Go beyond technique to master the difficult judgement calls of forecasting

A variety of software can be used effectively to achieve accurate forecasting, but no software can replace the essential human component. You may be new to forecasting, or you may have mastered the statistical theory behind the software's predictions, and even more advanced "power user" techniques for the software itself--but your forecasts will never reach peak accuracy unless you master the complex judgement calls that the software cannot make. Profit From Your Forecasting Software addresses the issues that arise regularly, and shows you how to make the correct decisions to get the most out of your software.

Taking a non-mathematical approach to the various forecasting models, the discussion covers common everyday decisions such as model choice, forecast adjustment, product hierarchies, safety stock levels, model fit, testing, and much more. Clear explanations help you better understand seasonal indices, smoothing coefficients, mean absolute percentage error, and r-squared, and an exploration of psychological biases provides insight into the decision to override the software's forecast. With a focus on choice, interpretation, and judgement, this book goes beyond the technical manuals to help you truly grasp the more intangible skills that lead to better accuracy.

* Explore the advantages and disadvantages of alternative forecasting methods in different situations

* Master the interpretation and evaluation of your software's output

* Learn the subconscious biases that could affect your judgement toward intervention

* Find expert guidance on testing, planning, and configuration to help you get the most out of your software

Relevant to sales forecasters, demand planners, and analysts across industries, Profit From Your Forecasting Software is the much sought-after "missing piece" in forecasting reference.

PAUL GOODWIN, PHD, is Professor Emeritus at University of Bath, Bath, UK, where he teaches courses on Management Science, business forecasting, and decision analysis. He regularly conducts workshops at forecasting events around the world. A Fellow of the International Institute of Forecasters, he is a well-known keynote speaker at SAS.

Klappentext

SUPPLIES THE MISSING PIECE IN YOUR FORECASTING PROCESS

No matter how sophisticated the forecasting software you use, your forecasts will not achieve peak accuracy until you master the complex judgment calls that only a human can make. The first book of its kind, Profit from Your Forecasting Software shows you how to make the correct decisions to get the most out of your software.

Taking a non-mathematical approach, it explores common decisions such as choosing the most appropriate model, adjusting forecasts, product hierarchies, safety stock levels, model fit, testing, and more. It also provides plain-English explanations of crucial concepts such as seasonal indices, smoothing coefficients, mean absolute percentage error, and r-squared. And it helps you:

• Choose the best forecasting methods for a range of different purposes and situations
• Master the interpretation and evaluation of your software's output
• Gain insight into the psychological biases that can influence forecasters' judgment calls
• Know when and if to override your software's predictions
• Learn to test, plan, and configure your software for optimal accuracy

Whether you're a forecasting ace, a reluctant newcomer, or anything in-between, Profit from Your Forecasting Software is one professional resource you can't afford to be without.

Inhalt

Acknowledgments xv

Prologue xvii

Chapter 1 Profit from Accurate Forecasting 1

1.1 The Importance of Demand Forecasting 2

1.2 When Is a Forecast Not a Forecast? 2

1.3 Ways of Presenting Forecasts 3

1.3.1 Forecasts as Probability Distributions 3

1.3.2 Point Forecasts 4

1.3.3 Prediction Intervals 6

1.4 The Advantages of Using Dedicated Demand Forecasting Software 7

1.5 Getting Your Data Ready for Forecasting 8

1.6 Trading-Day Adjustments 10

1.7 Overview of the Rest of the Book 11

1.8 Summary of Key Terms 12

1.9 References 13

Chapter 2 How Your Software Finds Patterns in Past Demand Data 15

2.1 Introduction 16

2.2 Key Features of Sales Histories 16

2.2.1 An Underlying Trend 16

2.2.2 A Seasonal Pattern 17

2.2.3 Noise 22

2.3 Autocorrelation 23

2.4 Intermittent Demand 25

2.5 Outliers and Special Events 25

2.6 Correlation 27

2.7 Missing Values 30

2.8 Wrap-Up 31

2.9 Summary of Key Terms 31

Chapter 3 Understanding Your Software's Bias and Accuracy Measures 33

3.1 Introduction 34

3.2 Fitting and Forecasting 34

3.2.1 Fixed-Origin Evaluations 36

3.2.2 Rolling-Origin Evaluations 36

3.3 Forecast Errors and Bias Measures 38

3.3.1 The Mean Error (ME) 39

3.3.2 The Mean Percentage Error (MPE) 40

3.4 Direct Accuracy Measures 40

3.4.1 The Mean Absolute Error (MAE) 40

3.4.2 The Mean Squared Error (MSE) 41

3.5 Percentage Accuracy Measures 42

3.5.1 The Mean Absolute Percentage Error (MAPE) 42

3.5.2 The Median Absolute Percentage Error (MDAPE) 44

3.5.3 The Symmetric Mean Absolute Percentage Error (SMAPE) 44

3.5.4 The MAD/MEAN Ratio 45

3.5.5 Percentage Error Measures When There Is a Trend or Seasonal Pattern 46

3.6 Relative Accuracy Measures 46

3.6.1 Geometric Mean Relative Absolute Error (GMRAE) 47

3.6.2 The Mean Absolute Scaled Error (MASE) 48

3.6.3 Bayesian Information Criterion (BIC) 49

3.7 Comparing the Different Accuracy Measures 50

3.8 Exception Reporting 52

3.9 Forecast Value-Added Analysis (FVA) 52

3.10 Wrap-Up 55

3.11 Summary of Key Terms 56

3.12 References 57

Chapter 4 Curve Fitting and Exponential Smoothing 59

4.1 Introduction 60

4.2 Curve Fitting 60

4.2.1 Common Types of Curve 60

4.2.2 Assessing How Well the Curve Fits the Sales History 63

4.2.3 Strengths and Limitations of Forecasts Based on Curve Fitting 64

4.3 Exponential Smoothing Methods 65

4.3.1 Simple (or Single) Exponential Smoothing 65

4.3.2 Exponential Smoothing When There Is a Trend: Holt's Method 68

4.3.3 The Damped Holt's Method 70

4.3.4 Holt's Method with an Exponential Trend 72

4.3.5 Exponential Smoothing Where There Is a Trend and Seasonal Pattern: The Holt-Winters Method 73

4.3.6 Overview of Exponential Smoothing Methods 74

4.4 Forecasting Intermittent Demand 74

4.5 Wrap-Up 77

4.6 Summary of Key Terms 78

Chapter 5 Box-Jenkins ARIMA Models 81

5.1 Introduction 82

5.2 Stationarity 82

5.3 Models of Stationary Time Series: Autoregressive Models 85

5.4 Models of Stationary Time Series: Moving Average Models 87

5.5 Models of Stationary Time Series: Mixed Models 88

5.6 Fitting a Model to a Stationary Time Series 89

5.7 Diagnostic Checks 91

5.7.1 Check 1: Are the Coefficients of the Model Statistically Significant? 91

5.7.2 Check 2: OverfittingShould We Be Using a More Complex Model? 92

5.7.3 Check 3: Are the Residuals of the Model White Noise? 92

5.7.4 Check 4: Are the Residuals Normally Distributed? 93

5.8 M...