Contributed by Charles Chase, Business Enablement Manager for SAS's Manufacturing and Supply Chain Global Practice
As readers of books we rarely consider their origin. A book just magically appears on the bookshelf. We decide whether it is worth reading and then either buy it or look for another. Think about it. Have you ever really considered how a book actually comes to life, or why someone decides to write a book? Being a fan of history, and realizing that forecasting is based on past historical trends and relationships, I rarely ever think about the origins of a book, when in fact it takes roughly two years to write and publish a book from conceptual design (or outline) to the actual publishing of the book. It takes a minimum of one year to write the content of all the chapters. Not to mention the multitude of edits and rewrites based on technical reviews, grammatical reviews, and layout configurations. The real reward is when you hold the book in your hand in amazement after working late into the evenings and weekends, thinking to yourself, I just wrote this book. It was a tearful event for me. I could hardly talk. I just kept looking at it in astonishment, because I never thought that such an opportunity would ever materialize, let alone anyone would actually want to read what I have to say, about anything.
Demand-Driven Forecasting began with a chance meeting with SAS Publications Sales Manager Lou Metzger, whom I met when I was speaking at an annual SAS publications conference at the University of Louisville in 2007. We spoke several times during that event about my twenty years of experience in forecasting as well as the many lectures I’ve given and articles I’ve published. Lou was convinced that my practical experience working in the consumer packaged goods industry prior to coming to SAS would translate into valuable lessons worth sharing with others. A few months later I received a call from the SAS Press Editor-in-Chief Julie Platt, who arranged for the submission of a book outline to John Wiley & Sons. Six months later, Wiley sent its approval to begin writing the book. That was the easy part. With a great deal of guidance and patience from Stacey Hamilton, my SAS Press editor, and technical editors Dr. Ken Kahn and Mike Gilliland, I began the long process of writing the book. It took roughly one month to write each chapter (nine chapters in total). My technical reviewers kept me honest to the discipline of demand forecasting and supported my views even in situations where they didn’t agree completely. The final edits with Wiley were even more intense, resulting in the reorganization of some chapters not to mention the positioning of the graphs and tables. Overall, however, this was a very rewarding experience. As a result, I have the utmost respect and admiration for the SAS Publications team. Without their help and encouragement this book would have never come to life.
Demand-driven forecasting is a new, structured approach to forecasting that focuses on analytics to sense, shape, and predict demand. Although it is new in design, the quantitative methods that support it have been around since the early 1900s. In addition, with recent improvements in data collection, storage, and processing technologies, it is now possible to implement demand-driven forecasting across thousands of products within a business hierarchy. Unfortunately, many companies still view quantitative forecasting methods as a black box, or unknown approach, that adds little value to improving overall demand forecast accuracy. Fortunately, there is a new awareness emerging across many industries due to the current economic times regarding the value of integrating demand data (point-of-sale and syndicated scanner data) into the demand forecasting process. Many industries are now looking for enabling solutions that can sense, shape, and predict demand using more sophisticated methods and tools. Industry leaders that have been striving toward demand-driven networks include consumer packaged goods, pharmaceuticals, automotive, and heavy manufacturing companies. The purpose of my book is to provide practitioners with a detailed blueprint and roadmap that will help them better understand this new structured approach as well as real-life examples to build a business case for the justification of demand-driven forecasting.
Although I have spent most of my career in the consumer packaged goods industry, I have found that a majority of the practical analytics described in this book are applicable across all industries. Some of my colleagues may not completely agree with such a structured approach, which puts so much emphasis on analytics rather than on what is referred to as the art of forecasting. Throughout my career, I have not been an advocate of judgment-based forecasting methods due to the inevitable political bias, which tends to add error rather than improve demand forecast accuracy. Therefore, many may view this book as implicitly biased toward forecasting situations in which data are plentiful and accessible. Although this may seem to be the case, given the current data collection capabilities and improvements in processing, it is no longer a legitimate reason to dismiss analytics in favor of judgment, particularly when judgment has such a poor track record when it comes to demand forecasting. Given this situation, I believe there is a need for a book that shares practical applications in quantitative analytics from a practitioner’s perspective.
The underlying message throughout Demand-Driven Forecasting is that the combination of analytics and domain knowledge in a structured framework in many cases adds significant improvement to demand forecast accuracy. I do not advocate more sophisticated analyses but rather applying the appropriate method, given the purpose and potential value to the overall corporate product portfolio. The book provides forecast practitioners with a basic understanding of the methods and processes required to implement a demand-driven forecasting process. My intent is to provide practitioners with a fundamental understanding of the quantitative methods used to sense, shape, and predict demand within a structured process. As practitioners, it is our responsibility to demonstrate the value of analytics to senior-level managers and gain their trust over time through performance metrics that link demand forecast accuracy to key performance indicators.
Lastly, I especially want to thank the SAS Business Enablement team and my manager, Mark Demers, who had to endure my many personality changes over the past year due to the late hours and weekends spent working on the book. They are the real heroes.
For more information about SAS Press author Charles Chase, visit his author page.