Demand Planning for S&OP

On-Demand Workshop: Demand Planning for S&OP :

An accurate demand plan helps you reduce inventory costs and increase customer service levels. When properly implemented, the demand plan helps create a lean and customer-centric supply chain.

In this workshop, you will learn how to develop a baseline statistical forecast and leverage a collaborative process to add customer intelligence. The result is a more accurate plan that includes promotional activity, customer intelligence, and display activity.

This two-day intensive workshop will take you through an overview of the demand planning process, and the organization and structure within a supply chain. Our course comprehensively covers statistical modeling to create accurate forecasts, in-depth discussion on model diagnostics to improve the quality of the forecast models with specific references to popular applications such as SAP IBP, Oracle, Demantra, ForecastPro, etc.

To read the detailed agenda, please download the brochure below.

Detailed Outline of The Workshop

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


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