What is included with this book?
Many companies still look at quantitative forecasting methods with suspicion, but a new awareness is emerging across many industries as more businesses and professionals recognize the value of integrating demand data (point-of-sale and syndicated scanner data) into the forecasting process. Demand-Driven Forecasting equips you with solutions that can sense, shape, and predict future demand using highly sophisticated methods and tools. From a review of the most basic forecasting methods to the most advanced and innovative techniques in use today, this guide explains demand-driven forecasting, offering a fundamental understanding of the quantitative methods used to sense, shape, and predict future demand within a structured process. Offering a complete overview of the latest business forecasting concepts and applications, this revised Second Edition of Demand-Driven Forecasting is the perfect guide for professionals who need to improve the accuracy of their sales forecasts.
Accurate forecasting is vital to success in today's challenging business climate. Demand-Driven Forecasting offers proven and effective insight on making sure your forecasts are right on the money.
Chapter 1 Demystifying Forecasting: Myths versus Reality
Data Collection, Storage, and Processing Reality
“Art of Forecasting” Myth
End-Cap Display Dilemma
Reality of Judgmental Overrides
Oven Cleaner Connection
More Is Not Necessarily Better
Reality of Unconstrained Forecasts, Constrained Forecasts, and Plans
Northeast Regional Sales Composite Forecast
The Plan that Was Not Good Enough
Package-to-Order versus Make-to-Order
“Do You Want Fries with That?”
Chapter 2 What Is Demand-Driven Forecasting?
Delivering Demand Management from the Confines of Traditional Demand Forecasting
What’s wrong with this demand generation picture?
Fundamental Flaw with Traditional Demand Generation
Relying solely on a Supply-Driven strategy is not the solution
What is Demand-Driven Forecasting?
What is demand sensing and shaping?
Changing the Demand Management Process is Essential
Communication through collaborative workflow is the key to an effective demand management process
How do company’s measure demand management success?
The Benefits of a Demand-Driven Forecasting Process
What key steps can a company take to improve their demand management process?
Why haven’t companies embraced the concept of demand-driven?
Chapter 3 Overview of Forecasting Methods
Different Categories of Methods
How Predictable Is the Future?
Some Causes of Forecast Error
Segmenting Your Products to Choose the Appropriate Forecasting Method
Chapter 4 Measuring Forecast Performance
“We Overachieved Our Forecast, So Let's Party!”
Purposes for Measuring Forecasting Performance
Standard Statistical Error Terms
Specific Measures of Forecast Error
Forecast Value Added
Chapter 5 Quantitative Forecasting Methods Using Time Series Data
Understanding the Model-Fitting Process
Introduction to Quantitative Time Series Methods
Quantitative Time Series Methods
Single Exponential Smoothing
Holt's Two-Parameter Method
Winters' Additive Seasonality
Quantitative Forecasting Methods Using Time Series Data
Chapter 6 Regression Analysis
Coefficient of Determination
Data Visualization Using Scatter Plots and Line Graphs
Variance of Analysis
Variance Inflation Factor
Intervention Variables (or Dummy Variables)
Regression Model Results
Key Activities in Building a Multiple Regression Model
Cautions about Regression Models
Chapter 7 ARIMA Models
Phase 1. Identifying the Initial Model
Phase 2: Estimating and Diagnosing the Model Parameter Coefficients
Phase 3: Create a Forecast
Seasonal ARIMA Models
What are the tradeoffs among the various seasonal models?
Extending ARIMA Models to Include Explanatory Variables
Numerators and Denominators
Rational Transfer Functions
ARIMA Model Results
Chapter 8 Weighted Combined Forecasting Methods
What Is Weighted Combined Forecasting?
Developing a Variance Weighted Combined Forecast
Guidelines for the Use of Weighted Combined Forecasts
Chapter 9 Sensing, Shaping, and Linking Demand to Supply: A Case Study Using MTCA
Linking Demand to Supply Using Multi-Tiered Causal Analysis
Case Study: The Carbonated Soft Drink Story
Appendix 9A Consumer Packaged Goods Terminology
Appendix 9B Adstock Transformations for Advertising GRP/ TRPs
Chapter 10 Structured Judgment: A New Product Forecasting Approach that Combines Data, Analytics and Domain Knowledge
The Difference between Evolutionary and Revolutionary New Products
What is the general feeling about new product forecasting?
New Product Forecasting Overview
What is a Candidate Product?
New Product Forecasting Process
Structured Judgment Analysis
Structured Process Steps
Statistical Filter Step
Chapter 11 Strategic Value Assessment: Assessing the Readiness of Your Demand Forecasting Process
Strategic Value Assessment Framework
Strategic Value Assessment Process
SVA Case Study: XYZ Company
About the Author