Demand Planning and Sales Forecasting 2-Day Workshop, Feb 16-17 2017, Chennai India

Day 1

8:30AM to 5:30PM

Day 2

9:00AM to 5:00PM

• 8:30 AM Registration & Breakfast
• 9:00 AM Welcome
• 9:30 AM Demand Planning Overview
  • Why Forecast and Plan?
  • The Service – Cost – Balance Model
  • Introduction to Demand Planning- What, Why and How?
  • Distinction between Business Forecasting and Demand Planning
  • Beyond Statistical Forecasting
  • Key Components of a Demand Plan
  • Key Terminology – Forecast Horizon, Buckets & Periodicity
  • Forecast Pass
  • Constrained vs. Unconstrained forecasts
  • Demand Management
• 10:30 AM Coffee Break
• 10:45 AM Data Cleaning and Adjustments for Demand Forecasting
  • What do we know about data?
  • The Forecast Problem and Data collection
  • Definition of Demand
     - Shipment Vs. Orders
     - Gross Demand Vs. Net Demand
  • Data cleaning challenges
  • Adjusting for historical shifts in demand
  • Data filtering
• Outliers
• Process to Identify Outliers
• What is a tolerance band
• Methodology for outlier correction
• 1:00 PM Lunch Break
• 2:00 PM Statistical Modeling
  • Forecast Model
  • Key components of demand
  • Additive Vs. Multiplicative Seasonality in Models
  • Modeling by decomposition
  • Introduction to Forecast Modeling
  • Qualities of a good statistical forecast
  • Balancing between Model Fit Vs. Model Robustness
  • Uni-Variate Time Series vs. Multi-Variate methods
  • Moving Average
  • First Order Exponential Smoothing or Constant Models
  • Holt Models to accommodate trend
  • Holt Winters Model
  • Exponential Trend and Dampening
• 3:30 PM Coffee Break & Networking Exercise
• 4:30 PM Product Life Cycle & Long-term Planning
  • Cyclicality
  • Product Lifecycle and trend
  • Launch Forecasting
  • Volume effect on line extension
• 5:00 PM Assignment of Forecasting exercises to participants

• 9:00 AM Data Analysis and SKU Segmentation
  • Simple Model of Demand
  • Demand Volatility
  • What is the approach to Data Analysis?
  • Impact of Data Volatility on Forecasting
  • Measuring Volatility
  • Impact of multiple Extreme Observations on Volatility
  • SKU Segmentation for forecast modeling
  • Volume Volatility a 2x2 grid
  • Modeling by exception
• 10:45 AM Forecast Errors and Model Diagnostic
• Definition of Demand Forecast Errors
• Forecast Accuracy
• Forecast Bias vs. Forecast Error
• Error and Volatility Reduction
• Errors across SKUs vs. Errors across time
• Model Diagnostics - Measuring Errors over time
   - MAD
   - MAPE vs. MPE
   - WAPE
   - Root Mean Squared Error
   - General Illustration MPE, MAPE and RMSE
• 11:30 AM Morning Break
• 11:45 AM Event Modeling
• Event Modeling
• Baseline vs. Incremental
• Event Models Identify Volume Spikes
• Illustration of Event Models
• 1:00 PM Lunch Break
• 2:00 PM Discussion and answers assigned exercises
• 3:30 PM Planning for Intermittent Demand
• What is Intermittent Demand?
• Illustration of Intermittent Demand Data
• What causes intermittency?
• Strategies to handle intermittent demand
• Business Perspective
• Stat Models
• Statistical Models for Intermittent Demand
  • Application of Croston's Model
  • Discrete Distribution
• 4:30 PM Measuring forecast performance
  • Forecast Performance Metric
  • Forecast errors and actionablity
  • Sources of Forecast Error
  • Definition of Demand Planning Metrics - WAPE & Bias
  • Types of Bias
  • SKU Mix Error
  • Error Analysis for Continuous Improvement
  • Forecast Accuracy Reporting