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Forecasting Practice and Process for Demand Management

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ISBN-10: 0534262686

ISBN-13: 9780534262686

Edition: 2006

Authors: Hans Levenbach, Leonard J. Tashman, James P. Cleary

List price: $245.95
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How do forecast managers and planners create forecasts for products and services? FORECASTING: PRACTICE AND PROCESS FOR DATA MANAGEMENT covers topics ranging from macroeconomic forecasting procedures to specific product-level forecasting. This is a must-have book for anyone interested in pursuing this field!
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Book details

List price: $245.95
Copyright year: 2006
Publisher: Brooks/Cole
Publication date: 6/22/2005
Binding: Mixed Media
Pages: 600
Size: 5.75" wide x 9.50" long x 0.50" tall
Weight: 2.728
Language: English

Hans Levenbach starts his career at AT&T Bell Laboratories as an applied statistician, participating in forecasting project and developing analytical support systems for the Bell Operating Companies. Over the years he has many years of teaching experience as Adjunct Professor in the Business Schools of Columbia University and New York University. In 1996, he was a Visiting Professor at the Stern School of Business at NYU. In his professional life, he served as President, Treasurer and Board member of the International Institute of Forecasters (IIF). In June 2003 he was elected Fellow of the IIF. Hans graduated from Acadia University (Canada) with a degree in Physics and Mathematics, and…    

James P. Cleary held a number of leadership positions in marketing and finance at AT&T, Lucent Technologies, Avaya, and New York Telephone Company. Recently, he developed market sizing and quantified business case value propositions that demonstrated the product benefits from the perspectives of the end users, the service providers and the manufacturer. He directed the business management functions of strategic planning, business planning, forecasting and results analysis. He also led a forecast improvement team that improved the accuracy of the customer team demand forecasts. He was the Director-Market Research & Analysis in the Business Communications Services unit. His financial…    

Introducing the Forecasting Process
Forecasting as a Structured Process
Inside the Crystal Ball
Is Forecasting Worthwhile? Creating a Structured Forecasting Process
Establishing an Effective Demand Forecasting Strategy
Summary
References
Problems
Useful Reading
Cases
Classifying Forecasting Techniques
Selecting a Forecasting Technique
A Life Cycle Perspective
Market Research
New Product Introductions
Promotions and Special Events
Sales Force Composites and Customer Collaboration
Neural Nets for Forecasting
The Prototypical Forecasting Application: Projecting Historical Patterns
Computer Study: How to Forecast with Weighted Averages
Summary
References
Problems
Useful Reading
Cases
Exploring Time Series
Data Exploration for Forecasting
Exploring Data
Creating Data Summaries
Displaying Data Summaries
Serially Correlated Data
What Does Normality Have to Do with It? The Need for Nontraditional Methods
Summary
References
Problems
Useful Reading
Cases
Appendix A: The Need for Robustness in Correlation
Appendix B: Comparing Estimation Techniques
Characteristics of Time Series
Visualizing Components in a Time Series
A First Look at Trend and Seasonality
What is Stationarity? Classifying Trends
Computer Study: How to Detect Trends
Summary
References
Problems
Computer Exercises
Useful Reading
Cases
Appendix A: A Two-Way Table Decomposition
Assessing Accuracy of Forecasts
The Need to Measure Forecast Accuracy
Ways to Evaluate Accuracy
Measures of Forecast Accuracy
Comparing with Na?ve Techniques
Tracking Tools
Computer Study: How to Monitor Forecasts
Summary
References
Problems
Useful Reading
Cases
Forecasting the Aggregate
Dealing with Seasonal Fluctuations
Seasonal Influences
The Ratio-to-Moving Average Method
Additive and Multiplicative Seasonal Decompositions
Census Seasonal Adjustment Method
Resistant Smoothing
Computer Study: How to Detect Seasonal Cycles-Formalwear Rental Revenue
Summary
References
Problems
Computer Exercises
Useful Reading
Cases
Forecasting the Business Environment
Forecasting with Economic Indicators
Trend-Cycle Forecasting with Turning Points
Using Elasticities
Econometrics and Business Forecasting
Computer Study: Using "Pressures" to Analyze Business Cycles
Summary
References
Problems
Computer Exercises
Useful Reading
Cases
Applying Bottom-Up Techniques
The Exponential Smoothing Method
What is Exponential Smoothing? Smoothing Weights
Types of Smoothing Techniques
Smoothing Levels and Constant Change
Damped and Exponential Trends
Seasonal Models
Handling Special Events with Smoothing Models
Summary
References
Problems
Computer Exercises
Useful Reading
Cases
Appendix
Disaggregate Product-Demand Forecasting
Forecasting for the Supply Chain
A Framework for an Integrated Demand Forecasting System
Automated Statistical Forecasting
Disaggregate Product-Demand Forecasting Checklist
Computer Study: How to Create a Time-Phased Replenishment Plan
Summary
References
Problems
Computer Exercises
Useful Reading
Cases
Forecasting with Causal Forecasting Models
Creating and Analyzing Causal Forecasting Models
A Model Building Strategy
What are Regression Models? Creating Multiple Linear Regression Models
Learning from Residual Patterns
Validating Preliminary Modeling Assumptions
Computer Study: How to Forecast with Transformed Data
Summary
References
Problems
Computer Exercises
Useful Reading
Cases
Appendix: Achieving Linearity
Linear Regression Analysis
Graphing Relationships
Creating and Interpreting Output
Making Inferences about Model Parameters
Autocorrelation Correction
Summary
References
Problems
Computer Exercises
Useful Reading
Cases
Forecasting with Regression Models
Multiple Linear Regression Analysis
Assessing Model Adequacy
Selecting Variables
Indicators for Qualitative Variables
Analyzing Residuals
The Need for Robustness in Regression
Multiple Regression Checklist
Computer Study: How to Forecast with Qualitative Variables
Summary
References
Computer Exercises
Useful Reading
Cases
Forecasting with Arima Models
Building ARIMA Models: The Box-Jenkins Approach
Why Use ARIMA Models for Forecasting? The Linear Filter Model as A Black Box
A Model Building Strategy
Identification: Interpreting ACF and PACF
Identifying Nonseasonal ARIMA Models
Estimation: Fitting Models to Data
Diagnostic Checking: Validating Model Adequacy
Implementing Nonseasonal ARIMA Models
Identifying Seasonal ARIMA Models
Implementing Seasonal ARIMA Models
ARIMA Modeling Checklist
Summary
References
Problems
Computer Exercises
Useful Reading
Cases
Forecasting with ARIMA Models
ARIMA Models for Forecasting
Models for Forecasting Stationary Time Series
Models for Nonstationary Time Series
Seasonal ARIMA Models
Forecast Probability Limits
ARIMA Forecasting Checklist
Summary
References
Problems
Computer Exercises
Useful Reading
Cases
Appendix A: Expressing ARIMA Models in Compact Form
Appendix B: Forecast Error and Forecast Variance for ARIMA Models
Improving Forecasting Effectiveness
Selecting the Final Forecast Number
Preparing Forecast Scenarios
Establishing Credibility
Using Forecasting Simulations
Designing Forecasting Simulations
Reconciling Sales Force and Customer Inputs
Gaining Acceptance from Management
The Forecaster's Checklist
Summary
References
Case
Useful Reading
Cases
Implementing the Forecasting Process
PEERing into the Future
A Framework for Process Improvement
An Implementation Checklist
Using "Virtual" Forecasting Services
The Forecasting Manager's Checklists
Summary
References
Useful Reading
Cases
Glossary