Cover Page

Profit from Your Forecasting Software

A Best Practice Guide for Sales Forecasters

 

 

Paul Goodwin

 

 

 

 

 

 

 

 

 

 

 

 

Wiley Logo

Wiley & SAS Business Series

The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions.

Titles in the Wiley & SAS Business Series include:

  • Analytics: The Agile Way by Phil Simon
  • Analytics in a Big Data World: The Essential Guide to Data Science and Its Applications by Bart Baesens
  • A Practical Guide to Analytics for Governments: Using Big Data for Good by Marie Lowman
  • Bank Fraud: Using Technology to Combat Losses by Revathi Subramanian
  • Big Data Analytics: Turning Big Data into Big Money by Frank Ohlhorst
  • Big Data, Big Innovation: Enabling Competitive Differentiation through Business Analytics by Evan Stubbs
  • Business Analytics for Customer Intelligence by Gert Laursen
  • Business Intelligence Applied: Implementing an Effective Information and Communications Technology Infrastructure by Michael Gendron
  • Business Intelligence and the Cloud: Strategic Implementation Guide by Michael S. Gendron
  • Business Transformation: A Roadmap for Maximizing Organizational Insights by Aiman Zeid
  • Connecting Organizational Silos: Taking Knowledge Flow Management to the Next Level with Social Media by Frank Leistner
  • Data-Driven Healthcare: How Analytics and BI Are Transforming the Industry by Laura Madsen
  • Delivering Business Analytics: Practical Guidelines for Best Practice by Evan Stubbs
  • Demand-Driven Forecasting: A Structured Approach to Forecasting, Second Edition by Charles Chase
  • Demand-Driven Inventory Optimization and Replenishment: Creating a More Efficient Supply Chain by Robert A. Davis
  • Developing Human Capital: Using Analytics to Plan and Optimize Your Learning and Development Investments by Gene Pease, Barbara Beresford, and Lew Walker
  • Economic and Business Forecasting: Analyzing and Interpreting Econometric Results by John Silvia, Azhar Iqbal, Kaylyn Swankoski, Sarah Watt, and Sam Bullard
  • Economic Modeling in the Post Great Recession Era: Incomplete Data, Imperfect Markets by John Silvia, Azhar Iqbal, and Sarah Watt House
  • Enhance Oil & Gas Exploration with Data Driven Geophysical and Petrophysical Models by Keith Holdaway and Duncan Irving
  • The Executive's Guide to Enterprise Social Media Strategy: How Social Networks Are Radically Transforming Your Business by David Thomas and Mike Barlow
  • Foreign Currency Financial Reporting from Euros to Yen to Yuan: A Guide to Fundamental Concepts and Practical Applications by Robert Rowan
  • Harness Oil and Gas Big Data with Analytics: Optimize Exploration and Production with Data Driven Models by Keith Holdaway
  • Health Analytics: Gaining the Insights to Transform Health Care by Jason Burke
  • Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World by Carlos Andre Reis Pinheiro and Fiona McNeill
  • Human Capital Analytics: How to Harness the Potential of Your Organization's Greatest Asset by Gene Pease, Boyce Byerly, and Jac Fitz-enz
  • Implement, Improve and Expand Your Statewide Longitudinal Data System: Creating a Culture of Data in Education by Jamie McQuiggan and Armistead Sapp
  • Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards, Second Edition, by Naeem Siddiqi
  • JMP Connections by John Wubbel
  • Killer Analytics: Top 20 Metrics Missing from your Balance Sheet by Mark Brown
  • Machine Learning for Marketers: Hold the Math by Jim Sterne
  • On-Camera Coach: Tools and Techniques for Business Professionals in a Video-Driven World by Karin Reed
  • Predictive Analytics for Human Resources by Jac Fitz-enz and John Mattox II
  • Predictive Business Analytics: Forward-Looking Capabilities to Improve Business Performance by Lawrence Maisel and Gary Cokins
  • Profit-Driven Business Analytics: A Practitioner's Guide to Transforming Big Data into Added Value by Wouter Verbeke, Cristian Bravo, and Bart Baesens
  • Retail Analytics: The Secret Weapon by Emmett Cox
  • Social Network Analysis in Telecommunications by Carlos Andre Reis Pinheiro
  • Statistical Thinking: Improving Business Performance, Second Edition by Roger W. Hoerl and Ronald D. Snee
  • Strategies in Biomedical Data Science: Driving Force for Innovation by Jay Etchings
  • Style & Statistics: The Art of Retail Analytics by Brittany Bullard
  • Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics by Bill Franks
  • Too Big to Ignore: The Business Case for Big Data by Phil Simon
  • The Analytic Hospitality Executive by Kelly A. McGuire
  • The Value of Business Analytics: Identifying the Path to Profitability by Evan Stubbs
  • The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions by Phil Simon
  • Using Big Data Analytics: Turning Big Data into Big Money by Jared Dean
  • Win with Advanced Business Analytics: Creating Business Value from Your Data by Jean Paul Isson and Jesse Harriott

For more information on any of the above titles, please visit www.wiley.com.

To my parents, Sidney and Norma Goodwin

Acknowledgments

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.

Prologue

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:

  • When, if ever, should management judgment be used to adjust or override a computer's forecast?
  • Should I choose my own forecasting method or let the expert system in the software choose it?
  • How should I use the software to handle product hierarchies or to plan safety stock levels?
  • How much past data should I use to fit and test my forecasting model?

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.