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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Serie: SAS Institute Inc
- Sprache: Englisch
- Veröffentlichungsjahr: 2016
A practical framework for revenue-boosting supply chain management Next Generation Demand Management is a guidebook to Next Generation Demand Management, with an implementation framework that improves revenue forecasts and enhances profitability. This proven approach is structured around the four key catalysts of an efficient planning strategy: people, processes, analytics, and technology. The discussion covers the changes in behavior, skills, and integrated processes that are required for proper implementation, as well as the descriptive and predictive analytics tools and skills that make the process sustainable. Corporate culture changes require a shift in leadership focus, and this guide describes the necessary "champion" with the authority to drive adoption and stress accountability while focusing on customer excellence. Real world examples with actual data illustrate important concepts alongside case studies highlighting best-in-class as well as startup approaches. Reliable forecasts are the primary product of demand planning, a multi-step operational supply chain management process that is increasingly seen as a survival tactic in the changing marketplace. This book provides a practical framework for efficient implementation, and complete guidance toward the supplementary changes required to reap the full benefit. * Learn the key principles of demand driven planning * Implement new behaviors, skills, and processes * Adopt scalable technology and analytics capabilities * Align inventory with demand, and increase channel profitability Whether your company is a large multinational or an early startup, your revenue predictions are only as strong as your supply chain management system. Implementing a proven, more structured process can be the catalyst your company needs to overcome that one lingering obstacle between forecast and goal. Next Generation Demand Management gives you the framework for building the foundation of your growth.
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Table of Contents
Foreword
Preface
Changing Influences
Automated Consumer Engagement
New World Order
Game Changer
Acknowledgments
About the Author
Chapter 1: The Current State
Why Demand Management Matters More Than Ever
Current Challenges and Opportunities
Primary Obstacles to Achieving Demand Management Planning Goals
Why Do Companies Continue to Dismiss the Value of Demand Management?
Summary
Key Learnings
Notes
Further Reading
Chapter 2: The Journey
Starting the Supply Chain Journey
Introducing Sales & Operations Planning (S&OP) into the Supply Chain Journey
Sales & Operations Planning Connection
Transitioning to a Demand-Driven Supply Chain
The Digitalization of the Supply Chain
Leveraging New Scalable Technology
Benefits
Summary
Key Learnings
Notes
Chapter 3: The Data
What Is Big Data?
Why Is Downstream Data Important?
Demand Management Data Challenges
CPG Company Case Study
Does Demand History Really Need to Be Cleansed?
How Much Data Should Be Used?
Demand-Signal Repositories
What Is Demand Signal Analytics?
Demand Signal Analytics Key Benefits
Summary
Key Learnings
Notes
Further Reading
Chapter 4: The Process
Centers of Forecasting Excellence
Demand Management Champion
Demand-Driven Planning
What Is Demand Sensing and Shaping?
A New Paradigm Shift
Large-Scale Automatic Hierarchical Forecasting
Transactional Data
Time Series Data
Forecasting Models
Skill Requirements
Summary
Key Learnings
Further Reading
Chapter 5: Performance Metrics
Why MAPE Is Not Always the Best Metric
Why In-Sample/Out-of-Sample Measurement Is So Important
Forecastability
Forecast Value Added
H
o
: Your Forecasting Process Has No Effect
Summary
Key Learnings
Notes
Further Reading
Chapter 6: The Analytics
Underlying Fundaments of Statistical Models
How Predictable Is the Future?
Importance of Segmentation of Your Products
Consumption-Based Modeling
Consumption-Based Modeling Using Multi-Tiered Causal Analysis
Consumption-Based Modeling Case Study
Summary
Key Learnings
Notes
Further Reading
Chapter 7: The Demand Planning Brief
Demand Planning Brief
Overview
Background
Recommended Forecast Methodology
Model Hierarchy
Model Selection Criteria
Supporting Information
Analytic Snapshot
Model Selection and Interpretation
Scenario Analysis
Summary
Key Learnings
Chapter 8: The Strategic Roadmap
Current State versus Future State
Current State
Future State
Gaps and Interdependencies
Strategic Roadmap
Summary
Index
End User License Agreement
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Guide
Table of Contents
Begin Reading
List of Illustrations
Chapter 1: The Current State
Figure 1.1 People, process, analytics, and technology required for adoption and sustainability.
Figure 1.2 Current use of forecasting methodologies and tools.
Figure 1.3 Demand data used for forecasting and planning.
Figure 1.4 Number of people reviewing, adjusting, and approving demand forecasts.
Figure 1.5 Comparing upper/lower forecast ranges for different forecasting methods.
Chapter 2: The Journey
Figure 2.1 The supply chain journey started by becoming supply-driven.
Figure 2.2 Sales & operations planning was introduced to improved synchronization.
Figure 2.3 Traditional supply chain equation.
Figure 2.4 New supply chain equation.
Figure 2.5 The new SM&OP/F process goals, purpose, and needs.
Figure 2.6 Companies are moving to become demand-driven.
Figure 2.7 Key components of the demand-driven planning process.
Figure 2.8 The next stage in the supply chain journey is becoming digital driven.
Figure 2.9 2015 CGT Report: Forecasting leads analytics benefits.
Figure 2.10 Potential benefits of more accurate demand forecasts.
Chapter 3: The Data
Figure 3.1 Top four elements of supply chain management pain for respondent.
Figure 3.2 Supply chain focal points for next two years.
Figure 3.3 Demand information currently used for forecasting and planning.
Figure 3.4 Consumer demand (POS/syndicated scanner data) versus supply (shipments).
Figure 3.5 Proof-of-value results using uncleansed data: holistic modeling.
Figure 3.6 Before and after data cleansing.
Figure 3.7 Holistic model using an ARIMAX model.
Figure 3.8 Demand signal analytics combining descriptive and predictive analytics.
Chapter 4: The Process
Figure 4.1 Demand-driven forecasting and planning process.
Figure 4.2 Demand sensing and shaping workflow.
Figure 4.3 Demand-driven collaborative workflow.
Figure 4.4 Most companies review their forecasts in a product hierarchy.
Figure 4.5 Globalization has made product hierarchies more complex.
Chapter 5: Performance Metrics
Figure 5.1 In-sample/out-of-sample test for forecast accuracy.
Figure 5.2 Coefficient of variation comparisons.
Chapter 6: The Analytics
Figure 6.1 Three key times series components and unexplained randomness (or irregular component).
Figure 6.2 Four segmentation quadrants using product portfolio management principles.
Figure 6.3 Plotting statistical methods based on segmentation and portfolio management principles.
Figure 6.4 Percentage of products that fall in each quadrant.
Figure 6.5 Product hierarchy.
Figure 6.6 CPG product-level consumption model fit with forecast versus shipments.
Figure 6.7 CPG product-level shipment model fit with forecast.
Chapter 7: The Demand Planning Brief
Figure 7.1 Demand forecast results at the top company level.
Figure 7.2 Wine sales by region.
Figure 7.3 Base price for wine types: value priced higher than red/white.
Figure 7.4 Demand forecasting and planning process flow.
Figure 7.5 Breakdown of the data series.
Figure 7.6 Autocorrelation panel for value wines in Western region.
Figure 7.7 Parameter estimates for overall model.
Figure 7.8 Nationwide sales projections for 2015, with and without a price increase.
Chapter 8: The Strategic Roadmap
Figure 8.1 Strategic roadmap from current state to future state.
List of Tables
Chapter 5: Performance Metrics
Table 5.1 Example of SKU Demand Metrics for a Large Company
Table 5.2 In-Sample/Out-of-Sample Weekly Actuals versus Forecast
Table 5.3 Performance Metrics Comparisons
Table 5.4 An Example of an FVA Report
Table 5.5 Which Demand Forecasting Is More Accurate?
Table 5.6 Performance Metrics Comparisons
Chapter 6: The Analytics
Table 6.1 CPG Product Level Consumption Model Fit
Table 6.2 CPG Product Level Shipment Model Fit
Chapter 7: The Demand Planning Brief
Table 7.1 Correlation between Sales and Price for Each Region/Wine Type
Table 7.2 System-Generated Hierarchical Forecasts with Corresponding Error Percentages
Table 7.3 Final Model Fit Statistics
Table 7.4 Projected Change Due to 5% Price Increase
Table 7.5 West Region Projected Change in Sales Volume & Income by Type
Table 7.6 P&L Analysis Assumptions
Table 7.7 What-If Analysis for 5% Price Increase in Region 2
Table 7.8 Estimated Change in Sales due to Promotions
Chapter 8: The Strategic Roadmap
Table 8.1 Strategic Roadmap from Current State to Future State
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:
Agile by Design: An Implementation Guide to Analytic Lifecycle Management by Rachel Alt-Simmons
Analytics in a Big Data World: The Essential Guide to Data Science and Its Applications by Bart Baesens
Bank Fraud: Using Technology to Combat Losses by Revathi Subramanian
Big Data, Big Innovation: Enabling Competitive Differentiation through Business Analytics by Evan Stubbs
Business Forecasting: Practical Problems and Solutions edited byMichael Gilliland, Len Tashman, and Udo Sglavo
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
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
Financial Institution Advantage and the Optimization of Information Processing by Sean C. Keenan
Financial Risk Management: Applications in Market, Credit, Asset, and Liability Management and Firmwide Risk by Jimmy Skoglund andWei Chen
Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection by Bart Baesens, Veronique Van Vlasselaer, and Wouter Verbeke
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 ofWhat Influences Our Analytical World by Carlos Andre, Reis Pinheiro, and Fiona McNeill
Hotel Pricing in a Social World: Driving Value in the Digital Economy by Kelly McGuire
Implement, Improve and Expand Your Statewide Longitudinal Data System: Creating a Culture of Data in Education by Jamie McQuiggan and Armistead Sapp
Killer Analytics: Top 20Metrics Missing from Your Balance Sheet by Mark Brown
Mobile Learning: A Handbook for Developers, Educators, and Learners by Scott McQuiggan, Lucy Kosturko, Jamie McQuiggan, and Jennifer Sabourin
The Patient Revolution: How Big Data and Analytics Are Transforming the Healthcare Experience by Krisa Tailor
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
Statistical Thinking: Improving Business Performance, Second Edition by Roger W. Hoerl and Ronald D. Snee
Too Big to Ignore: The Business Case for Big Data by Phil Simon
Trade-Based Money Laundering: The Next Frontier in International Money Laundering Enforcement by John Cassara
The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions by Phil Simon
Understanding the Predictive Analytics Lifecycle by Al Cordoba
Unleashing Your Inner Leader: An Executive Coach Tells All by Vickie Bevenour
Using Big Data Analytics: Turning Big Data into Big Money by Jared Dean
Visual Six Sigma, Second Edition by Ian Cox, Marie Gaudard, and Mia Stephens
For more information on any of the above titles, please visit www.wiley.com.
Next Generation Demand Management
People, Process, Analytics, and Technology
Charles W. Chase
Copyright © 2016 by SAS Institute, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the Web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions.
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To my wife, Cheryl, who has always been an inspiration and supporter of my career and written work.
Foreword
Cuneyt ErogluAssistant Professor
Supply Chain and Information Management Group,D'Amore-McKim School of Business,Northeastern University
Demand management is one of those essential business functions that every business professional has to know at least a little about regardless of whether they work in marketing, production, finance, or human resources. Sales forecasting and demand management form the foundation for all planning processes. Demand management is also one of those areas that companies continue to struggle with. No matter how good a company is in demand management, there still appears to be more room for improvement. When it comes to demand management, the journey to excellence seems to be an endless one.
In this book, Charlie Chase takes the reader on a journey to excellence in demand management. As a thought leader in this area, he provides a comprehensive and expertly written treatment of demand management for guiding business professionals. He first explains why demand management can be such a challenge for many companies. He then provides a sound framework on which companies can structure or redesign their demand management practices. He addresses many critical issues that are ignored in other books on this topic. He writes about and addresses, among others things, what kind of skills people in demand management should have, what kind of organization is needed for demand management, how to make sense of predictive and descriptive analytics, and how to take advantage of big data and new technologies.
Unlike many other books on the same topic, Charlie provides a big-picture approach to demand management. Based on many years of experience, he integrates strategic, tactical, as well as operational aspects of demand management. Consequently, this book contains useful insights for everyone from an analyst to a manager to a senior executive. Yet as Charlie writes about the different aspects of demand management, he also provides a unifying vision: The goal of demand management is not to increase forecast accuracy; it is to foster sales growth. This is a truly unique and insightful observation.
As my undergraduate students would say, Charlie “keeps it real.” He carefully chooses the most applicable parts of the theory that have direct applications in the real world of demand management. Readers can directly apply what they learn from this book. The concepts are illustrated by relevant real-life examples, which make them so much easier to understand and apply. Charlie is also careful in avoiding unnecessary theoretical details that are not applicable or that readers can learn on their own. The practical approach taken in this book makes it an excellent choice for practicing managers.
Before I finish, I must say something about SAS. I admit that I am biased in favor of SAS as I have been using it over two decades. As a company, SAS has been on the forefront of demand management not only because of its technological capabilities but also because it chose to integrate business insights of thought leaders like Charlie in their software. This makes SAS the ideal platform for the readers to implement the framework that Charlie has built based on his extensive experience in demand management over many years. In summary, this book is a gem for all who are interested in demand management, including the students in my future forecasting classes.
Preface
In today's volatile market, businesses are urgently seeking new ways to protect themselves and keep profit margins strong. External market factors are creating challenges, and manufacturers, perhaps more than most, are suffering from the consequences of that ripple effect. According to analysts' research, one of the highest-ranking challenges faced by CFOs is generating revenue growth and growing profit margin, yet CFOs believe it's not the right time to increase risk. As a result, companies are challenged with striking a fine balance between delivering growth while minimizing risk.
Meanwhile, as companies continue to strive to maintain market share and grow revenue it ultimately lies in the hands of the C-level and senior management teams to generate profitable growth across all levels of the business. Importantly, that includes organizations that manage the supply chain. There is a shift in focus influencing how companies are managing the supply chain, which is not simply about how supply drives demand, but how demand drives supply. It has been proven time after time that better predicting of the impact of demand on the supply chain increases revenues by at least 3 to 7 percent, and a third of companies could increase it by 6 percent or more.
For the entire business to become more demand-driven, it must secure better control over data and the ability to turn it into actionable insights. To gain a competitive edge requires a change in operational processes because companies are so used to forecasting supply rather than demand. Sales and operations planning processes are a focus, but becoming demand-driven requires a broader shift in the business model. It also requires a radical change in the corporate culture, people skills, horizontal processes, predictive analytics, and scalable technology. The entire company needs to become demand centric, and better equipped to influence and anticipate what consumers are going to purchase before they know what they're going to purchase.
CHANGING INFLUENCES
There are a number of internal and external factors that are shifting companies toward demand-driven business models. It's essential that business leaders recognize the impact of these factors on their business, and act on them.
Today, the traditional top-down approach to supply chain is no longer applicable. Companies have gone through a process where margins have been compromised by changing retailer and consumer purchasing patterns. When retailers started to reduce stock levels and consumers had a tendency to stockpile products, manufacturers responded by creating more product categories in a bid to increase profit margins. The result, product proliferation on shelf, expanded buffer inventories and wasted working capital. Yet forecasts are still based on an inventory or replenishment response.
There is a more fluid distribution of goods today because customer purchase behavior has changed the way products are created and sold. The rise of the Omni-channel and new purchasing processes such as Amazon.com make inventory management more unpredictable. The Omni-channel also increases the influence of external factors like social media, tweeter, and mobile devices which make it more challenging for distributors and retailers to plan deliveries and stock orders. Regardless, same day or next day delivery is an expectation that manufacturers and the supply chain process are tasked to support. These factors are making demand more volatile, and as a result manufacturers can no longer operate using inventory buffer stock to protect against demand volatility as it can too easily result in lost profit.
AUTOMATED CONSUMER ENGAGEMENT
The definition of fast for consumers today is dramatically different from the fast of 5 to 10 years ago. Consumers are demanding more, and expect it quicker than ever before. This is being driven by the Millennials, as they want instant response and same-day delivery. Consumer demand is no longer driven by supply availability, but instead, companies must shift their operational models by listening to demand and responding to consumer pull in order to remain successful. A supply push strategy is no longer viable in today's digit world.
Using sales and marketing tactics and a consumer-centric approach, companies are now pulling demand through the channels of distribution. To do so, sales and marketing tactics have to be more focused on the automated consumer engagement (experience). The influence of unstructured data and social media are having a more prevalent impact than ever before on the entire purchase process, which must be factored into the demand management process. This is the result of the openness and availability of consumer feedback that social media influences and delivers. Feedback via social media is both a gift and a detriment for retailers, distributors, and manufacturers. Although it provides insight into sentiment and provides opportunity for brand exposure, it adds additional complexity to how consumer pull can be influenced. It also means demand can be influenced across multiple channels and, more often than not, with very immediate consequences.
Demand is also changing because customers want to consume products in new ways. Subscription lifestyles and shared economies due to the on-demand world have impacted how companies need to plan, design, and create products for an indecisive generation of consumers. The consumer experience must remain at the forefront of retailer and manufacturer priorities. Flexibility, efficiency, and a consumer-centric approach will be the key to their success.
An increasing percentage of revenue will come from new product lines increasing product life cycles, which are getting shorter. Also, levels of stock-keeping units (SKUs) are escalating. This challenges companies to create faster delivery systems for more products, making the supply chain even more complex. In addition, the rise of online shopping and same-day delivery has resulted in consumers expecting quicker turnaround from retailers and the manufacturers that support them. 3D printing at home is representative of this ever-increasing phenomenon. In the near future, consumers who want a product now may well create it themselves. Companies, particularly manufacturers, will be competing with a very short-lived product life cycle. Business leaders will need to adapt their business models in order to cope with more frequent peaks or troughs in consumer demand. This has to be achieved in a sustainable way and without negative impact on revenue and profit.
NEW WORLD ORDER
Business leaders need to adapt their business models for today's demand-driven supply chain. Big data analytics allows a more accurate demand forecasting and planning process to improve production and shipments. To be successful, companies must redefine their supply chain definition to include the commercial side of the business.
The shift to the next generation demand management will only be achieved through better use of data, the implementation of horizontal processes, and more emphasis on predictive analytics. Subsequently, there needs to more importance on consumption-based modeling using a process called multi-tiered causal analysis (MTCA), which combines downstream data with upstream data and applies in-depth predictive analytics to:
Measure the impact of marketing programs on consumer demand at retail
Link retail demand to shipments from manufacturers to retailers
Enable manufacturers to perform what-if analyses to shape future demand and help them choose the optimal sales and marketing strategy for producing the highest volume and return on investment (ROI)
Consumption-based modeling is an approach that links a series of quantitative methods to measure the impact of marketing programming and business strategies that influence downstream consumer demand (demand sensing). Then, creating what-if scenarios to shape and predict future demand (demand shaping) using point of sale (POS) and/or syndicated scanner data. Finally, using consumer demand history and the future-shaped consumer demand forecast as a leading indicator in a supply model to enhance supply volumes (shipments and sales orders) using predictive analytics rather than judgment.
Once MTCA measures the KPIs (key performance indicators) that influence consumer demand, the demand analyst can model and perform what-if simulations to predict and shape future demand, developing short- and long-term forecasts. These simulations capture real-world scenarios and show what happens in different situations. The demand analyst can simulate the impact of changes on key variables that can be controlled (e.g., price, advertising, in-store merchandising, and sales promotions), predict demand, and choose the optimal strategy for producing the highest volume and ROI.
Through this process, leaders can predict how market influences or changes will impact their supply chain, which allows them to formalize ways in which the business can accurately learn through the increasing automated consumer engagement process. It will require more anticipatory predictive analytics to ensure that the right amount of products in the right product mix make it to the shelves and into consumers' hands. The sheer size makes demand forecasting and planning on a global scale highly complex. Product categories, sales regions, and an abundance of participating internal organizations combine to weave a tangled corporate web. “To have the right quantity of the right products at the right place and time,” companies will rely heavily on the combination of transactional data and digital information to anticipate and influence what consumers will purchase. The overarching goal is to be able to “take proactive measures instead of simply reacting” through strong horizontal alignment processes, stronger collaboration with key accounts (customers), and the use of predictive analytics supported by scalable technology.
GAME CHANGER
To make the shift to the next generation demand management, leaders need to bring together different aspects of the organization to make informed decisions based on a holistic view of available data. Previously, the technology available to companies did not facilitate the integration of data, nor facilitate predictive analytics. This is especially true for the sales, marketing & operations planning organizations. They will all be required to source and share data on a continual basis and learn from not only the shared knowledge collected from across the company, but from information collected digitally by sensors, as a result of Internet of Things (IoT). This is why the corporate culture is crucial to the success of this new demand management model. The culture requires an atmosphere of horizontal collaboration, trust of predictive analytics, and scalable technology in order to ensure all the ingredients are in place. Similarly, organizations need to be ready to work quickly with minimal latency to act on the trends and insights produced. Failure to do so risks a reactive culture prevailing.
There needs to be people with the appropriate skills to provide advice to drive the process with the right domain expertise to make more informed fact-based decisions to support business strategies. There is also a broader requirement for those involved to better understand how supply chains are managed under the new demand management model. For example, making sure demand and supply data are not confusing, but, rather, integrated—working in lock-step to deliver value to consumers and customers. Finally, sales and marketing organizations will need a new way to source and organize information in order to feed into the new generation demand management model. The frequency and the way in which the company collects data will require changes, as well.
Like all change management, transitioning to the next generation demand management model while working in a volatile marketplace is a journey that requires time and does not happen overnight. Data and predictive analytics provide the insights and quantify the challenges a company is facing, but it is business leaders who see the bigger picture, realize the urgency and are not afraid to tackle changes, and the frequency of recurring common problems. So to make informed decisions on how to reorganize and resource the business will require leaders, not followers.
The myriad forces impacting the relationship between demand and supply are set to expand their influence. Finding ways to be better prepared means implementing a corporate culture and structure that brings together organizations, and most of all, data from different sources. The analytics and technology capability is now available, so organizational changes and skills must be the focus to transition to the next generation demand management. However, it will also require ongoing change management to not only gain adoption but sustainability that will eventually become the new corporate culture.
Acknowledgments
A number of friends and colleagues over the course of my career have been influential in my success as a thought leader and trusted adviser. Their continued support and encouragement made it possible to write this book.
I also want to thank my SAS editor Stacey Hamilton and publication advisor Lou Metzger for their continued support and help with the editing and distribution of this book. Their encouragement to write the first book ultimately led me to write this third book. Their input and suggestions have enhanced the quality of the book.
Most of all, I want to thank my wife, Cheryl, 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. ChaseAdvisory Industry Consultant and Trusted AdviserSAS Institute, Inc.
About the Author
As Advisory Industry Consultant and Consumer Packaged Goods (CPG) Team Lead for the Global Retail/CPG Industry Practice at SAS Institute, Charles Chase is a thought leader and trusted adviser for delivering demand-driven solutions to improve SAS customers' supply chain efficiencies. Chase has more than 20 years of experience in the CPG industry and is an expert in demand forecasting and planning, market response modeling, econometrics, and supply chain management.
Prior to working as Advisory Industry Consultant, 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. Chase launched the SAS Demand-Driven Planning and Optimization Solution in 2008, which is being used by more than 100 large corporations globally. He has also been involved in the reengineering, design, and implementation of three forecasting/marketing intelligence process/systems. He has previously worked for the Mennen Company, Johnson & Johnson, Consumer Products, Reckitt & Benckiser, the Polaroid Corporation, Coca Cola, Wyeth-Ayerst Pharmaceuticals, and Heineken USA.
Chase's authority in the area of forecasting/modeling and advanced marketing analytics is further exemplified by his prior posts as president of the International Association of Business Forecasting, associate editor of the Journal of Business Forecasting, and chairperson of the Institute of Business Forecasting (IBF) Best Practices Conferences. Chase currently writes a quarterly column in the Journal of Business Forecasting titled “Innovations in Business Forecasting.” He also served as a member of the Practitioner Advisory Board for Foresight: The International Journal of Applied Forecasting.
In 2013, Chase won the Institute of Business Forecasting Lifetime Achievement Award, and the following year he was certified in professional forecasting by the Institute of Business Forecasting. In 2004, he was named Pro to Know by Supply and Demand Chain Executive magazine. He is the author of Demand-Driven Forecasting: A Structured Approach to Forecasting, which is now in its second edition (Hoboken, NJ: John Wiley & Sons, 2013), and, with Lora Cecere, Bricks Matter: The Role of Supply Chains in Building Market-Driven Differentiation (Hoboken, NJ: John Wiley & Sons, 2013). He served as an adjunct instructor in the Masters of Science in Analytics program at North Carolina State University in 2012–2013.
Chapter 1The Current State
Today's business challenges are numerous due to globalization pressures, supply chain complexity, rising customer demands, and the need to increase revenues across global markets while continuing to cut costs. Adding to these challenges is the current economy in which the last several years supply has outstripped demand. Intense market volatility and fragmentation are compelling companies to develop and deploy more integrated, focused, demand-driven processes and technologies to achieve best-in-class performance. As a result, there have been major shifts in demand management.
Unfortunately, there has been more discussion than actual adoption, and where adoption has occurred, there has been little if any sustainability. Demand-driven processes are challenging and more difficult to get right than supply, and they tend to be politically charged. Furthermore, implementing a demand-driven process in support of a new-generation demand management process requires investment in people, process, analytics, and technology. Adoption requires an executive champion who has the influence to change corporate behavior, encourage new analytics skills (descriptive and predictive), and integrate processes horizontally utilizing new scalable technology. Strategic intent and interdependencies play a key role in maintaining long-term sustainability. Without sustainability, the adoption of new conceptual designs like demand-driven tends to fail over the long-term. In most cases manufacturers lack the necessary analytical skills, horizontal processes, and scalable technologies needed to capitalize on big data and digitally collected information. After all, it's not just about process anymore.
As shown in Figure 1.1 investment in people, process, analytics, and technology requires a champion not only to facilitate adoption but also for sustainability purposes. Sustainability can only occur if the strategic intent and business interdependencies are horizontally aligned and supported by scalable technology.
Figure 1.1 People, process, analytics, and technology required for adoption and sustainability.
Companies are realizing that moving to the next generation demand management will require a laser focus on four key areas:
Investing in their people's skills, which requires change in behavior
Reorganization around horizontal processes
Integrating predictive as well as descriptive analytics into the process
Investing in large-scale automatic forecasting technology
These four areas are the key catalysts to move from the current state to the future state, along with good metrics to measure progress. Although adoption requires changes in people behaviors that include new skills and horizontal processes, it will also require more focus on predictive analytics supported by large-scale technology that can adapt and scale to big data. It requires changes in corporate culture led by a champion who has the authority and leadership to not only drive adoption, but also create a new corporate culture that stresses accountability with a focus on customer excellence. Finally, sustainability can only occur if the strategic “intent and business interdependencies” are horizontally aligned and supported by scalable technology.
In many cases, companies get adoption, but once the champion moves on to a new project, the process participants tend to go back to the old process, stop investing in new skills, bypass the analytics, and create Excel workaround programs to avoid using the technology. This suboptimizes the process and technology, not to mention creates poor results. In other words, the intent becomes self-serving to all people and all things—except for the right thing, generating revenue and profit. We have become so immersed in achieving low MAPEs (mean absolute percentage error) that we have lost the original intent of the process.
Before a company invests in people, process, analytics, and technology, they need to define their true intent. We all know through experience that the one number forecast does not work. It might work in theory, but not in practice. Plus, only a handful of companies are forecasting true demand (e.g., POS and/or syndicated scanner data). Most companies are forecasting the supply replenishment signal (sales orders), and/or the supply response (shipments). Finally, most demand planners really don't do forecasting. They manage data and information. This is another reason why more and more companies are looking to hire demand analytics and data scientists who have strong statistical skills. The key word is intent. Is your demand management process intended to create accurate forecasts (lower MAPEs) to reduce inventory costs or to provide business decision support to grow revenue and profitability?
WHY DEMAND MANAGEMENT MATTERS MORE THAN EVER
Demand management concepts are now 20 to 25 years old. The first use of the term demand management surfaced in the commercial sector in the late 1980s or early 1990s. Previously, the focus was on a more siloed approach to demand forecasting and planning that was manual, using very simple statistical techniques like moving averaging and simple exponential smoothing, and then, Excel, and a whole lot of gut-feeling judgment. Sound familiar? In the mid-1990s, demand planning and supply planning were lumped together, which gave birth to supply chain management concepts of demand planning and integrated supply chain planning.
Most supply chain professionals are quickly realizing that their supply chain planning solutions have not driven down costs, and have not reduced inventories or speed to market. Companies globally across all industry verticals have actually moved backward over the course of the last 10 years when it comes to growth, operating margin, and inventory turns. In some cases, they have improved days payable, but this has pushed costs and working capital responsibility backward in the supply chain, moving the costs to the suppliers. To make matters worse, Excel is still the most widely used demand forecasting and planning technology in the face of significant improvements in data collection, storage, processing, analytics, and scalability.
According to a 2014 Industry Week report (see Figure 1.2), moving averaging has now become the preferred statistical model of choice for forecasting demand, digressing from Holt-Winters Three Parameter Exponential Smoothing based on studies conducted by the Institute of Business Forecasting in the late 1990s. Furthermore, with all the advancements in analytics and technology, there has been minimal investment in the analytic skills of demand planners. To make matters worse, downstream data—with all the improvements with data collection, minimal latency in delivery, and increased coverage across channels—is still being used in pockets across sales and marketing, rather than the entire supply chain, for demand forecasting and planning.
Figure 1.2 Current use of forecasting methodologies and tools.
Companies are quickly learning that in order to move forward, they need to admit their bad practices of the past. They must be willing to risk failure in order to move forward. Leaders must confront a number of mistakes made in the design of their demand management processes over the course of the last decade. The mistakes are many, but all can be corrected with changes to the process, use of downstream data, and most all, the inclusion of analytics. Here are a number of good intentions with poor execution that have caused companies to make key mistakes in demand management.
The One-Number Forecast
Well-intentioned academics and consultants tout the concept of one-number forecasting. Enthusiastic supply chain executives have drunk the Kool-Aid, as they say. But, the reality is, it does not reduce latency and it is too simplistic. In other words, it is conceptually appealing, but not practical in execution.
The sole concept of a one-number demand forecast is that if everyone is focused on one number, the probability of achieving the number is great. As a result, the concept adds unintentional, and in many cases, intentional bias, adding error to the demand plan. It is too simplistic; all the participants have different purposes, or intentions.
I ask supply chain managers, “What is the purpose of your forecasting process?” They say, “To create an accurate demand forecast.” I respond, “What is the true purpose of their demand forecasting and planning process? Is it to create a financial plan, set sales targets, or create a shipment forecast?” They pause, and say, “All the above.” I say, “All the above are plans, not an unconstrained consumer demand forecast.”
There is only one true forecast—the unconstrained demand forecast, or as close as possible to “unconstrained,” given the inherent constraints, whether self-inflicted or customer specific. There is no such thing as a shipment forecast, financial forecast, or sales forecast. They are all plans created from the unconstrained consumer demand forecast. Furthermore, most consensus forecasts are a blend of different plans and financial targets. The people who push the one-number concept really do not understand demand forecasting and planning. An unconstrained consumer demand forecast is used to build a demand plan, financial plan, sales plan, marketing plan, and operations plan. Each plan has a different intent, or purpose, and as such, will be different. There are many separate activities including workflow that require different skills (people), process, analytics, and technology capabilities.
A demand forecast is hierarchical around products, time, geographies, channels, and attributes. It is a complex set of role-based time-phased data. As a result, a one-number thought process is naïve. An effective demand forecast has many numbers that are tied together in an effective data model for role-based planning and what-if analysis. Even the eventual demand plan is sometimes not reflective of the original unconstrained demand forecast due to capacity constraints, which results in demand shifting to accommodate supply lead times and materials availability. In fact, most companies who describe demand shaping during interviews with supply chain executives actually describe demand shifting, not true demand shaping. A one-number plan is too constraining for the organization. A demand plan is a series of time-phased plans carefully architected in a data model of products, calendars, channels, and regions. The numbers within the plans have different purposes (intents) to different individuals within the organization.
So, instead of a one-number demand plan, the focus needs to be a common set of plans for marketing, sales, finance, and operations planning (supply plan) with different plan views based on an agreement of market assumptions and one unconstrained consumer demand response. This requires the use of an advanced enterprise demand forecasting and planning solution with the design of the system to create a true demand response and visualize role-based planning views. The legacy systems implemented over the past decade were not designed to accommodate different plan views based on an unconstrained consumer demand response.
