Advances in Business and Management Forecasting
Volume 4
1st Edition
Published on 17. February 2006
300 pages
978-0-08-045658-4 (ISBN)
Description
"e;Advances in Business and Management Forecasting"e; is a blind refereed serial publication published on an annual basis. The objective of this research annual is to present state-of-the-art studies in the application of forecasting methodologies to such areas as sales, marketing, and strategic decision making. (An accurate, robust forecast is critical to effective decision making.) It is the hope and direction of the research annual to become an applications- and practitioner-oriented publication. The topics will normally include sales and marketing, forecasting, new product forecasting, judgmentally based forecasting, the application of surveys to forecasting, forecasting for strategic business decisions, improvements in forecasting accuracy, and sales response models. It is both the hope and direction of the editorial board to stimulate the interest of the practitioners of forecasting to methods and techniques that are relevant.
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
File size
2,28 MB
ISBN-13
978-0-08-045658-4 (9780080456584)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Kenneth D. Lawrence | Michael D. Geurts
Advances in Business and Management Forecasting
Book
02/2006
JAI Press Inc.
€164.00
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Content
- Intro
- copyright
- Contents
- front matter
- List of Contributors
- Editorial Board
- Statement of Purpose
- Forecasting in Supply Chain Management
- An Introduction to Supply Chain Management
- Demand Chains
- Forecasting in Supply Chain
- Forecasting at the SKU Level
- Inventory Drivers
- The Bullwhip Effect
- Conclusion
- References
- Extracting Forecasts from Advance Orders
- One View of the Future
- A Second and Alternative View of the Future
- A Needed Conditional View of the Future
- More Precise Prior Posterior Notation
- Guiding the Sales Trajectory Y(t|t) with the Signals P¯¹ (t)*Z(t) from the Advance Orders
- An Application
- Conclusions, Managerial Ramifications and Future Research
- Guided Forecasts Converge on the True Sales Trajectory
- Early Warnings of Future Changes
- Predicting the Turning Point of a Brand's Life Cycle Trajectory
- Research Opportunities
- References
- Inventory-Shipment Ratio Time Series Models for Durable and Non-Durable Products
- Introduction
- Literature Review
- Motivation and Background
- Descriptive Statistics
- Modeling
- Durable Products
- Non-Durable Products
- Total Products
- Forecast Comparisons
- Discussion
- Acknowledgment
- References
- Appendix
- An Application of Confirmatory Factor Analysis to the A Priori Classification of Financial Ratios
- Introduction
- Literature Review
- Data and Modeling Considerations
- Results
- Concluding Remarks
- References
- Bank Rating Change Predictions: Alternative Forecasting Models
- Introduction
- Background
- Literature Review
- Data, Research Design, and Results
- Future Research
- References
- Forecasting Security Returns: The use of Heterogeneous Expectations
- Introduction
- Review of the literature
- Methodology
- Data and Sample Characteristics
- Results
- Conclusions, Recommendations and Directions for Future Research
- References
- Combining Moving Averages with Exponential Smoothing to Produce More Stable Sales Forecasts
- Introduction
- Example
- Notation
- Proposed Forecasting Model
- Computation of Base Level
- Computation of Trend
- Computation of Seasonal Factors
- Computation of Projected Base Levels
- Computation of Forecasts
- Model Fitting and Application
- Model Performance
- Conclusion
- Reference
- Improved Exponential Smoothing with Applications to Sales Forecasting
- Introduction
- Proposed Smoothing Equation
- Applications to Sales Forecasting
- Conclusion
- References
- Using Flow-Through and Diffusion Models to Forecast New Product Sales
- Flow-Through Models
- Diffusion Models
- Diffusion Model Software
- The Oral/Nasal Insulin Example
- References
- An Application of a Repeat Purchase Diffusion Model to the Pharmaceutical Industry
- Introduction
- Diffusion and Repeat Purchase
- The Model
- Marketing/Sales Organization/Customer Relationship in a Detail-Intensive Industry
- Allocation Percentages and the Effects of the Marketing Mix
- The Full Allocation Model
- Empirical Application
- The Data
- Calibration Results
- Fit Statistics (H4)
- Parameter Estimates and Standard Errors (H1)
- ?1, ?2, and r(t) values (H2 and H3)
- Forecasting
- Conclusion
- Notes
- References
- Forecasting Product Sales with Conjoint Analysis Data
- Conjoint Analysis
- Competitive Reaction
- Adding Change into the Forecast
- Funnel Analysis
- Choice Probabilities: Necessary but Insufficient
- References
- Improving Sales Forecastsby Testing Underlying Hypotheses about Consumer Behavior: A Proposed Qualitative Method
- Introduction
- Quantitative Survey Methods
- Qualitative Methods
- The Biasing Influence of a Priori Hypotheses
- Outline of Proposed Method
- Example of Proposed Method
- Qualitative Researcher
- Zaltman Metaphor Elicitation Technique
- ZMET Data Collection
- ZMET Analysis
- The Hypothesis Test
- Limitations of the Proposed Method
- Applicability of the Proposed Method
- Conclusions
- References
- Forecasting Sales of Comparable Units with Data Envelopment Analysis (DEA)
- Introduction
- Data Envelopment Analysis (DEA)
- New Regression Forecasting Methodology
- Example of Regression Analysis of Comparable Units
- Conclusions
- References
- Appendix
- Data Mining Reliability: Model-Building with MARS and Neural Networks
- Data Mining, Reliability, and Model-Building
- Methods Used in this Study
- Forward Stepwise Regression
- Neural Networks
- Network Architecture
- Solving the Network: ''Training'' Multi-Layer Perceptrons
- NNW Implementation in this Study
- Multivariate Adaptive Regression Splines
- MARS Implementation in this Study
- Hypotheses
- Background
- Previous Studies
- Analysis
- Study Protocol
- Data Development
- Results
- Accuracy
- Underfitting
- Overfitting
- Factors of Influence
- Overall
- Underfit
- Overfit
- Methods Comparison: Contrasts
- PMSE
- Underfit
- Overfit
- Conclusions
- References
- Appendix: Data Description
- Selecting Forecasting Intervals to Increase Usefulness and Accuracy
- Introduction
- Artificial Boundaries
- Commodity Purchases
- Variance and Forecasting Accuracy
- Accuracy Determined by Selected Interval
- Conclusion
- Forecasting Simultaneous Brand Life Cycle Trajectories
- Consistent Measurements of Market Share Trajectories
- Real Markets and an Equivalent URN Model
- The Probability Density Governing Simultaneous Market Shares
- Using Current Market Output Signals to Update the Prior Share Estimates
- Increasing the Relative Weight of New Information
- An Efficient Algorithm for Revising Conditional Probabilities Sequentially
- An Application: Measuring the Market Shares of Five Competing Brands
- Consistent, Simultaneous Share and Life Cycle Forecasts
- Forecast the Shares of the Brands in Category 1 for Declining Brands
- Forecast the Shares of the Brands in Category 2 of Non-Declining Brands
- Identify the Dominant Growth Brand in Category 2
- Forecast the Market Share of the Dominant Growth Brand
- Forecast the Shares of the Remaining Brands in Category 2
- Forecast Compared with Actual
- Summary and Conclusions
- References
- A Typology of Psychological Biases in Forecasting Analysis
- Introduction
- The Cognitive Process
- Types of Psychological Biases
- Cognitive
- Philosophical Orientation
- Organizational
- Operational
- Categories of Biases
- Summary
- References
- A Forecast Combination Methodology for Demand Forecasting
- Introduction
- Forecast Combination
- Case Study
- Stage 1: Data Preparation
- Stage 2: Generation of the Component Forecast Models
- Stage 3: Generation of Alternative Combined Forecasts
- Stage 4: Model Comparisons in Year 1
- Stage 5: Generation of Forecasts for Year 2
- Discussion
- References
