Book descriptionA 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.
Table of contents
Chapter 1 Fundamental Considerations in Business Forecasting
- 1.1 Getting Real about Uncertainty
- 1.2 What Demand Planners Can Learn from the Stock Market
- 1.3 Toward a More Precise Definition of Forecastability
- 1.4 Forecastablity: A New Method for Benchmarking and Driving Improvement
- 1.5 Forecast Errors and Their Avoidability
- 1.6 The Perils of Benchmarking
- 1.7 Can We Obtain Valid Benchmarks from Published Surveys of Forecast Accuracy?
- 1.8 Defining “Demand” for Demand Forecasting
- 1.9 Using Forecasting to Steer the Business: Six Principles
- 1.10 The Beauty of Forecasting
Chapter 2 Methods of Statistical Forecasting
- 2.1 Confessions of a Pragmatic Forecaster
- 2.2 New Evidence on the Value of Combining Forecasts
- 2.3 How to Forecast Data Containing Outliers
- 2.4 Selecting Your Statistical Forecasting Level
- 2.5 When Is a Flat-line Forecast Appropriate?
- 2.6 Forecasting by Time Compression
- 2.7 Data Mining for Forecasting: An Introduction
- 2.8 Process and Methods for Data Mining for Forecasting
- 2.9 Worst-Case Scenarios in Forecasting: How Bad Can Things Get?
- 2.10 Good Patterns, Bad Patterns
Chapter 3 Forecasting Performance Evaluation and Reporting
- 3.1 Dos and Don’ts of Forecast Accuracy Measurement: A Tutorial
- 3.2 How to Track Forecast Accuracy to Guide Forecast Process Improvement
- 3.3 A “Softer” Approach to the Measurement of Forecast Accuracy
- 3.4 Measuring Forecast Accuracy
- 3.5 Should We Define Forecast Error as e = F – A or e = A – F?
- 3.6 Percentage Error: What Denominator?
- 3.7 Percentage Errors Can Ruin Your Day
- 3.8 Another Look at Forecast-Accuracy Metrics for Intermittent Demand
- 3.9 Advantages of the MAD/Mean Ratio over the MAPE
- 3.10 Use Scaled Errors Instead of Percentage Errors in Forecast Evaluations
- 3.11 An Expanded Prediction-Realization Diagram for Assessing Forecast Errors
- 3.12 Forecast Error Measures: Critical Review and Practical Recommendations
- 3.13 Measuring the Quality of Intermittent Demand Forecasts: It’s Worse than We’ve Thought!
- 3.14 Managing Forecasts by Exception
- 3.15 Using Process Behavior Charts to Improve Forecasting and Decision Making
- 3.16 Can Your Forecast Beat the Naïve Forecast?
Chapter 4 Process and Politics of Business Forecasting
- 4.1 FVA: A Reality Check on Forecasting Practices
- 4.2 Where Should the Forecasting Function Reside?
- 4.3 Setting Forecasting Performance Objectives
- 4.4 Using Relative Error Metrics to Improve Forecast Quality in the Supply Chain
- 4.5 Why Should I Trust Your Forecasts?
- 4.6 High on Complexity, Low on Evidence: Are Advanced Forecasting Methods Always as Good as They Seem?
- 4.7 Should the Forecasting Process Eliminate Face-to-Face Meetings?
- 4.8 The Impact of Sales Forecast Game Playing on Supply Chains
- 4.9 Role of the Sales Force in Forecasting
- 4.10 Good and Bad Judgment in Forecasting: Lessons from Four Companies
- 4.11 Worst Practices in New Product Forecasting
- 4.12 Sales and Operations Planning in the Retail Industry
- 4.13 Sales and Operations Planning: Where Is It Going?
- About the Editors
- Title: Business Forecasting
- Release date: January 2016
- Publisher(s): Wiley
- ISBN: 9781119224563
You might also like
Discover the role of machine learning and artificial intelligence in business forecasting from some of the …
Financial Forecasting and Decision Making
Many companies fail to succeed due to poor planning, which is one reason why accountants are …
Demand-Driven Forecasting: A Structured Approach to Forecasting, 2nd Edition
An updated new edition of the comprehensive guide to better business forecasting Many companies still look …
Demand Forecasting for Managers
Most decisions and plans in a firm require a forecast. Not matching supply with demand can …