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Business Forecasting
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Business Forecasting
by Udo Sglavo, Len Tashman, Michael Gilliland
Business Forecasting
Foreword
Preface
Chapter 1 Fundamental Considerations in Business Forecasting
1.1 Getting Real about Uncertainty
1.2 What Demand Planners Can Learn from the Stock Market
1.3 Toward a More Precise Definition of Forecastability
1.4 Forecastablity: A New Method for Benchmarking and Driving Improvement
1.5 Forecast Errors and Their Avoidability
1.6 The Perils of Benchmarking
1.7 Can We Obtain Valid Benchmarks from Published Surveys of Forecast Accuracy?
1.8 Defining “Demand” for Demand Forecasting
1.9 Using Forecasting to Steer the Business: Six Principles
1.10 The Beauty of Forecasting
Chapter 2 Methods of Statistical Forecasting
2.1 Confessions of a Pragmatic Forecaster
2.2 New Evidence on the Value of Combining Forecasts
2.3 How to Forecast Data Containing Outliers
2.4 Selecting Your Statistical Forecasting Level
2.5 When Is a Flat-line Forecast Appropriate?
2.6 Forecasting by Time Compression
2.7 Data Mining for Forecasting: An Introduction
2.8 Process and Methods for Data Mining for Forecasting
2.9 Worst-Case Scenarios in Forecasting: How Bad Can Things Get?
2.10 Good Patterns, Bad Patterns
Chapter 3 Forecasting Performance Evaluation and Reporting
3.1 Dos and Don’ts of Forecast Accuracy Measurement: A Tutorial
3.2 How to Track Forecast Accuracy to Guide Forecast Process Improvement
3.3 A “Softer” Approach to the Measurement of Forecast Accuracy
3.4 Measuring Forecast Accuracy
3.5 Should We Define Forecast Error as e = F – – A or e = A – – F?
3.6 Percentage Error: What Denominator?
3.7 Percentage Errors Can Ruin Your Day
3.8 Another Look at Forecast-Accuracy Metrics for Intermittent Demand
3.9 Advantages of the MAD/Mean Ratio over the MAPE
3.10 Use Scaled Errors Instead of Percentage Errors in Forecast Evaluations
3.11 An Expanded Prediction-Realization Diagram for Assessing Forecast Errors
3.12 Forecast Error Measures: Critical Review and Practical Recommendations
3.13 Measuring the Quality of Intermittent Demand Forecasts: It’s Worse than We’ve Thought!
3.14 Managing Forecasts by Exception
3.15 Using Process Behavior Charts to Improve Forecasting and Decision Making
3.16 Can Your Forecast Beat the Naïve Forecast?
Chapter 4 Process and Politics of Business Forecasting
4.1 FVA: A Reality Check on Forecasting Practices
4.2 Where Should the Forecasting Function Reside?
4.3 Setting Forecasting Performance Objectives
4.4 Using Relative Error Metrics to Improve Forecast Quality in the Supply Chain
4.5 Why Should I Trust Your Forecasts?
4.6 High on Complexity, Low on Evidence: Are Advanced Forecasting Methods Always as Good as They Seem?
4.7 Should the Forecasting Process Eliminate Face-to-Face Meetings?
4.8 The Impact of Sales Forecast Game Playing on Supply Chains
4.9 Role of the Sales Force in Forecasting
4.10 Good and Bad Judgment in Forecasting: Lessons from Four Companies
4.11 Worst Practices in New Product Forecasting
4.12 Sales and Operations Planning in the Retail Industry
4.13 Sales and Operations Planning: Where Is It Going?
About the Editors
Index
EULA
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Business Forecasting
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Foreword
CONTENTS
Foreword
Preface
Chapter 1 Fundamental Considerations in Business Forecasting
1.1 Getting Real about Uncertainty
1.2 What Demand Planners Can Learn from the Stock Market
1.3 Toward a More Precise Definition of Forecastability
1.4 Forecastablity: A New Method for Benchmarking and Driving Improvement
1.5 Forecast Errors and Their Avoidability
1.6 The Perils of Benchmarking
1.7 Can We Obtain Valid Benchmarks from Published Surveys of Forecast Accuracy?
1.8 Defining “Demand” for Demand Forecasting
1.9 Using Forecasting to Steer the Business: Six Principles
1.10 The Beauty of Forecasting
Chapter 2 Methods of Statistical Forecasting
2.1 Confessions of a Pragmatic Forecaster
2.2 New Evidence on the Value of Combining Forecasts
2.3 How to Forecast Data Containing Outliers
2.4 Selecting Your Statistical Forecasting Level
2.5 When Is a Flat-line Forecast Appropriate?
2.6 Forecasting by Time Compression
2.7 Data Mining for Forecasting: An Introduction
2.8 Process and Methods for Data Mining for Forecasting
2.9 Worst-Case Scenarios in Forecasting: How Bad Can Things Get?
2.10 Good Patterns, Bad Patterns
Chapter 3 Forecasting Performance Evaluation and Reporting
3.1 Dos and Don’ts of Forecast Accuracy Measurement: A Tutorial
3.2 How to Track Forecast Accuracy to Guide Forecast Process Improvement
3.3 A “Softer” Approach to the Measurement of Forecast Accuracy
3.4 Measuring Forecast Accuracy
3.5 Should We Define Forecast Error as
e
=
F
–
A
or
e
=
A
–
F
?
3.6 Percentage Error: What Denominator?
3.7 Percentage Errors Can Ruin Your Day
3.8 Another Look at Forecast-Accuracy Metrics for Intermittent Demand
3.9 Advantages of the MAD/Mean Ratio over the MAPE
3.10 Use Scaled Errors Instead of Percentage Errors in Forecast Evaluations
3.11 An Expanded Prediction-Realization Diagram for Assessing Forecast Errors
3.12 Forecast Error Measures: Critical Review and Practical Recommendations
3.13 Measuring the Quality of Intermittent Demand Forecasts: It’s Worse than We’ve Thought!
3.14 Managing Forecasts by Exception
3.15 Using Process Behavior Charts to Improve Forecasting and Decision Making
3.16 Can Your Forecast Beat the Naïve Forecast?
Chapter 4 Process and Politics of Business Forecasting
4.1 FVA: A Reality Check on Forecasting Practices
4.2 Where Should the Forecasting Function Reside?
4.3 Setting Forecasting Performance Objectives
4.4 Using Relative Error Metrics to Improve Forecast Quality in the Supply Chain
4.5 Why Should I Trust Your Forecasts?
4.6 High on Complexity, Low on Evidence: Are Advanced Forecasting Methods Always as Good as They Seem?
4.7 Should the Forecasting Process Eliminate Face-to-Face Meetings?
4.8 The Impact of Sales Forecast Game Playing on Supply Chains
4.9 Role of the Sales Force in Forecasting
4.10 Good and Bad Judgment in Forecasting: Lessons from Four Companies
4.11 Worst Practices in New Product Forecasting
4.12 Sales and Operations Planning in the Retail Industry
4.13 Sales and Operations Planning: Where Is It Going?
About the Editors
Index
EULA
List of Tables
Chapter 1
Table 1.1
Table 1.2
Table 1.3
Table 1.4
Table 1.5
Table 1.6
Table 1.7
Table 1.8
Table 1.9
Chapter 3
Table 3.1
Table 3.2
Table 3.3
Table 3.4
Table 3.5
Table 3.6
Table 3.7
Table 3.8
Table 3.9
Table 3.10
Table 3.11
Table 3.12
Table 3.13
Table 3.14
Table 3.15
Table 3.16
Table 3.17
Table 3.18
Table 3.19
Table 3.20
Table 3.21
Table 3.22
Chapter 4
Table 4.1
Table 4.2
Table 4.3
List of Illustrations
Chapter 1
Figure 1.1
A Fan Chart
Figure 1.2
A Density Forecast
Figure 1.3
Forecast Error Lower
Figure 1.4
Forecast Error (MAPE) vs. Coefficient of Variation (CoV)
Figure 1.5
Forecast Error (MAPE) vs. Yearly SOH Volume
Figure 1.6
Actual vs. Predicted Benchmark Forecasting Errors (log units); All SKUs, All Businesses, All Regions
Figure 1.7
Review of Single-Item History
Figure 1.8
The Unavoidability Ratio (Absolute Errors Relative to Those of a Naïve Forecast) for Unit A
Figure 1.9
The Unavoidability Ratio (Absolute Errors Relative to Those of a Naïve Forecast) for Unit B
Chapter 2
Figure 2.1
Monthly Sales for Cough Syrup Brand
Figure 2.2
Monthly Sales for Cough Syrup SKU
Figure 2.3
Monthly Demand
Figure 2.4
Annual Rainfall
Figure 2.5
Daily Sales Volume
Figure 2.6
Weekly Sales Volume
Figure 2.7
Forecast Model Generated by Forecasting Software
Figure 2.8
Model Fit Errors
Figure 2.9
Sales Volume after Compression of the Time Dimension
Figure 2.10
Forecast Model of the Compressed Volume
Figure 2.11
Sales and Forecast (after Transformation back to Weekly)
Figure 2.12
Model Fit Errors
Figure 2.13
Model Development Process
Figure 2.14
Table Prepared for Review
Figure 2.15
Research Database
Figure 2.16
New Car Sales, January 2000–August 2008 (thousands)
Figure 2.17
Forecasts and Outturn for September 2008
Figure 2.18
Forecasts and Outturn for October 2008
Figure 2.19
Lower 95% Prediction Bounds: Conventional vs. GARCH
Figure 2.20
GARCH Estimates of a Time-Varying Volatility (Standard Deviation) of New-Car Sales
Figure 2.21
Excess Returns for Five Takeover Targets
Figure 2.22
U.S. Nonfarm Employment and Forecast
Figure 2.23
Head and Shoulders Stock Price Pattern
Chapter 3
Figure 3.1
Key Steps in the Tracking Process
Figure 3.2
Flowchart for Storing Attributes of a Forecasting Process
Figure 3.3
Illustration of a Tracking Signal
Figure 3.4
Soft Systems Methodology Applied to Forecasting
Figure 3.5
A time series is often divided into training data (used to estimate the model) and test data (used to evaluate the forecasts).
Figure 3.6
Forecasts of Australian quarterly beer production using an ARIMA model applied to data up to the end of 2006. The thin line shows actual values (in the training and test data sets) while the thick line shows the forecasts.
Figure 3.7
In time series cross-validation, a series of training and test sets are used. Each training set (black) contains one more observation than the previous one, and consequently each test set (gray) has one fewer observations than the previous one.
Figure 3.8
Time-series cross-validation based on one-step forecasts. The black points are training sets, the gray points are test sets, and the light-gray points are ignored.
Figure 3.9
Survey Question
Figure 3.10
Expected Percentage Errors When Rolling a Standard Six-Sided Die, for Variations in APE
Figure 3.11
Three Years of Monthly Sales of a Lubricant Product Sold in Large Containers
Figure 3.12
Calculation of the Mean Absolute Deviation (MAD)
Figure 3.13
Calculation of the Mean Absolute Percentage Error (MAPE)
Figure 3.14
Calculation of the MAD/Mean
Figure 3.15
MAD/Mean as a Weighted Mean of APEs
Figure 3.16
For Constant True Demands, WMAPE = MAPE
Figure 3.17
Forecasts Minimizing the MAPE and MAD/Mean
Figure 3.18
Calculation of the Mean Absolute Scaled Error (MASE)
Figure 3.19
Error Measurement and Accuracy Statistics
Figure 3.20
GMASE as an Average of Individual Product MASEs
Figure 3.21
Segmentation of MASE by AFAR
Figure 3.22
Advanced Segmentation of MASE
Figure 3.23
The LFPI Barometer
Figure 3.24
The Expanded Prediction-Realization Diagram
Figure 3.25
Energy Price Forecasts, by Year 2000–2004
Figure 3.26
Figure 3.27
U.S. and Eastern Regions Payroll Job Forecasts, by Year 2000–2004
Figure 3.28
Box-plot for APEs (log scale)
Figure 3.29
Box-plot for Absolute Scaled Errors (log scale)
Figure 3.30
Box-plot for Absolute Scaled Errors Found by the MAD/MEAN Scheme (log scale)
Figure 3.31
A Tabular Management Report (SPLY = same period last year)
Figure 3.32
Time Series with Trend
Figure 3.33
Example of a PBC
Figure 3.34
Data for Calculation of Upper and Lower Process Limits
Figure 3.35
Type 1 Signal—Single Data Point Outside the Process Control Limits
Figure 3.36
Type 2 Signal—Three or Four out of Four Consecutive Points Closer to One of the Limits than to the Trend
Figure 3.37
Type 3 Signal—Eight or More Successive Points Falling on the Same Side of the Trend
Figure 3.38
Illustrative Historical Sales Analysis
Figure 3.39
New Marketing Plan Forecast
Figure 3.40
New Marketing Plan Forecast with Budget and Trend through Historical Sales
Figure 3.41
Tactical Classification for Decision Making
Figure 3.42
A Bad Month
Figure 3.43
A Bad Month 2
Chapter 4
Figure 4.1
Forecast Value Added “Stairstep” Report
Figure 4.2
Statistical Forecast Value Added
Figure 4.3
RAE vs. Volume
Figure 4.4
Distribution of RAE
Figure 4.5
Volume vs. Volatility (CoV) of Forecast Items (Color codes distinguish forecast quality (RAE))
Figure 4.6
Average RAE Volatility (CoV)
Figure 4.7
Guidelines for Problem-Solving Meetings
Figure 4.8
A Manufacturing Supply Chain Example
Figure 4.9
Sales Forecasting Games and Outcomes
Figure 4.10
Conditions Fostering Game Playing
Figure 4.11
Percentage of Company Forecasts That Are Adjusted
Figure 4.12
Effect of Adjustments by Size and Direction (% improvement measures the reduction in Median APE, so higher is better)
Figure 4.13
Ignoring the Uncertainty
Figure 4.14
S&OP Escalation Process
Figure 4.15
Meeting Cadence
Figure 4.16
The Adoption Curve
Figure 4.17
Complexity, Change, and Coordination
Figure 4.18
Complexity, Change, and Coordination
Figure 4.19
The Executive S&OP Process for Superior Widgets, Inc.
Figure 4.20
Energy Alignment
Guide
Cover
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Preface
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