By the end of this course, participants will be able to:

• Understand the role of predictive analytics in modern inventory management
• Differentiate between descriptive, diagnostic, predictive, and prescriptive analytics
• Analyze demand patterns, variability, and uncertainty
• Link forecasts to inventory decisions and service level targets
• Apply predictive concepts to:
• Safety stock
• Reorder points
• Min–max policies
• Balance inventory cost, service level, and risk
• Identify inventory optimization opportunities across the supply chain
• Communicate analytics-driven inventory recommendations to stakeholders

This course is designed for:

• Inventory and Materials Managers
• Supply Chain and Operations Managers
• Demand Planners and Forecast Analysts
• Procurement and Logistics Professionals
• Warehouse and Distribution Managers
• Operations Excellence and Continuous Improvement Teams
• ERP / Planning System Users and Key Business Analysts
• Managers responsible for working capital and service performance

No advanced statistics or data science background is required.

The course uses a practical, applied, and decision-oriented approach, including:

• Instructor-led conceptual sessions
• Real-world supply chain and inventory case studies
• Hands-on exercises with simplified datasets
• Group discussions and scenario analysis
• Visual analytics and dashboard interpretation
• Inventory policy design workshops
• Daily reviews and application discussions

The emphasis is on business understanding and application, not software tools or coding.

Day 1 – Predictive Analytics Foundations & Demand Behavior

Topics:

• Evolution of analytics in supply chains:
• Descriptive → Predictive → Prescriptive
• Why traditional inventory planning fails
• Data used in predictive inventory analytics
• Demand patterns:
• Stable, seasonal, intermittent, lumpy
• Understanding demand variability and uncertainty
• Forecast accuracy vs forecast usefulness
• Common forecasting errors and bias
• Role of predictive analytics in demand sensing

Practical Exercise:

• Identifying demand patterns and variability from sample data

Day 2 – Linking Predictive Analytics to Inventory Decisions

Topics:

• From forecasts to inventory policies
• Key inventory trade-offs:
• Cost vs service level vs risk
• Service level concepts:
• Cycle service level
• Fill rate
• Predictive approach to safety stock
• Reorder point (ROP) logic under uncertainty
• Lead time variability and supply risk
• Single-echelon vs multi-echelon thinking
• Managing slow-moving and critical items

Workshop:

• Designing safety stock and reorder logic using predictive insights

Day 3 – Inventory Optimization, Scenarios & Decision Support

Topics:

• Inventory segmentation (ABC / XYZ and beyond)
• Differentiated inventory strategies by category
• Predictive analytics for:
• Excess and obsolete inventory
• Stock-out risk prediction
• Demand spikes and disruptions
• Scenario analysis and “what-if” simulations
• Integrating predictive analytics into ERP / planning systems
• Performance metrics:
• Inventory turns
• Service level
• Working capital
• Building a predictive inventory improvement roadmap
  
  

Final Group Exercise:

• Inventory optimization case:
• Analyze data
• Identify risks
• Propose optimized inventory policies
• Present recommendations

CDGA attendance certificate will be issued to all attendees completing minimum of 80% of the total course duration.