Demand Planning Overview

• Planning objectives
• The Service – Cost – Balance Model
• Define your Plan
• Budgeting vs. Forecasting vs. Planning
• Beyond Statistical Forecasting
• Key Components of a Demand Plan
• Terminology in Planning – Forecast Horizon, Buckets & Periodicity
• Forecast Pass
• Demand Management

Data Integration and Cleansing

• It is all about the data
• The Forecast Problem and Data collection
• Define True Demand
• Data challenges
   - Shipment Vs. Orders
   - Gross Demand Vs. Net Demand
• Historical shifts in demand
• Data filtering
• Outliers– Identification and Correction
• The process to Identify Outliers
• Tolerance band
• Methodology for outlier correction

Stat Modeling and AI-driven Planning

• Demand Modeling
• 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
• AI-driven Planning and Expert Models
• Introduction to Planvida

Advanced smoothing models

• First Order Exponential Smoothing
• Holt Models to accommodate the trend
• Holt-Winters Model
• Exponential Trend and Dampening
• Interaction between components

Modeling special cases of Demand Product Life Cycle & Long-term Planning 

• Product Lifecycle and trend • Launch Forecasting • Volume effect on line extension
• Event Modeling
  Baseline vs. Incremental • Illustration of Event Models
• Planning for Intermittent Demand 
  What is Intermittent Demand & what causes it? Strategies to handle intermittent demand & Statistical Models for Intermittent Demand
• Higher-order Models

Product Portfolio Management

• Impact of Data Volatility on Forecasting
• Measuring Volatility
• Impact of multiple Extreme Observations on Volatility
• SKU Segmentation for demand modeling & inventory strategies
• Modeling by exception
• ABC analysis - Classification philosophy
• Pareto analysis  based on dollar usage
• Item criticality
• Excess, obsolete and Slow-moving Alignment with the product lifecycle
• Discontinuance and end of life (EOL)
• Process flow for Segmenting SKUs
• Example using a three-dimensional matrix; ABC / Volume / Critical / Status; the excess, obsolete impact of Segmentation on Cycle Counting and Inventory Accuracy.

Demand Planning Analytics toolkit

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 vs Performance
   - MAD
   - MAPE vs. MPE
   - WAPE
   - Root Mean Squared Error

Measuring forecast performance   

• Forecast Performance Metric
• Forecast errors and actionability
• Sources of Forecast Error
• Definition of Demand Planning Metrics - WAPE & Bias
• Types of Bias
• SKU Mix Error
• Error Analysis for Continuous Improvement

Why S&OP

• Fragmented Planning Activities
• – Supply chain challenges – Service, costs and inventories
• Disparities between the Financial forecast and operational forecasts
• Bottom Line challenges from Fragmented Planning
• Benefits of a holistic S&OP Design
• Core Components of SIOP
• Consensus Demand Planning
• Rough Cut Planning and Supply Collaboration
• Executive Presentation
• Demand-Supply Balancing
• Modeling by exception