Produktbild: Forecasting 2E (SAS)

Forecasting 2E (SAS)

Aus der Reihe SAS Institute Inc

88,99 €

inkl. gesetzl. MwSt., Versandkostenfrei


Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

09.08.2013

Verlag

John Wiley & Sons

Seitenzahl

384

Maße (L/B/H)

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

Gewicht

710 g

Farbe

Terracotta / Blau

Auflage

2nd edition

Sprache

Englisch

ISBN

978-1-118-66939-6

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

09.08.2013

Verlag

John Wiley & Sons

Seitenzahl

384

Maße (L/B/H)

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

Gewicht

710 g

Farbe

Terracotta / Blau

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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  • Produktbild: Forecasting 2E (SAS)
  • 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 2