Role-Based Programme
RB1314

AI for Inventory Management

Stock Intelligence, Demand Alignment & Inventory Optimisation

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

Programme Objectives

  • Develop practical AI capabilities for stock monitoring, inventory planning, replenishment, inventory control, and operational decision support.
  • Apply AI to analyse stock levels, consumption patterns, demand variability, ageing, shortages, excess inventory, and replenishment requirements.
  • Use AI to improve stock classification, discrepancy analysis, inventory reporting, root-cause investigation, and action planning.
  • Strengthen inventory performance through improved availability, working-capital awareness, stock-risk identification, and continuous improvement.
  • Apply responsible AI practices related to inventory data, commercial sensitivity, forecasting assumptions, accuracy, and human validation.

Tools covered

Generative AI AssistantsAI Search & ResearchInventory Data AnalysisDemand AnalysisStock ClassificationReplenishment AnalysisInventory Risk AnalysisRoot-Cause AnalysisAgeing & Obsolescence AnalysisWarehouse Inventory IntelligenceOperational ReportingExecutive Summarisation

Who should attend

  • Inventory Managers
  • Inventory Executives
  • Inventory Analysts
  • Inventory Controllers
  • Stock Control Professionals
  • Materials Management Professionals
  • Supply Chain Professionals
  • Warehouse Operations Professionals
  • Demand Planning Professionals
  • Supply Planning Professionals
  • Operations Managers
  • Store & Warehouse Supervisors
  • Materials Planners
  • Operational Excellence Professionals
  • Inventory Operations Leaders

Prerequisites & Participant Readiness

  • Experience in inventory management, materials management, warehousing, supply chain, or operations
  • Familiarity with stock levels, replenishment, inventory movements, ageing, and operational reporting
  • Basic familiarity with Generative AI tools is recommended
  • Ability to interpret inventory and demand information
  • No programming knowledge required

TOC Modules

Concepts
  • Understanding Generative AI, reasoning models, multimodal AI, and AI assistants
  • Exploring AI applications across inventory planning, stock analysis, replenishment, and reporting
  • Understanding how AI differs from ERP, WMS, inventory-control, and planning systems
  • Recognising hallucinations, unsupported assumptions, and limitations in inventory decisions
Practical activities
  • Mapping the inventory-management lifecycle to practical AI applications
  • Identifying high-value versus high-risk inventory use cases
  • Comparing traditional and AI-assisted inventory workflows

Scenarios

Stockout Risk to Replenishment Recovery Plan

Demand Pattern → Stock Position → Lead-Time Review → Reorder Analysis → Shortage Risk → Priority Actions → Replenishment Plan → Service Protection

Participants use AI to analyse a simulated inventory portfolio with emerging stockout risks, identify priority items, assess demand and lead-time factors, and develop a structured replenishment recovery plan.

Excess & Ageing Inventory to Working-Capital Improvement Strategy

Inventory Ageing → Movement Analysis → Demand Review → Root-Cause Analysis → Stock Segmentation → Disposition Options → Inventory Actions → Working-Capital Improvement

Participants use AI to analyse excess and ageing inventory, identify likely causes, prioritise high-value problem areas, and develop a structured inventory optimisation strategy.

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