Business forecasting : practical problems and solutions /
"A comprehensive collection of the field's most provocative, influential new work Business Forecasting compiles some of the field's important and influential literature into a single, comprehensive reference for forecast modeling and process improvement. It is packed with provocative...
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| Other Authors: | , , |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken :
Wiley,
2015.
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| Series: | Wiley and SAS business series.
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| Subjects: | |
| Online Access: | Full text (Emerson users only) Full text (Emmanuel users only) Full text (NECO users only) Full text (MCPHS users only) Access E-Book Full text (Wentworth users only) Full text (Wentworth users only) Full text (Wentworth users only) Full text (Wentworth users only) Full text (Wentworth users only) |
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| 245 | 0 | 0 | |a Business forecasting : |b practical problems and solutions / |c edited by Michael Gilliland, Udo Sglavo, Len Tashman. |
| 264 | 1 | |a Hoboken : |b Wiley, |c 2015. | |
| 300 | |a 1 online resource | ||
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| 490 | 1 | |a Wiley and SAS business series | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Foreword ; Preface ; Chapter 1 Fundamental Considerations in Business Forecasting ; 1.1 Getting Real About Uncertainty (Goodwin -- Foresight) ; 1.2 What Demand Planners Can Learn from the Stock Market (ReCorr -- JBF) ; 1.3 Toward a More Precise Definition of Forecastability (Boylan -- Foresight) ; 1.4 Forecastability: A New Method (Schubert -- Foresight) ; 1.5 Forecast Errors and Their Avoidability (Morlidge -- Foresight) ; 1.6 The Perils of Benchmarking (Gilliland -- APICS eNews) ; 1.7 Can We Obtain Valid Benchmarks from Published Surveys of Forecast Accuracy? (Kolassa -- Foresight) ; 1.8 Defining 'Demand' for Demand Forecasting (Gilliland -- Foresight) ; 1.9 Using Forecasting to Steer the Business: Six Principles (Morlidge -- Foresight) ; 1.10 The Beauty of Forecasting (Orrell -- Foresight) ; Chapter 2 Methods of Statistical Forecasting ; 2.1 Confessions of a Pragmatic Forecaster (Chatfield -- Foresight) ; 2.2 New Evidence on the Value of Combining Forecasts (Goodwin -- Foresight) ; 2.3 How to Forecast Data Containing Outliers (Stellwagen -- Trends Newsletter) ; 2.4 Selecting Your Statistical Forecasting Level (Stellwagen -- ForecastPro blog) ; 2.5 When is a Flat-line Forecast Appropriate (Stellwagen -- ForecastPro blog) ; 2.6 Forecasting by Time Compression (Sglavo -- SAS whitepaper) ; 2.7 Data Mining for Forecasting: An Introduction (Wells & Rey -- new content -- adapt from book) ; 2.8 Process and Methods for Data Mining for Forecasting (Wells & Rey -- new content -- adapt from book) ; 2.9 Worst-Case Scenarios in Forecasting: How Bad Can Things Get (Batchelor -- Foresight) ; 2.10 Good Patterns, Bad Patterns (Batchelor -- Foresight) ; Chapter 3 Forecasting Performance Evaluation & and Reporting ; 3.1 Dos and Don'ts of Forecast Accuracy Measurement: A Tutorial (Tashman -- Foresight) ; 3.2 How to Track Forecast Accuracy to Guide Forecast Process Improvement (Hoover -- Foresight) ; 3.3 A "Softer" Approach to the Measurement of Forecast Accuracy (Boylan -- Foresight) ; 3.4 Measuring Forecast Accuracy (Hyndman -- new content) ; 3.5 Should We Define Forecast Error as e = F A or e = A F? (Green & Tashman -- Foresight) ; 3.6 Percentage Error: What Denominator? (Green & Tashman -- Foresight) ; 3.7 Percentage Errors Can Ruin Your Day (Kolassa & Martin -- Foresight) ; 3.8 Another Look at Forecast-Accuracy Metrics for Intermittent Demand (Hyndman -- Foresight) ; 3.9 Advantages of the MAD/Mean Ratio Over the MAPE (Kolassa & Schutz -- Foresight) ; 3.10 Use Scaled Errors Instead of Percentage Errors in Forecast Evaluations (Valentin -- Foresight) ; 3.11 An Expanded Prediction-Realization Diagram for Assessing Forecast Errors (Pearson -- Foresight) ; 3.12 Forecast Error Measures: Critical Review and Practical Recommendations (Davydenko & Fildes -- adapt from IJF) ; 3.13 Measuring the Quality of Intermittent Demand Forecasts: It's Worse than We've Thought! (Steve Morlidge -- Foresight) ; 3.14 Managing Forecasts by Exception (Stellwagen -- ForecastPro blog) ; 3.15 Using Process Behaviour Charts to Improve Forecasting and Decision-Making (Joseph & Finney -- Foresight) ; 3.16 Can Your Forecast Beat the Naïve Forecast (Snapp -- SCMFocus blog) ; Chapter 4 Process and Politics of Business Forecasting ; 4.1 FVA: A Reality Check on Forecasting Practices (Gilliland -- Foresight) ; 4.2 Where Should the Forecasting Function Reside (Lapide -JBF)[AU: Spell out JBF] ; 4.3 Setting Performance Objectives (Gilliland -- BFD blog)[AU: spell out BFD.] ; 4.4 Using Relative Error Metrics to Improve Forecast Quality in the Supply Chain (Morlidge -- Foresight) ; 4.5 Why Should I Trust Your Forecasts (Gonul, Onkal & Goodwin -- Foresight) ; 4.6 High on Complexity, Low on Evidence: Are Advanced Forecasting Methods Always as Good as They Seem? (Goodwin -- Foresight) ; 4.7 Should the Forecasting Process Eliminate Face-to-Face Meetings? (Armstrong -- Foresight) ; 4.8 The Impact of Sales Forecast Game Playing on Supply Chains (Mello -- Foresight) ; 4.9 Role of the Sales Force in Forecasting (Gilliland -- Foresight) ; 4.10 Good and Bad Judgment in Forecasting (Fildes & Goodwin -- Foresight) ; 4.11 Worst practices in new product forecasting (Gilliland -- JBF) ; 4.12 Sales & Operations Planning in the Retail Industry (Harwell -- JBF) ; 4.13 Sales & Operations Planning: Where Is It Going? (Wallace -- JBF) ; Index | |
| 520 | |a "A comprehensive collection of the field's most provocative, influential new work Business Forecasting compiles some of the field's important and influential literature into a single, comprehensive reference for forecast modeling and process improvement. It is packed with provocative ideas from forecasting researchers and practitioners, on topics including accuracy metrics, benchmarking, modeling of problem data, and overcoming dysfunctional behaviors. Its coverage includes often-overlooked issues at the forefront of research, such as uncertainty, randomness, and forecastability, as well as emerging areas like data mining for forecasting. The articles present critical analysis of current practices and consideration of new ideas. With a mix of formal, rigorous pieces and brief introductory chapters, the book provides practitioners with a comprehensive examination of the current state of the business forecasting field. Forecasting performance is ultimately limited by the 'forecastability' of the data. Yet failing to recognize this, many organizations continue to squander resources pursuing unachievable levels of accuracy. This book provides a wealth of ideas for improving all aspects of the process, including the avoidance of wasted efforts that fail to improve (or even harm) forecast accuracy. Analyzes the most prominent issues in business forecasting Investigates emerging approaches and new methods of analysis Combines forecasts to improve accuracy Utilizes Forecast Value Added to identify process inefficiency The business environment is evolving, and forecasting methods must evolve alongside it. This compilation delivers an array of new tools and research that can enable more efficient processes and more accurate results. Business Forecasting provides an expert's-eye view of the field's latest developments to help you achieve your desired business outcomes"-- |c Provided by publisher. | ||
| 520 | |a "This title provides many of the most important and though-provoking articles by the leading business forecasting practitioners and academics. It exposes the reader to many of the best minds (and most provocative ideas) in the forecasting profession, with thorough referencing to related material for further reading. It provides: - A critical look at many of the vexing problems in business forecasting, such as volatility, forecastability, performance metrics, and human interaction in the forecasting process. - Introduces emerging new approaches such as combining data mining with forecasting and aggregating/reconciling across time hierarchies. - Addresses the often overlooked topic of data preparation and data quality (part of the "pre-processing" of data prior to forecasting. - Covers the proven (yet rarely used) method of combining forecasts to improve accuracy. Contains a mix of more formal/rigorous pieces, with brief chapters (adapted from blog posts) dealing narrowly with very specific topics"-- |c Provided by publisher. | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 650 | 0 | |a Business forecasting. | |
| 700 | 1 | |a Gilliland, Michael, |e editor. | |
| 700 | 1 | |a Sglavo, Udo, |d 1968- |e editor. | |
| 700 | 1 | |a Tashman, Len, |d 1942- |e editor. | |
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| 830 | 0 | |a Wiley and SAS business series. | |
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