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How do forecast managers and planners create forecasts for products and services? FORECASTING: PRACTICE AND PROCESS FOR DEMAND 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!
- Sales Rank: #711366 in Books
- Brand: Brand: Cengage Learning
- Published on: 2005-06-22
- Original language: English
- Number of items: 1
- Dimensions: 1.19" h x 7.38" w x 9.82" l, 2.54 pounds
- Binding: Hardcover
- 400 pages
- Used Book in Good Condition
Review
Part I: INTRODUCING THE FORECASTING PROCESS. 1. 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. 2. 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. Part II: EXPLORING TIME SERIES. 3. 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. 4. 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. 5. Assessing Accuracy of Forecasts. The Need to Measure Forecast Accuracy. Ways to Evaluate Accuracy. Measures of Forecast Accuracy. Comparing with Naive Techniques. Tracking Tools. Computer Study: How to Monitor Forecasts. Summary. References. Problems. Useful Reading. Cases. Part III: FORECASTING THE AGGREGATE. 6. 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. 7. 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. Part IV: APPLYING BOTTOM-UP TECHNIQUES. 8. 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. 9. 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. Part V: FORECASTING WITH CAUSAL FORECASTING MODELS. 10. 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. 11. Linear Regression Analysis. Graphing Relationships. Creating and Interpreting Output. Making Inferences about Model Parameters. Autocorrelation Correction. Summary. References. Problems. Computer Exercises. Useful Reading. Cases. 12. 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. Part VI: FORECASTING WITH ARIMA MODELS. 13. 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. 14. 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. Part VII: IMPROVING FORECASTING EFFECTIVENESS. 15. 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. 16. 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.
About the Author
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 received an MSc in Electrical Engineering from Queen's University (Canada) and an M.A. and Ph.D. in Mathematical Statistics from the University of Toronto. Hans is involved in all aspects of the company and particularly enjoys working with forecasting practitioners and software developers.
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 positions included the CFO-Data Communications Services and CFO-Computer Systems. Mr. Cleary developed courses in time series and econometric modeling for AT&T. He also developed a course for managers of the forecasting function.
Most helpful customer reviews
0 of 0 people found the following review helpful.
Helpful and highly recommended
By Jared R. Endicott
I highly recommend "Forecasting: Practice and Process for Demand Management" for anyone interested in learning the technical and practical techniques for successful business forecasting.
I am a forecasting practitioner, and I have read a few books on technical forecasting. This text is the best one that I have come across. I received this book when I took a certification workshop with one of its co-authors, Hans Levenbach. While I recommend the workshop as well, the book stands on its own as an excellent primer and resource on business forecasting. The text is based on a process oriented approach, which merges the practical aspects of prediction modeling in the real time business environment with in depth textbook coverage of technical methods.
Levenbach and Cleary present a clear and detailed description of the most common and useful forecasting techniques, exponential smoothing, regression modeling, and ARIMA. In addition, they provide extensive treatment of practical techniques for data exploration, time series analysis, and accuracy assessment, as well as enlightening chapters on how to handle seasonality, business cycles, and disaggregate product-demand forecasting, including a section on the automation of statistical forecasting.
The final section of the book focuses on ways to improve forecasting effectiveness, and these two very helpful chapters gave me a practical perspective on forecasting in my day to day job that I have not found in any other book on technical forecasting. The authors lay out a structured forecasting process that establishes pragmatic ways to achieve and improve forecasting accuracy, credibility, goals, and the overall effectiveness of your forecasting organization.
Another great feature of the text is its frequent checklists, which give step by step functional instructions for various scenarios that come up in business forecasting. Helpful and highly recommended.
Jared Endicott
0 of 0 people found the following review helpful.
A formula for success
By Stephen Kitchen
We live in a world where Statistical Forecasting is poorly understood.
Statisticians do not understand business and business does not understand statisticians.
This book and the processes it outlines bridge that divide.
It builds up concepts from first principals so that practitioners can understand what their forecasting tool is doing in the 'black box'. More importantly it highlights what many forecast tools are NOT doing and arms practitioners with the right questions to ask software vendors.
It is for serious Demand Managers and Planners who are looking for a detailed reference tool to equip them in creating statistical forecasts to be used in the Demand Planning process
0 of 0 people found the following review helpful.
Practical Business
By BI
The application of statistics to a discipline like Forecasting, demand planning and predictive analytics must be taken seriously. The practitioner is expected to be able to stand behind not only the methods and output, but the quality and interpertation of source data. The professionalism required is throughly embodied throughout the textbook and is supplimented with many real world experiences from large and small companies and enterprises. Forecasting is as comprehensive a referance and learning tool that will expand the readers capabilities and career.
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