Supply Chain Analytics & Demand Forecasting Training Course

Course Overview

Learning Objectives

• Analyze the role of analytics and demand forecasting in supply chain performance, cost control, and service continuity on Day 1.
• Apply descriptive, diagnostic, predictive, and prescriptive analytics to common supply chain decisions.
• Develop a focused set of supply chain KPIs, including service level, fill rate, on-time in-full delivery, inventory turnover, days of supply, and forecast accuracy.
• Implement practical data preparation techniques, including cleansing, handling outliers, and adjusting for stockouts and one-off events.
• Analyze demand patterns to identify level, trend, seasonality, and intermittent behavior in historical data.
• Apply forecasting methods, including moving averages, exponential smoothing, trend and seasonal models, and regression, to realistic datasets.
• Select appropriate forecasting approaches for different item types, including fast-moving, seasonal, and slow-moving or intermittent items.
• Evaluate forecast performance using accuracy and bias measures and identify actions to improve forecasting quality.
• Integrate statistical forecasts with business intelligence and expert judgment through a structured consensus process.
• Apply inventory analytics, including ABC and XYZ classification, safety stock, and reorder point calculations, to align inventory with demand variability and service targets.
• Design a supply chain analytics dashboard that supports planning and management decisions.
• Develop an improvement plan for supply chain analytics and demand forecasting in the participant's organization by the end of Day 5.

Who Should Attend

Learning Outcomes

• By the end of the course, participants will be able to:
• Explain how analytics and forecasting improve service levels, costs, and supply chain resilience.
• Select and interpret key supply chain KPIs for their operations.
• Prepare and clean historical demand data so that it is suitable for forecasting.
• Identify demand patterns and choose forecasting methods that match them.
• Build and test forecasting models using spreadsheet tools.
• Measure forecast accuracy and bias and use the results to improve forecasts.
• Handle slow-moving and intermittent demand items using suitable techniques.
• Combine statistical forecasts with expert input in a disciplined way.
• Classify inventory using ABC and XYZ analysis and set differentiated inventory policies.
• Calculate safety stock and reorder points based on demand variability, lead times, and service targets.
• Use scenario analysis to assess the impact of demand and supply changes.
• Present supply chain performance and forecasts through a clear dashboard for decision makers.

Course Outline