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Library of Congress Cataloging-in-Publication Data is Available:
ISBN 978-1-119-41457-5 (Hardcover)
ISBN 978-1-119-41598-5 (ePDF)
ISBN 978-1-119-41600-5 (ePub)
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Cover Image: © kraphix/iStockphoto
To my parents, Sidney and Norma Goodwin
A number of people have assisted me during the preparation of this book. The teams at SAS and Wiley have been helpful in speedily answering my queries and encouraging me to progress the book. In particular, I would like to thank Sheck Cho, Mike Gilliland, Lauree Shepard, Emily Paul, Banurekha Venkatesan, and Siân Roberts. Thanks are also due to Eric Stellwagen of Business Forecast Systems, Inc.
Much of the book reflects what I have learned from my fellow researchers and those with whom I have conducted consultancy work. These are too numerous to mention in their entirety, but they include Robert Fildes, George Wright, Richard Lawton, Dilek Önkal, Kostas Nikolopoulos, John Boylan, Aris Syntetos, Michael Lawrence, and Fotios Petropoulos. Nevertheless, any views expressed in the book are my own and any errors are my responsibility.
I must also thank Len Tashman who has allowed me, for the last 12 years, to write a regular Hot New Research column for Foresight: The International Journal of Applied Forecasting, which he edits. This has motivated me to keep abreast of the very latest research into applied forecasting, much of which is covered in the book.
Finally, I thank my wife, Chris, for her patience and encouragement during the many hours I have spent in my study absorbed in the fascinating and challenging topic of demand forecasting.
I once heard of a woman who was working late on a Friday afternoon in her office when her boss appeared.
“We've just lost our sales forecaster,” he said. “He's transferring to Customer Relations so we need someone to do his job from Monday. I'd like you to take on that role.”
When the woman protested that she had no relevant experience in forecasting or any knowledge of statistics, the boss was reassuring.
“You'll soon pick it up. I think it's mostly done by the computer, anyway.”
And with that he was gone.
If you find yourself in a similar position, then this is the book for you. Research indicates that many forecasters in companies, who may be experts in their products and markets, have little or no formal knowledge of forecasting methods. They are also not mathematicians, so explanations of these methods that befuddle them with reams of formulae and complex notation are of little help. This leads to a temptation to sidestep the methods. Allow the computer to produce its mysterious forecasts, but then replace them with finger-in-the-air judgments; or even avoid the computer altogether and fit a ruler roughly across a hand-drawn sales graph.
Even if you are willing to work with the computer, the technical terms it displays may seem forbidding. MSEs, MAPEs, AICs, exponential smoothing, R-squared, ARIMA models, and autocorrelations can sound as meaningful as the language of quantum physics is to the layperson. And yet, unlike quantum physics, all of these terms can be made understandable to a nonspecialist manager, at least at the intuitive level. In fact, many of them represent very simple concepts that are much easier to comprehend than a typical tax return.
It's a pity if forecasters aren't harnessing the full power of methods that are embodied in modern forecasting software because they don't understand the methods or their output. This book aims to remedy this situation by providing accessible explanations and guidance on when to use different methods and measures. It addresses key practical questions such as:
The book is not tied to any specific forecasting software product. Nor is it intended to duplicate a user's manual, so it won't tell you which button to press or describe particular menu structures. Instead, it has an important complementary role. It draws on the very latest forecasting research to enable you to interact with your software with confidence and insight so you can aim for maximum forecast accuracy, while also making the best use of your time. Depending on what you need to know, you can either read the book in its entirety or use it as a reference guide when you need an explanation, or an evaluation, of a particular method or metric. The focus is on commercial demand forecasting software products, so the book does not consider facilities that may be available in free software, such as R, though much of the content will still be relevant to R users. Also, there is no coverage of specialist software that uses neural networks or conjoint analysis.
Sales forecasts can rarely be perfectly accurate – if they are, the forecaster was either very lucky or was told the exact quantity of orders that were in the pipeline. The true challenge of forecasting is to avoid unnecessary inaccuracy caused by systematic bias, inefficient use of available information, or the wrong choice of method or its application. This book should help you to meet that challenge by employing best practices, so you can ultimately profit from your forecasting software.