Demand-Driven Forecasting E-Book

Charles W. Chase

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: SAS Institute Inc
• Sprache: Englisch
• Veröffentlichungsjahr: 2013

Seitenzahl: 509

Beschreibung

An updated new edition of the comprehensive guide to better business forecasting Many companies still look at quantitative forecasting methods with suspicion, but a new awareness is emerging across many industries as more businesses and professionals recognize the value of integrating demand data (point-of-sale and syndicated scanner data) into the forecasting process. Demand-Driven Forecasting equips you with solutions that can sense, shape, and predict future demand using highly sophisticated methods and tools. From a review of the most basic forecasting methods to the most advanced and innovative techniques in use today, this guide explains Demand-Driven Forecasting, offering a fundamental understanding of the quantitative methods used to sense, shape, and predict future demand within a structured process. Offering a complete overview of the latest business forecasting concepts and applications, this revised Second Edition of Demand-Driven Forecasting is the perfect guide for professionals who need to improve the accuracy of their sales forecasts. * Completely updated to include the very latest concepts and methods in forecasting * Includes real case studies and examples, actual data, and graphical displays and tables to illustrate how effective implementation works * Ideal for CEOs, CFOs, CMOs, vice presidents of supply chain, vice presidents of demand forecasting and planning, directors of demand forecasting and planning, supply chain managers, demand planning managers, marketing analysts, forecasting analysts, financial managers, and any other professional who produces or contributes to forecasts Accurate forecasting is vital to success in today's challenging business climate. Demand-Driven Forecasting offers proven and effective insight on making sure your forecasts are right on the money.

Leseprobe

Contents

Cover

Series

Title Page

Copyright

Foreword

A JOURNEY DOWN MEMORY LANE

QUALIFIED TO DEFINE DEMAND-DRIVEN FORECASTING

PRIMER ON ADVANCED FORECASTING

Preface

NEW IN THIS EDITION

UNIQUE FEATURES

Acknowledgments

About the Author

Chapter 1: Demystifying Forecasting: Myths versus Reality

DATA COLLECTION, STORAGE, AND PROCESSING REALITY

ART-OF-FORECASTING MYTH

END-CAP DISPLAY DILEMMA

REALITY OF JUDGMENTAL OVERRIDES

OVEN CLEANER CONNECTION

MORE IS NOT NECESSARILY BETTER

REALITY OF UNCONSTRAINED FORECASTS, CONSTRAINED FORECASTS, AND PLANS

NORTHEAST REGIONAL SALES COMPOSITE FORECAST

HOLD-AND-ROLL MYTH

THE PLAN THAT WAS NOT GOOD ENOUGH

PACKAGE TO ORDER VERSUS MAKE TO ORDER

“DO YOU WANT FRIES WITH THAT?”

SUMMARY

NOTES

Chapter 2: What Is Demand-Driven Forecasting?

TRANSITIONING FROM TRADITIONAL DEMAND FORECASTING

WHAT'S WRONG WITH THE DEMAND-GENERATION PICTURE?

FUNDAMENTAL FLAW WITH TRADITIONAL DEMAND GENERATION

RELYING SOLELY ON A SUPPLY-DRIVEN STRATEGY IS NOT THE SOLUTION

WHAT IS DEMAND-DRIVEN FORECASTING?

WHAT IS DEMAND SENSING AND SHAPING?

CHANGING THE DEMAND MANAGEMENT PROCESS IS ESSENTIAL

COMMUNICATION IS KEY

MEASURING DEMAND MANAGEMENT SUCCESS

BENEFITS OF A DEMAND-DRIVEN FORECASTING PROCESS

KEY STEPS TO IMPROVE THE DEMAND MANAGEMENT PROCESS

WHY HAVEN'T COMPANIES EMBRACED THE CONCEPT OF DEMAND-DRIVEN?

SUMMARY

NOTES

Chapter 3: Overview of Forecasting Methods

UNDERLYING METHODOLOGY

DIFFERENT CATEGORIES OF METHODS

HOW PREDICTABLE IS THE FUTURE?

SOME CAUSES OF FORECAST ERROR

SEGMENTING YOUR PRODUCTS TO CHOOSE THE APPROPRIATE FORECASTING METHOD

SUMMARY

NOTE

Chapter 4: Measuring Forecast Performance

“WE OVERACHIEVED OUR FORECAST, SO LET'S PARTY!”

PURPOSES FOR MEASURING FORECASTING PERFORMANCE

STANDARD STATISTICAL ERROR TERMS

SPECIFIC MEASURES OF FORECAST ERROR

OUT-OF-SAMPLE MEASUREMENT

FORECAST VALUE ADDED

SUMMARY

NOTES

Chapter 5: Quantitative Forecasting Methods Using Time Series Data

UNDERSTANDING THE MODEL-FITTING PROCESS

INTRODUCTION TO QUANTITATIVE TIME SERIES METHODS

QUANTITATIVE TIME SERIES METHODS

MOVING AVERAGING

EXPONENTIAL SMOOTHING

SINGLE EXPONENTIAL SMOOTHING

HOLT'S TWO-PARAMETER METHOD

HOLT'S-WINTERS' METHOD

WINTERS' ADDITIVE SEASONALITY

SUMMARY

NOTES

Chapter 6: Regression Analysis

REGRESSION METHODS

SIMPLE REGRESSION

CORRELATION COEFFICIENT

COEFFICIENT OF DETERMINATION

MULTIPLE REGRESSION

DATA VISUALIZATION USING SCATTER PLOTS AND LINE GRAPHS

CORRELATION MATRIX

MULTICOLLINEARITY

ANALYSIS OF VARIANCE

F-TEST

ADJUSTED R2

PARAMETER COEFFICIENTS

t-TEST

P-VALUES

VARIANCE INFLATION FACTOR

DURBIN-WATSON STATISTIC

INTERVENTION VARIABLES (OR DUMMY VARIABLES)

REGRESSION MODEL RESULTS

KEY ACTIVITIES IN BUILDING A MULTIPLE REGRESSION MODEL

CAUTIONS ABOUT REGRESSION MODELS

SUMMARY

NOTES

Chapter 7: ARIMA Models

PHASE 1: IDENTIFYING THE TENTATIVE MODEL

PHASE 2: ESTIMATING AND DIAGNOSING THE MODEL PARAMETER COEFFICIENTS

PHASE 3: CREATING A FORECAST

SEASONAL ARIMA MODELS

BOX-JENKINS OVERVIEW

EXTENDING ARIMA MODELS TO INCLUDE EXPLANATORY VARIABLES

TRANSFER FUNCTIONS

NUMERATORS AND DENOMINATORS

RATIONAL TRANSFER FUNCTIONS

ARIMA MODEL RESULTS

SUMMARY

NOTES

Chapter 8: Weighted Combined Forecasting Methods

WHAT IS WEIGHTED COMBINED FORECASTING?

DEVELOPING A VARIANCE WEIGHTED COMBINED FORECAST

GUIDELINES FOR THE USE OF WEIGHTED COMBINED FORECASTS

SUMMARY

NOTES

Chapter 9: Sensing, Shaping, and Linking Demand to Supply: A Case Study Using MTCA

LINKING DEMAND TO SUPPLY USING MULTI-TIERED CAUSAL ANALYSIS

CASE STUDY: THE CARBONATED SOFT DRINK STORY

SUMMARY

APPENDIX 9A CONSUMER PACKAGED GOODS TERMINOLOGY

APPENDIX 9B ADSTOCK TRANSFORMATIONS FOR ADVERTISING GRP/TRPs

NOTES

Chapter 10: New Product Forecasting: Using Structured Judgment

DIFFERENCES BETWEEN EVOLUTIONARY AND REVOLUTIONARY NEW PRODUCTS

GENERAL FEELING ABOUT NEW PRODUCT FORECASTING

NEW PRODUCT FORECASTING OVERVIEW

WHAT IS A CANDIDATE PRODUCT?

NEW PRODUCT FORECASTING PROCESS

STRUCTURED JUDGMENT ANALYSIS

STRUCTURED PROCESS STEPS

STATISTICAL FILTER STEP

MODEL STEP

FORECAST STEP

SUMMARY

NOTES

Chapter 11: Strategic Value Assessment: Assessing the Readiness of Your Demand Forecasting Process

STRATEGIC VALUE ASSESSMENT FRAMEWORK

STRATEGIC VALUE ASSESSMENT PROCESS

SVA CASE STUDY: XYZ COMPANY

SUMMARY

SUGGESTED READING

NOTES

Index

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 and SAS Business Series include:

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Business Analytics for Managers: Taking Business Intelligence beyond Reporting by Gert Laursen and Jesper Thorlund

The Business Forecasting Deal: Exposing Bad Practices and Providing Practical Solutions by Michael Gilliland

Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy by Olivia Parr Rud

CIO Best Practices: Enabling Strategic Value with Information Technology, Second Edition by Joe Stenzel

Connecting Organizational Silos: Taking Knowledge Flow Management to the Next Level with Social Media by Frank Leistner

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Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring by Naeem Siddiqi

The Data Asset: How Smart Companies Govern Their Data for Business Success by Tony Fisher

Delivering Business Analytics: Practical Guidelines for Best Practice by Evan Stubbs

Demand-Driven Forecasting: A Structured Approach to Forecasting by Charles Chase

Demand-Driven Inventory Optimization and Replenishment by Robert A. Davis

The Executive's Guide to Enterprise Social Media Strategy: How Social Networks Are Radically Transforming Your Business by David Thomas and Mike Barlow

Executive's Guide to Solvency II by David Buckham, Jason Wahl, and Stuart Rose

Fair Lending Compliance: Intelligence and Implications for Credit Risk Management by Clark R. Abrahams and Mingyuan Zhang

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Human Capital Analytics: How to Harness the Potential of Your Organization's Greatest Asset by Gene Pease, Boyce Byerly, and Jac Fitz-enz

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Manufacturing Best Practices: Optimizing Productivity and Product Quality by Bobby Hull

Marketing Automation: Practical Steps to More Effective Direct Marketing by Jeff LeSueur

Mastering Organizational Knowledge Flow: How to Make Knowledge Sharing Work by Frank Leistner

The New Know: Innovation Powered by Analytics by Thornton May

Performance Management: Integrating Strategy Execution, Methodologies, Risk, and Analytics by Gary Cokins

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

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 Value of Business Analytics: Identifying the Path to Profitability by Evan Stubbs

Visual Six Sigma: Making Data Analysis Lean by Ian Cox, Marie A. Gaudard, Philip J. Ramsey, Mia L. Stephens, and Leo Wright

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.

Cover image: © iStockphoto.com/Olena_TCover design: Wiley

Copyright © 2013 by SAS Institute, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

First Edition: Demand-Driven Forecasting: A Structured Approach to Forecasting, ISBN: 9780470415023. Published by John Wiley & Sons, Inc. Copyright © 2009.

Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data

Chase, Charles. Demand-driven forecasting : a structured approach to forecasting / Charles W. Chase, Jr.—Second edition. pages cm.—(Wiley & SAS business series) Includes index. ISBN 978-1-118-66939-6 (cloth); ISBN 978-1-118-73564-0 (ebk); ISBN 978-1-118-73557-2 (ebk) 1. Economic forecasting 2. Business forecasting. 3. Forecasting. I. Title. HB3730.C48 2013 330.01'12—dc23 2013015670

Foreword

Demand-Driven Forecasting was a long-overdue practical book on business forecasting when it was first published in 2009. One of the industry's top business forecasters, Charles W. Chase (also known as Charlie in the industry) has done a remarkable job in revising this second edition. I have been involved in business forecasting—the demand forecasting that is done by industry forecasters—for over 30 years and have not seen a better reference book for them. The content Charlie has added has made the revision much more relevant and useful. This is especially true as companies are doing more demand shaping during the turbulent economic times since the Great Recession. All businesses are keen to understand what is driving their volatile demand as well as to leverage demand shaping to compete and thrive in tough economic climates.

A JOURNEY DOWN MEMORY LANE

Business forecasting during my early years was largely based on the exponential smoothing forecasting methods developed by industry practitioner Robert G. (Bob) Brown, who published several books starting in the late 1950s. These exponential smoothing methods live on today and are often the under-the-hood statistical forecasting engines powering many software packages. Forecasting methods have evolved since that time to include a wide variety of statistical time series methods, many of which were discussed in several revisions of forecasting books written by two leading academic forecasters, Spyros Makridakis and Steven C. Wheelwright, starting in the late 1970s.

During the first half of my career, advanced methods focused on what might be termed history-driven forecasting, because the methods involved analyzing years of historical data in order to identify recurring patterns from which to project the future. Midway in my career, the focus started changing toward demand-driven forecasting.

The last several decades or so have been a period of increased consumerism, especially in the United States. During this time marketing and sales organizations developed more sophisticated and effective demand-shaping techniques to simulate demand for the products they were promoting. Industry forecasters, by necessity, started to experiment with and utilize methods that no longer assumed that demand just magically happened and could be estimated only from understanding what happened in the past. They started leveraging cause-and-effect methods, such as multiple regression methods, and time series methods incorporating causal factors, such as autoregressive integrated moving average (ARIMA) models with explanatory variables in order to reflect the fact that promotional activities would shape and create demand and therefore needed to be understood and incorporated into a forecast. (To reflect their importance, this second edition places increased emphasis on regression and ARIMA methods.)

Coincidently, midway in my career I was fortunate enough to meet Charlie Chase, who was already a pioneer in demand-driven forecasting. I was researching ways to do promotional forecasting for a consulting engagement that I was working on for a large drugstore chain. Professionally it was a watershed event for me, as my advocacy moved from largely espousing history-driven forecasting to including advanced demand-driven forecasting.

I had heard that Charlie had successfully implemented multivariate statistical methods to incorporate the effects of promotions at Polaroid, where he was employed at the time. Our consulting team visited him and learned a lot about how to use these sophisticated methods (which we all learned about in a college classroom) in a real-world setting. From that day on our relationship has blossomed, and we have become close colleagues and friends. We have shared a variety of ideas over time, such as multi-tiered forecasting concepts. Charlie introduced me to the Institute of Business Forecasting & Planning (IBF), an organization whose mission he was helping to recast at the time. From those efforts, the IBF has successfully evolved into the preeminent organization for “practical” business forecasters and planners.

QUALIFIED TO DEFINE DEMAND-DRIVEN FORECASTING

Charlie Chase is one of the top thought leaders in the business forecasting community, which makes him eminently qualified to write (what I consider to be) the definitive book on demand-driven forecasting. He bears many scars from the battles it took to get this type of forecasting implemented at a variety of consumer products companies, where the shaping of demand is critical to long-term market success. At these companies, running promotional and advertising campaigns, continually altering prices, and launching new products is a way of life. Thus, not only has Charlie had to leverage the forecasting methods learned in the classroom, but he has also had to develop innovative yet practical methods while in the heat of the battle at these dynamic companies.

This is why I believed the demand-driven concepts discussed in the original book were immediately applicable to business forecasters working in product industries as well as to those working at service-oriented and public sector organizations. In the original book, however, new product forecasting (a key element of demand shaping) was covered in a cursory fashion. This second edition has a completely new chapter on this topic and gives new product forecasting the appropriate due that it deserves.

With the rise in consumerism during the past 20 years or so, a business forecaster's job has become much more difficult. The dramatic growth in the entities that need to be forecast by multinational organizations has made demand forecasting methods and systems larger in scale. Business planning has become more complex in terms of having to deal with the myriad of products being sold—many with short life cycles (e.g., proliferation of stock-keeping units)—as well as the number of countries into which they are sold and the number of channels they are sold through. Technology has been evolving to keep up with this dramatic growth in scale, and Charlie has played an influential role in this area as well. His discussion of forecasting technology in this book comes from a wealth of experience in helping to develop and implement sophisticated forecasting systems enabled by leading-edge technology.

PRIMER ON ADVANCED FORECASTING

When I first reviewed a draft of the original book, my initial reaction was that it represented a primer on advanced forecasting. My second reaction was: Is that statement an oxymoron? It isn't, because Demand-Driven Forecasting takes the reader on a journey from the basic methods espoused by forecasting pioneer Bob Brown over 50 years ago to some of the most innovative business forecasting methods in use today.

After clearly defining demand-driven forecasting, the original and this second edition take the reader from a review of the most basic forecasting methods, to the most advanced time series methods, and then on to the most innovative techniques in use today, such as the linking of supply and demand to support multi-tiered forecasting and the incorporation of downstream demand signals. As Bob Brown's books turbocharged the evolution of history-driven forecasting, Charlie's books do the same for demand-driven forecasting. To the readers of this edition: Enjoy reading it and be prepared to become even more demand driven.

Lawrence “Larry” Lapide, Ph.D. Research Affiliate, MIT Center for Transportation & Logistics Lecturer, University of Massachusetts Recipient of the inaugural Lifetime Achievement in Business Forecasting & Planning award from the Institute of Business Forecasting & Planning

Preface

The global marketplace continues to be volatile, fragmented, and dynamic. Supply processes continue to mature faster than demand. As a result, there is a larger gap to fill in the redefinition of demand forecasting processes to become demand driven than in any other area of the supply chain. This redefinition of demand forecasting will require new data (downstream point-of-sales [POS] data), processes, analytics, and enabling technologies. To become demand driven, companies need to identify the right market signals, build demand-sensing capabilities, define demand-shaping processes, and effectively translate demand signals to create a more effective response.

The second edition of the book focuses on the continued evolution of demand-driven forecasting and addresses the challenges companies are experiencing with demand. Those challenges are making it more difficult to get demand right than to get supply right. Talent continues to be scarce, making it difficult to invest in enabling technologies to support the evolving demand forecasting process. Organizationally, the work on demand forecasting processes is fraught with political issues. This makes it more politically charged than supply processes. As a result, many companies often want to throw in the towel. They want to forget about demand and focus only on the redesign of supply processes to become more reliable, resilient, and agile. The list of possible projects is long and often includes lean manufacturing, cycle time reduction, order management, and the redefinition of distribution center flows. However, focusing only on supply has shown limited results.

Supply-centric approaches to resolving demand challenges can increase the complexity. They cannot improve the potential of the supply chain as a complex system. Working supply processes in isolation from demand only drives up costs, increases working capital, and reduces asset utilization. The secret to building demand excellence is to build the right stuff in the demand management processes. Improvements in demand forecasting have proven to enhance the supply chain by providing the right foundation to make effective trade-offs against the supply chain.

The development of a demand forecasting strategy is easier said than done. Demand management systems were designed for the supply chains of the 1990s, when there was less complexity. Over the past decade, supply chains have become more complex because of consolidation through acquisition and globalization. Unfortunately, the evolution of demand forecasting practices has not kept pace with business needs.

Historic approaches to demand forecasting emphasized planning rather than analytics, making it difficult to meet the task of creating a more accurate demand response. As a result, companies are coming to realize that the demand forecasting process requires a complete reengineering with an outward-in orientation supported by data and analytics. The process needs to focus on identifying market opportunities (market signals) and leveraging internal sales and marketing programs to influence customers to purchase the company's products and services. It requires a champion—an organizational leader—to orchestrate the change management requirements of the demand-driven process. This gap in demand forecasting will be the area of highest priority for supply chain leaders in the coming years.

The preparation of this second edition, like the first, is based on the author's view that the book should do six things:

1. Cover the full range of challenges regarding becoming demand driven, including process, statistical methods, performance metrics, and enabling technology.

2. Provide a complete description of the essential characteristics of demand-driven process.

3. Present the steps needed for the practical application of statistical methods.

4. Provide a practical framework rather than focusing on the unnecessary theoretical details that are not essential to understanding how to apply the methods.

5. Provide a step-by-step structured approach to applying the various statistical methods, demonstrating their advantages and disadvantages, so the reader can choose the most appropriate method for each forecasting situation.

6. Cover the most comprehensive set of statistical forecasting methods and approaches to demand forecasting.

NEW IN THIS EDITION

While meeting these criteria, this second edition includes major revisions for Chapters 1, 2, 5, 6, and 8 plus three completely new chapters. The purpose has not been merely to revise this edition but to rewrite it to include the latest theoretical developments and practical challenges while presenting the most recent empirical findings, thinking, and enabling technology advancements. Some of the new material covered includes:

New case studies and examples.A completely new Chapter 2 on demand-driven forecasting outlining new definitions and the addition of demand shifting.Additional definitions and examples illustrating the application of additive and multiplicative winters' methods.An expanded regression chapter.A completely new and expanded autoregressive integrated moving average (ARIMA) chapter covering nonseasonal and seasonal ARIMA models, transfer functions, and cross-correlation function plots.A revised weighted combined modeling chapter.A completely new Chapter 10 on new product forecasting using structured judgment.

UNIQUE FEATURES

The book is distinctive for its attention to practical demand forecasting challenges and its comprehensive coverage of both statistical methods and how to apply those statistical methods in practice within a demand-driven forecasting process using real data and examples. In particular:

There are many real data examples and a number of examples based on the author's experience. All the data sets in the book are from actual events but have been masked to protect confidentiality.There is emphasis on using graphical methods and plots to understand the analysis and statistical output.The author's perspective is that demand forecasting is much more than just fitting models to historical demand data. Although explaining and understanding the past history of demand is important, it is not adequate for accurately predicting future demand.Combining data, analytics, and domain knowledge is the only formula for successful demand forecasting.The second edition includes the most recent developments in demand-driven forecasting and implementation.

Acknowledgments

A number of friends and colleagues, influential to my career, have reviewed both editions of the manuscript and provided constructive recommendations. The continued support from my manager, Mark Demers, SAS Institute Inc., encouraged me to rewrite the book.

I also want to thank Stacey Hamilton, my SAS editor; Mike Gilliland, SAS Institute Inc.; and Dr. Aric Labarr at North Carolina State University for their help with the editing of this manuscript. Their input and suggestions have enhanced the quality of the book. Finally, I thank my wife, Cheryl, again for keeping the faith all these years and supporting my career. Without her support and encouragement, I would not have been in a position to write this book.

Charles W. Chase Jr. Chief Industry Consultant & Subject Matter Expert SAS Institute, Inc.

About the Author

Charles Chase is Chief Industry Consultant in the Manufacturing and Supply Chain Global Practice at SAS. He is the primary architect and strategist for delivering demand planning and forecasting solutions to improve supply chain efficiencies for SAS customers. He has more than 26 years of experience in the consumer packaged goods industry and is an expert in sales forecasting, market response modeling, econometrics, and supply chain management. Prior to working at SAS, Chase led the strategic marketing activities in support of the launch of SAS Forecast Server, which won the “Trend-Setting Product of the Year” award for 2005 by KM World magazine, and SAS Demand-Driven Forecasting. He has also been involved in the reengineering, design, and implementation of three forecasting and marketing intelligence processes/systems. Chase has also worked at the Mennen Company, Johnson & Johnson, Consumer Products Inc., Reckitt Benckiser, the Polaroid Corporation, Coca-Cola, Wyeth-Ayerst Pharmaceuticals, and Heineken USA.

Chase is former associate editor of the Journal of Business Forecasting and is currently an active member of the Practitioner Advisory Board for Foresight: The International Journal of Applied Forecasting. He has authored several articles on sales forecasting and market response modeling. He was named “2004 Pro to Know” in the February/March 2004 issue of Supply and Demand Chain Executive Magazine. He is also the coauthor of Bricks Matter: The Role of Supply Chains in Building Market-Driven Differentiation (Wiley, 2012).

CHAPTER 1

Demystifying Forecasting: Myths versus Reality

It has been an exciting time for the field of demand forecasting. All the elements are in place to support demand forecasting from a fact-based perspective. Advanced analytics has been around for well over 100 years and data collection has improved significantly over the past decade, and finally data storage and processing capabilities have caught up. It is not uncommon for companies' data warehouses to capture and store terabits of information on a daily basis, and parallel processing and grid processing have become common practices. With improvements in data storage and processing over the past decade, demand forecasting is now poised to take center stage to drive real value within the supply chain.