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E-raamat: Profit From Your Forecasting Software: A Best Practice Guide for Sales Forecasters [Wiley Online]

(Bristol Business School)
  • Formaat: 240 pages
  • Sari: Wiley and SAS Business Series
  • Ilmumisaeg: 01-Jun-2018
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119415993
  • ISBN-13: 9781119415992
Teised raamatud teemal:
  • Wiley Online
  • Hind: 52,81 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Profit From Your Forecasting Software: A Best Practice Guide for Sales ForecastersSuurem pilt
  • Formaat: 240 pages
  • Sari: Wiley and SAS Business Series
  • Ilmumisaeg: 01-Jun-2018
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119415993
  • ISBN-13: 9781119415992
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119415992

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.

Acknowledgments xv
Prologue xvii
Chapter 1 Profit from Accurate Forecasting
1(14)
1.1 The Importance of Demand Forecasting
2(1)
1.2 When Is a Forecast Not a Forecast?
2(1)
1.3 Ways of Presenting Forecasts
3(4)
1.3.1 Forecasts as Probability Distributions
3(1)
1.3.2 Point Forecasts
4(2)
1.3.3 Prediction Intervals
6(1)
1.4 The Advantages of Using Dedicated Demand Forecasting Software
7(1)
1.5 Getting Your Data Ready for Forecasting
8(2)
1.6 Trading-Day Adjustments
10(1)
1.7 Overview of the Rest of the Book
11(1)
1.8 Summary of Key Terms
12(1)
1.9 References
13(2)
Chapter 2 How Your Software Finds Patterns in Past Demand Data
15(18)
2.1 Introduction
16(1)
2.2 Key Features of Sales Histories
16(7)
2.2.1 An Underlying Trend
16(1)
2.2.2 A Seasonal Pattern
17(5)
2.2.3 Noise
22(1)
2.3 Autocorrelation
23(2)
2.4 Intermittent Demand
25(1)
2.5 Outliers and Special Events
25(2)
2.6 Correlation
27(3)
2.7 Missing Values
30(1)
2.8 Wrap-Up
31(1)
2.9 Summary of Key Terms
31(2)
Chapter 3 Understanding Your Software's Bias and Accuracy Measures
33(26)
3.1 Introduction
34(1)
3.2 Fitting and Forecasting
34(4)
3.2.1 Fixed-Origin Evaluations
36(1)
3.2.2 Rolling-Origin Evaluations
36(2)
3.3 Forecast Errors and Bias Measures
38(2)
3.3.1 The Mean Error (ME)
39(1)
3.3.2 The Mean Percentage Error (MPE)
40(1)
3.4 Direct Accuracy Measures
40(2)
3.4.1 The Mean Absolute Error (MAE)
40(1)
3.4.2 The Mean Squared Error (MSE)
41(1)
3.5 Percentage Accuracy Measures
42(4)
3.5.1 The Mean Absolute Percentage Error (MAPE)
42(2)
3.5.2 The Median Absolute Percentage Error (MDAPE)
44(1)
3.5.3 The Symmetric Mean Absolute Percentage Error (SMAPE)
44(1)
3.5.4 The MAD/MEAN Ratio
45(1)
3.5.5 Percentage Error Measures When There Is a Trend or Seasonal Pattern
46(1)
3.6 Relative Accuracy Measures
46(4)
3.6.1 Geometric Mean Relative Absolute Error (GMRAE)
47(1)
3.6.2 The Mean Absolute Scaled Error (MASE)
48(1)
3.6.3 Bayesian Information Criterion (BIC)
49(1)
3.7 Comparing the Different Accuracy Measures
50(2)
3.8 Exception Reporting
52(1)
3.9 Forecast Value-Added Analysis (FVA)
52(3)
3.10 Wrap-Up
55(1)
3.11 Summary of Key Terms
56(1)
3.12 References
57(2)
Chapter 4 Curve Fitting and Exponential Smoothing
59(22)
4.1 Introduction
60(1)
4.2 Curve Fitting
60(5)
4.2.1 Common Types of Curve
60(3)
4.2.2 Assessing How Well the Curve Fits the Sales History
63(1)
4.2.3 Strengths and Limitations of Forecasts Based on Curve Fitting
64(1)
4.3 Exponential Smoothing Methods
65(9)
4.3.1 Simple (or Single) Exponential Smoothing
65(3)
4.3.2 Exponential Smoothing When There Is a Trend: Holt's Method
68(2)
4.3.3 The Damped Holt's Method
70(2)
4.3.4 Holt's Method with an Exponential Trend
72(1)
4.3.5 Exponential Smoothing Where There Is a Trend and Seasonal Pattern: The Holt-Winters Method
73(1)
4.3.6 Overview of Exponential Smoothing Methods
74(1)
4.4 Forecasting Intermittent Demand
74(3)
4.5 Wrap-Up
77(1)
4.6 Summary of Key Terms
78(3)
Chapter 5 Box-Jenkins ARIMA Models
81(28)
5.1 Introduction
82(1)
5.2 Stationarity
82(3)
5.3 Models of Stationary Time Series: Autoregressive Models
85(2)
5.4 Models of Stationary Time Series: Moving Average Models
87(1)
5.5 Models of Stationary Time Series: Mixed Models
88(1)
5.6 Fitting a Model to a Stationary Time Series
89(2)
5.7 Diagnostic Checks
91(3)
5.7.1 Check 1: Are the Coefficients of the Model Statistically Significant?
91(1)
5.7.2 Check 2: Overfitting---Should We Be Using a More Complex Model?
92(1)
5.7.3 Check 3: Are the Residuals of the Model White Noise?
92(1)
5.7.4 Check 4: Are the Residuals Normally Distributed?
93(1)
5.8 Models of Nonstationary Time Series: Differencing
94(2)
5.9 Should You Include a Constant in Your Model of a Nonstationary Time Series?
96(1)
5.10 What If a Series Is Nonstationary in the Variance?
97(1)
5.11 ARIMA Notation
97(1)
5.12 Seasonal ARIMA Models
98(3)
5.13 Example of Fitting a Seasonal ARIMA Model
101(3)
5.14 Wrap-Up
104(1)
5.15 Summary of Key Terms
105(4)
Chapter 6 Regression Models
109(28)
6.1 Introduction
110(1)
6.2 Bivariate Regression
110(5)
6.2.1 Should You Drop the Constant?
113(1)
6.2.2 Spurious Regression
114(1)
6.3 Multiple Regression
115(14)
6.3.1 Interpreting Computer Output for Multiple Regression
115(4)
6.3.2 Refitting the Model
119(1)
6.3.3 Multicollinearity
119(4)
6.3.4 Using Dummy Predictor Variables in Your Regression Model
123(4)
6.3.5 Outliers and Influential Observations
127(2)
6.4 Regression Versus Univariate Methods
129(2)
6.5 Dynamic Regression
131(1)
6.6 Wrap-Up
132(1)
6.7 Summary of Key Terms
132(2)
6.8 Appendix: Assumptions of Regression Analysis
134(2)
6.9 Reference
136(1)
Chapter 7 Inventory Control, Aggregation, and Hierarchies
137(26)
7.1 Introduction
138(1)
7.2 Identifying Reorder Levels and Safety Stocks
139(3)
7.3 Estimating the Probability Distribution of Demand
142(4)
7.3.1 Using Prediction Intervals to Determine Safety Stocks
144(2)
7.4 What If the Probability Distribution of Demand Is Not Normal?
146(5)
7.4.1 The Log-Normal Distribution
146(2)
7.4.2 Using the Poisson and Negative Binomial Distributions
148(3)
7.5 Temporal Aggregation
151(3)
7.6 Dealing with Product Hierarchies and Reconciling Forecasts
154(5)
7.6.1 Bottom-Up Forecasting
154(1)
7.6.2 Top-Down Forecasting
155(2)
7.6.3 Middle-Out Forecasting
157(1)
7.6.4 Hybrid Methods
157(1)
7.6.5 Issues and Future Developments
158(1)
7.7 Wrap-Up
159(1)
7.8 Summary of Key Terms
160(1)
7.9 References
161(2)
Chapter 8 Automation and Choice
163(14)
8.1 Introduction
164(1)
8.2 How Much Past Data Do You Need to Apply Different Forecasting Methods?
165(3)
8.3 Are More Complex Forecasting Methods Likely to Be More Accurate?
168(1)
8.4 When It's Best to Automate Forecasts
169(4)
8.5 The Downside of Automation
173(1)
8.6 Wrap-Up
174(1)
8.7 References
175(2)
Chapter 9 Judgmental Interventions: When Are They Appropriate?
177(18)
9.1 Introduction
178(1)
9.2 Psychological Biases That Might Catch You Out
179(4)
9.2.1 Seeing Patterns in Randomness
179(1)
9.2.2 Recency Bias
180(1)
9.2.3 Hindsight Bias
181(1)
9.2.4 Optimism Bias
181(2)
9.3 Restrict Your Interventions
183(2)
9.3.1 Large Adjustments Perform Better
183(1)
9.3.2 Focus Your Efforts Where They'll Count
184(1)
9.4 Making Effective Interventions
185(7)
9.4.1 Divide and Conquer
185(1)
9.4.2 Using Analogies
186(1)
9.4.3 Counteracting Optimism Bias
187(2)
9.4.4 Harnessing the Power of Groups of Managers
189(3)
9.4.5 Record Your Rationale
192(1)
9.5 Combining Judgment and Statistical Forecasts
192(2)
9.6 Wrap-Up
194(1)
9.7 Reference
194(1)
Chapter 10 New Product Forecasting
195(16)
10.1 Introduction
196(1)
10.2 Dangers of Using Unstructured Judgment in New Product Forecasting
197(1)
10.3 Forecasting by Analogy
198(5)
10.3.1 Structured Analogies
198(1)
10.3.2 Applying Structured Analogies
199(4)
10.4 The Bass Diffusion Model
203(4)
10.4.1 Innovators and Imitators
203(1)
10.4.2 Estimating a Bass Model
204(2)
10.4.3 Limitations of the Basic Bass Model
206(1)
10.5 Wrap-Up
207(1)
10.6 Summary of Key Terms
208(1)
10.7 References
209(2)
Chapter 11 Summary: A Best Practice Blueprint for Using Your Software
211(8)
11.1 Introduction
212(1)
11.2 Desirable Characteristics of Forecasting Software
212(5)
11.2.1 Data Preparation
212(1)
11.2.2 Graphical Displays
212(2)
11.2.3 Method Selection
214(1)
11.2.4 Implementing Methods
215(1)
11.2.5 Hierarchies
215(1)
11.2.6 Forecasting with Probabilities
215(1)
11.2.7 Support for Judgment
216(1)
11.2.8 Presentation of Forecasts
216(1)
11.3 A Blueprint for Best Practice
217(1)
11.4 References
218(1)
Index 219
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.
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