Forecasting sales:
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Greenwich, Conn. u.a.
Jai Press
1994
|
| Series: | Advances in business and management forecasting
1 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XIX, 298 S. graph. Darst. |
| ISBN: | 1559386029 |
Staff View
MARC
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| 245 | 1 | 0 | |a Forecasting sales |c by Michael Geurts, Kenneth D. Lawrence and John Guerard |
| 264 | 1 | |a Greenwich, Conn. u.a. |b Jai Press |c 1994 | |
| 300 | |a XIX, 298 S. |b graph. Darst. | ||
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| 490 | 1 | |a Advances in business and management forecasting |v 1 | |
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Record in the Search Index
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CONTENTS
List of Figures xv
List of Tables xvii
Preface xix
1. Sales Forecasting as a Management Tool 1
Introduction 1
Forecasting as a Part of Scientific Efforts 2
The Taxonomy of Sales Forecasting 3
Forecasting: A Management Responsibility 5
Management Uses of Forecasts 5
The Hazards of Forecasting 6
The Cost and Benefits of Forecasting 6
Cost of Forecasting 9
Need for a Forecaster 11
Techniques and Uses of Forecasting for Planning 12
Uses of Forecasts 12
Inventory Control 12
The Inventory Control System 13
The Raw Materials Inventory Control Systems 13
Finished Goods Control 15
Determining the Production Plan 15
Determining the Production Schedule 15
Budgets and Cash Flow 16
Market Plans 17
Promotion 17
Shipping and Sales Goals 18
Abandoning Products 18
New Plant Requirements 18
vii
viii Forecasting Sales
Environmental Factors Affecting the Forecast 19
Political Factors 19
Economic Factors 20
Customer Factors 20
Competitors 21
Technological Factors 21
Summary 22
Questions 22
2. Forecasting with Econometrics and Economic Studies 25
Introduction 25
Econometric Models 26
Regression: The Basis of Econometric Models 28
A Simple Regression Model 29
Multiple Regression 35
The Selection of Independent Economic Variables 37
Quantitative Variables 37
Qualitative or Dummy Variables 38
Variable Transformations 39
Nonlinear Regressions 41
Examples of Econometric Sales Forecasting Models 41
First Example 41
Second Example 44
Third Example 46
Interpreting Results 46
Benchmark of a Forecast 47
Autoregression 47
Economic or Marketing Studies 48
Marketing Analysis 50
Appendix 51
Notes 53
Questions 54
3. Time Series Forecasting of a Patterned Process 57
Introduction 57
Seasonal Processes 57
Trend 60
Causes of Trend 62
Complex Time Series 63
Decomposing the Time Series 64
Building The Forecasting System or Model 67
f
CONTENTS ix
f Model Building 68
Time Series Forecasting Models 69
The Running Average Forecasting Technique 69
Seasonal Factors 70
An Additional Method of Calculating Seasonality 74
Additional Limitations of Running Averages 75
A Trend Forecasting Model 75
The Exponential Smoothing Model 76
The Smoothing Process 77
The Forecasting Equations 79
Selecting Alpha 82
Correcting Bias 84
Advantages of Exponential Smoothing Forecasting 84
Summary 85
Questions 86
4. The ARIMA or Box Jenkins Forecasting Procedure 87
Introduction 87
Theoretical Aspects 87
Using the Box Jenkins Method 90
Identification 91
The Use of Autocorrelations to Identify Models 91
Identification Procedures 92
Confounding in Autoregressive and Partial Autoregressions 94
Estimation 94
Diagnostic Check 95
Generating the Forecast 95
Technical Aspects of the Box Jenkins Technique 95
Expansion of ARIMA Models 96
An Example of the Box—Jenkins Procedure 96
The Empirical Results 98
5. Data: The Basis of Forecasting 101
Introduction 101
The Sales Generated Time Series 105
Accounting Induced Data Distortions 107
Closing Books 107
Crediting Returned Merchandise 108
Measuring Sales 109
The Number of Days in the Accounting Period 110
Outliers 110
x Forecasting Sales
Dealing with Outliers 111
Filtering 111
A Forecasted Data Approach 112
Trimming 117
Bayesian Analysis 118
Data Reported for Political Purposes 120
Adjustments to Data 122
Production Capacity Effects on Data Patterns 122
Improving Forecasting Accuracy by Identifying Misrepresentative Data 123
Atypical Periods 124
Forecasting Improvement with Outlier Removed Data 125
Replication of the Short Time Series 125
Forecasting Over a Longer Time Frame 126
Keep ajournal 127
Summary 128
Questions 129
6. Combining Forecasts and Partitioning
Time Series to Increase Accuracy 131
Introduction 131
Why Combining 132
An Example Of Forecasting Accuracy
Improvement By Combining 132
Which Combining Method Produces
the Best Forecasts 133
Theories On Why Combining Works 134
Errors 135
Errors in Good Forecasting Models 138
Opposite Signs of the Error Terms 138
An Example of Combining to Improve Accuracy 139
Time Series Components 141
Changing Over Time 142
Fitting, Not Forecasting 143
Partitioning 143
Partitioning Methods 144
Statistical Reasons Why Partitioning Works 145
Increased Sample Size and Reduced Variance 145
Model Misspecification 147
A Test for Forecasting Improvement from Partitioning 147
Where to Partition 148
An Example of Improved Forecasting by Partitioning 148
CONTENTS xi
t Temporary and Permanent Components
of a Time Series 149
Methodology for Partitioning into Permanent and
Temporary Components 151
A Temporary/Permanent Partition Forecasting Example 151
Time Partitioning 156
Generalizability Theory and Partitioning Schemes 157
To Combine or Not To Combine 159
Relationships Of Combining And Partitioning 160
Summary 161
Notes 162
Questions 162
7. Forecasting Economic System Changes Using
Input Output Analysis 163
Introduction 163
Regional Input Output Models 167
Forecasting and the Regional
Input Output Model 169
Input Output Analysis in Forecasting 169
An Example Study of the Input Output Forecasting Procedure 170
Questions 176
8. Stochastic Forecasting 177
Introduction 177
Diffusion Models 178
The Model 178
Empirical Findings 181
Implementation Enhancements 188
Empirically Based Diffusion Rates 190
Diffusion Rates 193
Diffusion Research 193
Forecasting Product Innovations 193
An Introduction to Markovian Modeling in
Market Demand Analysis 196
A Markovian Model:
a Mathematical Analysis For Customer
Demand Level Forecasting 197
The First Order Form 197
xii Forecasting Sales
The Steady State as an Eigenvector of the
Matrix of Transition Probabilities 198
An Application Of The Markovian Customer
Demand Level Model 199
The Markovian Customer Demand Level
Model and the Diffusion Model 201
Integrating Modeling 202
Adaptations to Diffusion Models 203
Issues for Further Investigation 204
Note 205
Questions 206
9. Gravity Modeling: Forecasting the Size of
Flows Between Pairs of Places 207
Introduction 207
The Simple Gravity Model 210
The Production Constrained Gravity Model 210
The Attraction Constrained Gravity Model 211
The Production Attraction Constrained Gravity Model 212
Gravity Model Structural Parameters 213
Determining the Functional Form of the Impedance Function 213
Determining Model Parameters 213
Model Calibration 216
Calibration in a Production Attraction Constrained Gravity Model 216
Forecasting Application of Gravity Models 217
Marketing Applications 217
Migration Model 218
Transportation Planning 219
Questions 219
10. Evaluating Sales Forecasting Models 221
Introduction 221
Four Commonly Used Criteria 221
Ease of Use 221
Expense ***
Lack of Bias 222
Accuracy 223
The Error Loss Function 224
The Mean Squared Error (MSE) 224
The Root Mean Squared Error (RMSE) 225
I
CONTENTS xiii
! The Mean Absolute Error (MAE)
or Mean Absolute Deviation (MAD) 225
Correlation Coefficient 226
Average Percent Error (APE) 226
Mean Absolute Percent Error (MAPE) 2TI
Computer Programs for Evaluating Forecasting Models 227
11. Computer Programs Used in Forecasting 229
Introduction 229
Disclaimer and Caution 229
Free Programs 230
Classified Ads 231
Electronic Bulletin Boards 231
Statistical Packages 231
SASARIMA Programs 239
Appendix A: Forecasting Software 244
Appendix B: Software Exchanges 247
12. Forecasting the Demand for New Products 249
Introduction 249
Problems in Forecasting 249
The Adoption Process 250
Forecasting Without a Relevant Database 251
Types of New Products 252
Test Marketing 253
The Panel Consensus Method 254
Historical Analogy 255
Models Used For Forecasting New Products 256
Fourt and Woodlock (1960) 256
The Bass Model (1969) 258
Limitations of the Bass Model 259
The Massy Model (1969) 260
Limitations of the Massy Model 262
The Eskin Model (1973) 262
The Market Screen Model (1975) 263
Limitations of the Market Screen Model 264
The Delphi Method 265
The Market Research New Product Forecasting Procedures 265
Probability Assessment 268
Model Development 270
Original Purchase Probabilities 271
xiv Forecasting Sales
Mediating Factors 272
Revised Probabilities 273
Notes 274
13. Forecasting Market Share 275
Introduction 275
Methods of Forecasting Market Share 276
Response Models 277
Logit Models 278
Extension of Logit Models 280
Data 281
Methodology 282
Results 283
Logit Transformation Models 284
Market Share Forecasts Using
Conjoint Analysis 288
Conjoint Analysis Process 290
Notes 292
References 293
5
List of Figures
Chapter 1
1. Expenditure of Time to Improve Forecasting Accuracy 9
2. Expenditure of Money to Improve Forecasting Accuracy 10
Chapter 2
1. Sales History of General Motors 29
2. GM Sales, by Quarters 30
Chapter 3
1. Sales of a Patterned Process 58
2. Constant Sales Trend 61
3. Segmented Sales Trends 61
4. Nonlinear Time Series 62
5. Time Series with a Cyclical Pattern 63
6. Flat, Linear, Stable Time Series 64
7. Tourists Visiting Hawaii 65
8. Tourists Visiting Hawaii Adjusting for Trend 66
9. Tourists Visiting Hawaii, Adjusted for
Trend and Seasonality 66
10. Tourists Visiting Hawaii, Adjusted for Trend,
Seasonality and Atypical Events 67
11. Three Month Running Average Forecasting 69
12. Three Month Running Average
Forecasting with Seasonal Factor 70
13. Plot of Seasonal Factors 72
14. Plot of Seasonal Detrended Factors 73
15. Time Series Patterns 80
16. Weighting Data 83
Chapter 4
1. A Sample Autocorrelogram 93
xv
xvi Forecasting Saks
Chapter 5
1. Flat, Stable Time Series 104
2. Variable Time Series 105
Chapter 6
1. Differenced Car Sales Time Series 152
2. Autocorrelations for Car Sales 153
3. Parital Autocorrelations for Car Sales 155
4. Thorndike Framework: Key Variable
Components of Hawaiian Tourism Time Series 158
Chapter 8
1. Comparison of Rate Constants 181
2. Plot of Predicted Peak Sales Level Before and
After Constraint on P* 182
3. Plot of Predicted Time Before and After Constraint on P* 183
4. Sales Forecsts, 1980 1986 184
5. Sales Forecasts, 1980 1986, with Competition 185
6. Sales Forecasts, 1980 1986, with Competition 186
7. Consumer Product Sales Fitted to the Basic Model 187
8. Overall Market Sales Forecast 189
Chapter 11
1. An spss*, ARIMA Program 232
2A. Plot of Original Series 233
2B. Differenced Original Series 234
2C. Autocorrelations of Differenced Series 234
2D. Partial Autocorrelations of Differenced Series 235
2E. Estimated 4 and 0 Values 235
2F. Forecasts 236
2G. Plot of Forecasts and Actuals 236
2H. Error Analyst 237
3A. Autocorrelations of Differenced Series 238
3B. Partial Autocorrelation of Differenced Series 238
3C. Estimated Values of 1 and 0 239
3D. Forecasts 240
4. The Estimate from PROC FORECAST LA 241
I
: List of Tables
Chapter 2
1. Complete Regression Analysis of
Log Transformed PDSS Data 44
2. Multiple Regression Forecast of Food Product Sales 45
Chapter 3
1. Airline Tickets Sold to Hawaii:
Three Month Running Average to Forecast 1970 70
2. Airline Tickets to Hawaii Time Series:
Three Month Running Average with Seasonal Factors 71
3. Seasonal Factors for Visitors to Hawaii 72
4. Trend Calculations (Regression) 74
5. Calculation of Seasonal Factors 75
6. Data Used to Build the Models
(Phoenix Department Store Sales) 78
7. Department Store Forecast and Actual Sales 79
Chapter 4
1. Autocorrelations of the Lagged Series:
Tourists in Hawaii 97
2. Partial Autocorrelations of the Lagged Series:
Tourists in Hawaii 97
3. Summary of the Four Models Suggested in the
Identification State 98
4. Comparison of Forecast Errors 99
Chapter 5
1. Atypical Periods: 1969 1978 115
2. Comparison of Forecasting Errors for 24 Month Period
of Geurts and Ibrahim Study 116
3. Subsequent Month Error Changes for Forecasts
Where the Outlier Month Data is Replaced
with Previous Forecast Value 117
xvii
xviii Forecasting Sales
4. Atypical Periods: 1969 1978 124
5. Comparison of Forecasting Errors for 24 Month
Period of Geurts and Ibrahim Study 126
6. Subsequent Month Error Changes for
Forecasts Where the Outlier Month Data is
Replaced with Previous Forecast Value 127
Chapter 6
1. Percent Increase in Accuracy of Composite 1
Forecast Over Best Individual Forecast 137
2. Comparison of Forecast Errors 140
3. Partitioning into Permanent and Temporary Components 154
4. Yearly Visitors to Hawaii 157
5. Monthly Sales of Tourists to Hawaii 158
Chapter 7
1. Input Coefficient Matrix 165
2. Regional Input Output Table 167
3. Shipment Matrix (For the ith Product) 168
4. Total Output in Millions of Dollars for Industrial
Sector E For a Three Year Period by
Month and by Region 171
5. Forecast of Total Output in Millions of Dollars for Industrial
Sector E for a Two Year Forecasting Horizon by Month 172
6. Product History for Industry E for Region 1 172
7. Intensity of Use for Industrial Sector E for Region 1 173
8. Intensity of Use Forecast for Industrial
Sector E for Region 1 174
9. Forecast for Product for Industry Sector E for Region 1 174
10. Comparison of Forecasts to Actuals for
First Six Forecast Periods for Region 1 1 5
Chapter 8
1. First Purchase Forecasts 1°°
2. a, b, c, and iVValues for the Semiconductor Industry 194
3. a, b, c, N Values 205
Chapter 13
1. Sales, Market Share, Price, Advertising, and Distribution 282
2. Model Accuracy 284
|
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| id | DE-604.BV009919174 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T17:43:12Z |
| institution | BVB |
| isbn | 1559386029 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-006570120 |
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| physical | XIX, 298 S. graph. Darst. |
| publishDate | 1994 |
| publishDateSearch | 1994 |
| publishDateSort | 1994 |
| publisher | Jai Press |
| record_format | marc |
| series | Advances in business and management forecasting |
| series2 | Advances in business and management forecasting |
| spelling | Geurts, Michael Verfasser aut Forecasting sales by Michael Geurts, Kenneth D. Lawrence and John Guerard Greenwich, Conn. u.a. Jai Press 1994 XIX, 298 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advances in business and management forecasting 1 Mathematisches Modell Sales forecasting -- Mathematical models Absatzprognose (DE-588)4252783-1 gnd rswk-swf Absatzprognose (DE-588)4252783-1 s DE-604 Lawrence, Kenneth D. Verfasser aut Guerard, John Verfasser aut Advances in business and management forecasting 1 (DE-604)BV035132238 1 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=006570120&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Geurts, Michael Lawrence, Kenneth D. Guerard, John Forecasting sales Advances in business and management forecasting Mathematisches Modell Sales forecasting -- Mathematical models Absatzprognose (DE-588)4252783-1 gnd |
| subject_GND | (DE-588)4252783-1 |
| title | Forecasting sales |
| title_auth | Forecasting sales |
| title_exact_search | Forecasting sales |
| title_full | Forecasting sales by Michael Geurts, Kenneth D. Lawrence and John Guerard |
| title_fullStr | Forecasting sales by Michael Geurts, Kenneth D. Lawrence and John Guerard |
| title_full_unstemmed | Forecasting sales by Michael Geurts, Kenneth D. Lawrence and John Guerard |
| title_short | Forecasting sales |
| title_sort | forecasting sales |
| topic | Mathematisches Modell Sales forecasting -- Mathematical models Absatzprognose (DE-588)4252783-1 gnd |
| topic_facet | Mathematisches Modell Sales forecasting -- Mathematical models Absatzprognose |
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