReportingWorkflow AutomationKnowledge AssistantsAI AgentsDecision-Support Tools

Who should attend

  • Plant Heads
  • Operations Heads
  • Manufacturing Heads
  • Factory Heads
  • Production Heads
  • Site Heads
  • Plant Managers
  • Operations Directors
  • Manufacturing Directors
  • Production Managers
  • Maintenance Heads
  • Engineering Heads
  • Operational Excellence Leaders
  • Continuous Improvement Heads
  • Senior Leaders Responsible for Plant and Operational Performance

Prerequisites & Participant Readiness

  • Significant experience in plant, manufacturing, production, engineering, or operations leadership
  • Familiarity with operational KPIs, production planning, maintenance, quality, safety, and performance reviews
  • Basic awareness of Generative AI and common business applications
  • Prior exposure to digital manufacturing, analytics, or automation tools is helpful
  • No programming knowledge required

TOC Modules

Concepts
  • Understanding Generative AI, reasoning models, multimodal AI, automation, and AI agents
  • Exploring AI applications across production, maintenance, quality, safety, planning, and operations
  • Distinguishing Generative AI, analytics, machine learning, automation, and autonomous systems
  • Understanding limitations, hallucinations, data dependency, and human accountability
Practical activities
  • Mapping AI opportunities across plant and operational activities
  • Identifying activities suitable for AI augmentation, automation, or human ownership
  • Creating a plant-level AI opportunity map

Scenarios

Plant Performance Loss to Operational Improvement Plan

Performance Gap → Production Data → Downtime & Quality Analysis → Root Causes → Improvement Options → Operational Impact → Risk & Safety Review → Management Action Plan

Participants use AI to investigate a plant-performance problem involving production losses, downtime, or quality variation, identify possible causes, evaluate corrective options, and create a structured improvement recommendation.

AI-Enabled Plant Transformation Roadmap

Plant Priorities → Operational Challenges → AI Use Cases → Value & Feasibility → Business Case → Safety & Governance → Workforce Impact → Prioritisation → 12–24 Month Roadmap

Participants act as Operations or Plant Heads to develop an AI transformation roadmap covering productivity, reliability, quality, safety, workforce capability, operational governance, investment, and measurable plant performance.

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