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.

• Completely updated to include the very latest concepts and methods in forecasting
• Includes real case studies and examples, actual data, and graphical displays and tables to illustrate how effective implementation works
• Ideal for CEOs, CFOs, CMOs, vice presidents of supply chain, vice presidents of demand forecasting and planning, directors of demand forecasting and planning, supply chain managers, demand planning managers, marketing analysts, forecasting analysts, financial managers, and any other professional who produces or contributes to 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.

Foreword

Preface

Acknowledgments

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

Hold-and-Roll Myth

The Plan that Was Not Good Enough

Package-to-Order versus Make-to-Order

“Do You Want Fries with That?”

Summary

Notes

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?

Summary

NOTES

Chapter 3 Overview of Forecasting Methods

Underlying Methodology

Different Categories of Methods

How Predictable Is the Future?

Some Causes of Forecast Error

Segmenting Your Products to Choose the Appropriate Forecasting Method

Summary

Note

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

Out-of-Sample Measurement

Forecast Value Added

Summary

Notes

Chapter 5 Quantitative Forecasting Methods Using Time Series Data

Understanding the Model-Fitting Process

Introduction to Quantitative Time Series Methods

Quantitative Time Series Methods

Moving Averaging

Exponential Smoothing

Single Exponential Smoothing

Holt's Two-Parameter Method

Holt's-Winters' Method

Winters' Additive Seasonality

Quantitative Forecasting Methods Using Time Series Data

Summary

Notes

Chapter 6 Regression Analysis

Regression Methods

Simple Regression

Correlation Coefficient

Coefficient of Determination

Multiple Regression

Data Visualization Using Scatter Plots and Line Graphs

Correlation Matrix

Multicollinearity

Variance of Analysis

F-test

Adjusted R²

Parameter Coefficients

t-test

P-values

Variance Inflation Factor

Durbin-Watson Statistic

Intervention Variables (or Dummy Variables)

Regression Model Results

Key Activities in Building a Multiple Regression Model

Cautions about Regression Models

Summary

Notes

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?

Box-Jenkins Overview

Extending ARIMA Models to Include Explanatory Variables

Transfer Functions

Numerators and Denominators

Rational Transfer Functions

ARIMA Model Results

Summary

Notes

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

Summary

Notes

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

Summary

Appendix 9A Consumer Packaged Goods Terminology

Appendix 9B Adstock Transformations for Advertising GRP/ TRPs

Notes

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

Model Step

Forecast Step

Summary

Notes

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

Summary

Suggested Reading

Notes

About the Author

Index