Produktbild: Demand-Driven Forecasting

Demand-Driven Forecasting A Structured Approach to Forecasting

Aus der Reihe SAS Institute Inc

88,99 €

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

19.08.2013

Verlag

John Wiley & Sons Inc

Seitenzahl

384

Maße (L/B/H)

23,5/15,7/2,5 cm

Gewicht

710 g

Auflage

2nd edition

Sprache

Englisch

ISBN

978-1-118-66939-6

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

19.08.2013

Verlag

John Wiley & Sons Inc

Seitenzahl

384

Maße (L/B/H)

23,5/15,7/2,5 cm

Gewicht

710 g

Auflage

2nd edition

Sprache

Englisch

ISBN

978-1-118-66939-6

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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Die Leseprobe wird geladen.
  • Produktbild: Demand-Driven Forecasting
  • 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