Module 5.1: Forecasting Fundamentals

  • Time series components: trend, seasonality, cyclicality
  • Forecast accuracy metrics: MAPE, MAD, MSE, bias
  • Forecast horizons and update frequencies
  • Understanding forecast error
  • Collaborative forecasting (S&OP)

Module 5.2: Quantitative Forecasting Methods

  • Moving averages and weighted moving averages
  • Exponential smoothing (simple, double, triple)
  • Trend analysis and linear regression
  • Seasonal decomposition
  • Choosing the right forecasting method

Module 5.3: Advanced Forecasting Techniques

  • Multiple regression for demand prediction
  • ARIMA models introduction
  • Handling promotional effects and outliers
  • New product forecasting
  • Forecast value added (FVA) analysis

Module 5.4: Forecast Performance Management

  • Measuring forecast accuracy
  • Identifying bias in forecasts
  • Continuous improvement processes
  • Forecast error analysis
  • Adjusting safety stock based on forecast accuracy

Hands-On Exercise:

  • Build forecasts using 3 different methods
  • Compare forecast accuracy across methods
  • Create a forecast accuracy tracking dashboard
  • Adjust forecasts for seasonal products

Tools Covered: Excel (Data Analysis ToolPak), R/Python basics

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