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Demand-Driven Forecasting: A Structured Approach to Forecasting, 2nd Edition

ISBN: 978-1-118-66939-6
384 pages
August 2013
Demand-Driven Forecasting: A Structured Approach to Forecasting, 2nd Edition (1118669398) cover image


An updated new edition of the comprehensive guide to better business forecasting

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.

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Table of Contents

Foreword xi

Preface xv

Acknowledgments xix

About the Author xx

Chapter 1 Demystifying Forecasting: Myths versus Reality 1

Data Collection, Storage, and Processing Reality 5

Art-of-Forecasting Myth 8

End-Cap Display Dilemma 10

Reality of Judgmental Overrides 11

Oven Cleaner Connection 13

More Is Not Necessarily Better 16

Reality of Unconstrained Forecasts, Constrained Forecasts, and Plans 17

Northeast Regional Sales Composite Forecast 21

Hold-and-Roll Myth 22

The Plan that Was Not Good Enough 23

Package to Order versus Make to Order 25

“Do You Want Fries with That?” 26

Summary 28

Notes 28

Chapter 2 What Is Demand-Driven Forecasting? 31

Transitioning from Traditional Demand Forecasting 33

What’s Wrong with The Demand-Generation Picture? 34

Fundamental Flaw with Traditional Demand Generation 37

Relying Solely on a Supply-Driven Strategy Is Not the Solution 39

What Is Demand-Driven Forecasting? 40

What Is Demand Sensing and Shaping? 41

Changing the Demand Management Process Is Essential 57

Communication Is Key 65

Measuring Demand Management Success 67

Benefits of a Demand-Driven Forecasting Process 68

Key Steps to Improve the Demand

Management Process 70

Why Haven’t Companies Embraced the Concept of Demand-Driven? 71

Summary 74

Notes 75

Chapter 3 Overview of Forecasting Methods 77

Underlying Methodology 79

Different Categories of Methods 83

How Predictable Is the Future? 88

Some Causes of Forecast Error 91

Segmenting Your Products to Choose the Appropriate Forecasting Method 94

Summary 101

Note 101

Chapter 4 Measuring Forecast Performance 103

“We Overachieved Our Forecast, So Let’s Party!” 105

Purposes for Measuring Forecasting Performance 106

Standard Statistical Error Terms 107

Specific Measures of Forecast Error 111

Out-of-Sample Measurement 115

Forecast Value Added 118

Summary 122

Notes 123

Chapter 5 Quantitative Forecasting Methods Using Time Series Data 125

Understanding the Model-Fitting Process 127

Introduction to Quantitative Time Series Methods 130

Quantitative Time Series Methods 135

Moving Averaging 136

Exponential Smoothing 142

Single Exponential Smoothing 143

Holt’s Two-Parameter Method 147

Holt’s-Winters’ Method 149

Winters’ Additive Seasonality 151

Summary 156

Notes 158

Chapter 6 Regression Analysis 159

Regression Methods 160

Simple Regression 160

Correlation Coefficient 163

Coefficient of Determination 165

Multiple Regression 166

Data Visualization Using Scatter Plots and Line Graphs 170

Correlation Matrix 173

Multicollinearity 175

Analysis of Variance 178

F-test 178

Adjusted R2 180

Parameter Coefficients 181

t-test 184

P-values 185

Variance Inflation Factor 186

Durbin-Watson Statistic 187

Intervention Variables (or Dummy Variables) 191

Regression Model Results 197

Key Activities in Building a Multiple Regression Model 199

Cautions about Regression Models 201

Summary 201

Notes 202

Chapter 7 ARIMA Models 203

Phase 1: Identifying the Tentative Model 204

Phase 2: Estimating and Diagnosing the Model Parameter Coefficients 213

Phase 3: Creating a Forecast 216

Seasonal ARIMA Models 216

Box-Jenkins Overview 225

Extending ARIMA Models to Include Explanatory Variables 226

Transfer Functions 229

Numerators and Denominators 229

Rational Transfer Functions 230

ARIMA Model Results 234

Summary 235

Notes 237

Chapter 8 Weighted Combined Forecasting Methods 239

What Is Weighted Combined Forecasting? 242

Developing a Variance Weighted Combined Forecast 245

Guidelines for the Use of Weighted Combined Forecasts 248

Summary 250

Notes 251

Chapter 9 Sensing, Shaping, and Linking Demand to Supply: A Case Study Using MTCA 253

Linking Demand to Supply Using Multi-Tiered Causal Analysis 256

Case Study: The Carbonated Soft Drink Story 259

Summary 276

Appendix 9A Consumer Packaged Goods Terminology 277

Appendix 9B Adstock Transformations for Advertising GRP/TRPs 279

Notes 282

Chapter 10 New Product Forecasting: Using Structured Judgment 283

Differences between Evolutionary and Revolutionary New Products 284

General Feeling about New Product Forecasting 286

New Product Forecasting Overview 288

What Is a Candidate Product? 292

New Product Forecasting Process 293

Structured Judgment Analysis 294

Structured Process Steps 296

Statistical Filter Step 303

Model Step 305

Forecast Step 308

Summary 313

Notes 316

Chapter 11 Strategic Value Assessment: Assessing the Readiness of Your Demand Forecasting Process 317

Strategic Value Assessment Framework 319

Strategic Value Assessment Process 321

SVA Case Study: XYZ Company 323

Summary 351

Suggested Reading 352

Notes 352

Index 355

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Author Information

CHARLES W. CHASE Jr. is the Chief Industry Consultant in SAS's Manufacturing & Supply Chain Global Practice, where he is the principal architect and strategist for delivering demand planning and forecasting solutions to improve SAS customers' supply chain efficiencies. He has more than twenty-six years of experience in the consumer packaged goods industry, and is an expert in sales forecasting, market response modeling, econometrics, and supply chain management.

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