Consumption-Based Forecasting and Planning - Charles W. Chase - E-Book

Consumption-Based Forecasting and Planning E-Book

Charles W. Chase

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Discover a new, demand-centric framework for forecasting and demand planning In Consumption-Based Forecasting and Planning, thought leader and forecasting expert Charles W. Chase delivers a practical and novel approach to retail and consumer goods companies demand planning process. The author demonstrates why a demand-centric approach relying on point-of-sale and syndicated scanner data is necessary for success in the new digital economy. The book showcases short- and mid-term demand sensing and focuses on disruptions to the marketplace caused by the digital economy and COVID-19. You'll also learn: * How to improve demand forecasting and planning accuracy, reduce inventory costs, and minimize waste and stock-outs * What is driving shifting consumer demand patterns, including factors like price, promotions, in-store merchandising, and unplanned and unexpected events * How to apply analytics and machine learning to your forecasting challenges using proven approaches and tactics described throughout the book via several case studies. Perfect for executives, directors, and managers at retailers, consumer products companies, and other manufacturers, Consumption-Based Forecasting and Planning will also earn a place in the libraries of sales, marketing, supply chain, and finance professionals seeking to sharpen their understanding of how to predict future consumer demand.

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Table of Contents

Cover

Title Page

Copyright

Foreword

Preface

WHY IS THIS IMPORTANT?

TRACKING SHIFTING CONSUMER DEMAND PATTERNS

Acknowledgments

About the Author

Chapter 1: The Digital Economy and Unexpected Disruptions

DISRUPTIONS DRIVING COMPLEX CONSUMER DYNAMICS

IMPACT OF THE DIGITAL ECONOMY

WHAT DOES ALL THIS MEAN?

SHIFTING TO A CONSUMER-CENTRIC APPROACH

THE ANALYTICS GAP

WHY PREDICTIVE AND ANTICIPATORY ANALYTICS?

DIFFERENCE BETWEEN PREDICTIVE AND ANTICIPATORY ANALYTICS

THE DATA GAP

THE IMPACT OF THE COVID-19 CRISIS ON DEMAND PLANNING

CLOSING THOUGHTS

NOTES

Chapter 2: A Wake-up Call for Demand Management

DEMAND UNCERTAINTY IS DRIVING CHANGE

CHALLENGES CREATED BY DEMAND UNCERTAINTY

ONGOING “BULLWHIP” EFFECT

WHEN WILL WE LEARN FROM OUR PAST MISTAKES?

WHY ARE COMPANIES STILL CLEANSING HISTORICAL DEMAND?

CONSUMER GOODS COMPANY CASE STUDY

PRIMARY OBSTACLES TO ACHIEVING PLANNING GOALS

WHY DO COMPANIES CONTINUE TO DISMISS THE VALUE OF DEMAND MANAGEMENT?

SIX STEPS TO PREDICTING SHIFTING CONSUMER DEMAND PATTERNS

CLOSING THOUGHTS

NOTES

Chapter 3: Why Data and Analytics Are Important

ANALYTICS MATURITY

COLLECTING AND STORING CONSUMER DATA

BUILDING TRUST IN THE DATA

AI/MACHINE LEARNING CREATES TRUST CHALLENGES

PURSUIT OF EXPLAINABILITY

HOW MUCH DATA SHOULD BE USED?

WHAT ARE USERS LOOKING TO GAIN?

CLOSING THOUGHTS

NOTES

Chapter 4: Consumption-Based Forecasting and Planning

A CHANGE OF MINDSET IS REQUIRED

WHY CONSUMPTION-BASED FORECASTING AND PLANNING?

WHAT IS CONSUMPTION-BASED FORECASTING AND PLANNING?

CONSUMPTION-BASED FORECASTING AND PLANNING CASE STUDY

CONSUMPTION-BASED FORECASTING AND PLANNING SIX-STEP PROCESS

UNDERSTANDING THE RELATIONSHIP BETWEEN DEMAND AND SUPPLY

WHY MOVE DEMAND PLANNING DOWNSTREAM CLOSER TO THE CONSUMER?

THE INTEGRATED BUSINESS PLANNING CONNECTION

DEMAND MANAGEMENT CHAMPION

CLOSING THOUGHTS

NOTES

Chapter 5: AI/Machine Learning Is Disrupting Demand Forecasting

STRAIGHT TALK ABOUT FORECASTING AND MACHINE LEARNING

WHAT IS THE DIFFERENCE BETWEEN EXPERT SYSTEMS AND MACHINE LEARNING?

DO MACHINE LEARNING ALGORITHMS OUTPERFORM TRADITIONAL FORECASTING METHODS?

M4 COMPETITION

M5 COMPETITION

BASIC KNOWLEDGE REGARDING NEURAL NETWORKS

WHY COMBINE ML MODELS?

CHALLENGES USING MACHINE LEARNING MODELS

DATA CHALLENGES AND CONSIDERATIONS

CASE STUDY 1

CASE STUDY 2: USING ADVANCED ANALYTICS TO ADAPT TO CHANGING CONSUMER DEMAND PATTERNS

CLOSING THOUGHTS

NOTES

Chapter 6: Intelligent Automation Is Disrupting Demand Planning

WHAT IS “INTELLIGENT AUTOMATION”?

HOW CAN INTELLIGENT AUTOMATION ENHANCE EXISTING PROCESSES?

WHAT IS FORECAST VALUE ADD?

CASE STUDY: USING INTELLIGENT AUTOMATION TO IMPROVE DEMAND PLANNERS‘ FVA

CLOSING THOUGHTS

NOTES

Chapter 7: The Future Is Cloud Analytics and Analytics at the Edge

WHY CLOUD ANALYTICS?

WHAT ARE THE DIFFERENCES BETWEEN CONTAINERS AND VIRTUAL MACHINES?

WHY CLOUD ANALYTICS?

PREDICTIVE ANALYTICS ARE CREATING IT DISRUPTIONS

DATA IS INFLUENCING SOFTWARE DEVELOPMENT

WHY CLOUD-NATIVE SOLUTIONS?

WHY DOES ALL THIS MATTER?

CLOUD-NATIVE FORECASTING AND PLANNING SOLUTIONS

WHY MOVE TO A CLOUD-NATIVE DEMAND PLANNING PLATFORM?

WHY “ANALYTICS AT THE EDGE”?

EDGE ANALYTICS BENEFITS

EDGE ANALYTICS LIMITATIONS

FORECASTING AT THE EDGE

CLOUD ANALYTICS VERSUS EDGE ANALYTICS

CLOSING THOUGHTS

NOTES

Index

End User License Agreement

List of Tables

Chapter 4

Table 4.1 Consumption Model Analytic Results

Table 4.2 Supply (Shipments) Model Analytic Results

Chapter 5

Table 5.1 Summary of Weekly Forecasting Accuracy at {Prod} and {Prod–ShipLoc} Le...

Table 5.2 Summary of Enhanced Daily Forecast Accuracy and Bias at Prod and Prod–...

Chapter 6

Table 6.1 Performance Metrics Comparisons

Table 6.2 An Example of an FVA Report

Table 6.3 Which Demand Forecasting Is More Accurate?

Table 6.4 Performance Metrics Comparisons with Naïve Forecast

List of Illustrations

Chapter 1

Figure 1.1 Top 5 Analysis Areas of Focus

Figure 1.2 Retail and Consumer Goods Companies Analytics Maturity

Figure 1.3 Pandemic Four Phases and Demand Shifts

Chapter 2

Figure 2.1 The Bullwhip Effect

Figure 2.2 Before Data Cleansing

Figure 2.3 After Data Cleansing

Figure 2.4 Holistic Model Using an ARIMAX Model

Figure 2.5 Proof-of-Value Results Using Uncleansed Data: Holistic Modeling

Figure 2.6 Comparing Upper/Lower Forecast Ranges for Different Forecasting Met...

Chapter 3

Figure 3.1 Analytics Maturity Model

Figure 3.2 Functions and Processes Most Impacted by Advanced Analytics

Figure 3.3 Types of Data Being Purchased for Internal Use

Figure 3.4 Leveraging Weather Data

Figure 3.5 Shipments (Supply) Versus Syndicated Scanner Data (Demand)

Figure 3.6 Shipments (Supply) Versus POS Data (Demand)

Figure 3.7 Consumption-Based Analytics Combining Descriptive and Predictive An...

Chapter 4

Figure 4.1 Forecasts That Fall in the 90% Upper/Lower Confidence Range

Figure 4.2 Large-scale Hierarchical Forecasting with Automatic Reconciliation

Figure 4.3 Consumer Demand (D) Forecasting Graphical Model Fit and Forecast

Figure 4.4 Marketing Planner Workbook

Figure 4.5 Marketing Planner Promotion Simulation Results

Figure 4.6 Demand/Supply Planner Workbook

Figure 4.7 Demand Planner Supply Simulation Results

Figure 4.8 Supply (S) Forecasting Graphical Model Fit and Forecast

Figure 4.9 Canned Food Product Demand and Supply Comparison

Figure 4.10 Canned Food Product Demand and Supply Comparison-Demand Pulled For...

Figure 4.11 Consumption-based Forecasting and Planning Process

Figure 4.12 Consumption-Based Forecasting and Planning Demand Sensing Workflow

Figure 4.13 Consumption-Based Process Collaborative Planning Workflow

Figure 4.14 Key Components of the Consumption-Based Planning Process

Chapter 5

Figure 5.1 M5 Competition Retail Business Hierarchy

Figure 5.2 Neural Network Architecture

Figure 5.3 ML Ensemble Architecture

Figure 5.4 Graphical Representation of the Provided Data

Figure 5.5 Neural Network + Time Series High Level Process Overview

Figure 5.6 Process Methodology for Converting Weekly Forecasts to Daily Foreca...

Figure 5.7 Comparison of Forecast Accuracy Using Three Forecasts at (a) {Prod}...

Figure 5.8 Comparison of Forecast Bias Using Three Forecasts at Prod–ShipLoc L...

Figure 5.9 Enhanced Customer Weekly Forecast Accuracy Using POS & Customer Tra...

Figure 5.10 Daily Forecasting Accuracy at Prod and Prod–ShipLoc Level (a) and ...

Figure 5.11 Daily Forecasting Accuracy at Prod and Prod–ShipLoc Level Versus a...

Figure 5.12 Daily Forecasting Bias at Prod and Prod–ShipLoc Level Versus a Naï...

Figure 5.13 Process Flow and Data Used to Model Shifting Short-term Demand Pat...

Figure 5.14 Uncovering Insights from the Data Using Advanced Analytics

Figure 5.15 Forecast Results as Additional Causal Factors Are Added to the Mod...

Figure 5.16 Short-term End-to-End Interactive COVID-19 Forecasting Dashboard

Chapter 6

Figure 6.1 Demand Planner Weekly Activities

Figure 6.2 Override Only If the Forecast Can Be Improved

Figure 6.3 Intelligent Automation Process Approach

Figure 6.4 User Override Interface

Chapter 7

Figure 7.1 VMs Versus Container Architectures

Figure 7.2 Edge Computing and Analytics Architecture

Guide

Cover Page

Title Page

Copyright

Foreword

Preface

Acknowledgments

About the Author

Table of Contents

Begin Reading

Index

WILEY END USER LICENSE AGREEMENT

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Consumption-Based Forecasting and Planning

Predicting Changing Demand Patterns in the New Digital Economy

 

 

Charles W. Chase

 

 

 

 

Copyright © 2021 by SAS Institute Inc. All rights reserved.

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

Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data

Names: Chase, Charles, author. | John Wiley & Sons, publisher.

Title: Consumption-based forecasting and planning : predicting changing demand patterns in the new digital economy / Charles W. Chase. Other titles: Wiley and SAS business series

Description: Hoboken, New Jersey : John Wiley & Sons, Inc., [2021] | Series: Wiley and SAS business series | Includes index.

Identifiers: LCCN 2021020635 (print) | LCCN 2021020636 (ebook) | ISBN 9781119809869 (cloth) | ISBN 9781119809883 (adobe pdf) | ISBN 9781119809876 (epub)

Subjects: LCSH: Business forecasting. | Business logistics. | Demand (Economic theory).

Classification: LCC HD30.27 .C473 2021 (print) | LCC HD30.27 (ebook) | DDC 658.4/0355—dc23

LC record available at https://lccn.loc.gov/2021020635

LC ebook record available at https://lccn.loc.gov/2021020636

Cover image: © Radoslav Zilinsky/Getty Images

Cover design: Wiley

Foreword

I have the honor of writing the foreword to Charles Chase's new book Consumption-Based Forecasting and Planning: Predicting Shifting Demand Patterns in the New Digital Economy. I have known Mr. Chase (“Charlie”) for roughly 35 years. Charlie and I have common interests in business forecasting and market analytics. In addition, Charlie and I are close friends, and he and his wife Cheryl are adopted members of my immediate family.

The purpose of a foreword is to confer credibility to the author(s) and to provide context and background of the book in question. Let's start with credibility. Charles Chase is unquestionably a leader in forecasting/modeling and advanced marketing analytics. Currently employed at SAS Institute, Inc., he is the author of Next Generation Demand Management: People, Process, Analytics, and Technology; Demand-Driven Forecasting: A Structured Approach to Forecasting; and coauthor of Bricks Matter: The Role of Supply Chains in Building Market-Driven Differentiation. Each of these books is required reading in my Business Forecasting PhD-level class at Texas A&M University. Moreover, Charlie has served as president of the International Association of Business Forecasting and currently writes a quarterly column in the Journal of Business Forecasting entitled “Innovations in Business Forecasting.”

Concerning the context of his new pithy tome, the book is divided into seven chapters: (1) The Digital Economy and Unexpected Disruptions; (2) A Wake-up Call for Demand Management; (3) Why Data and Analytics Are Important; (4) Consumption-Based Forecasting and Planning; (5) AI/Machine Learning Is Disrupting Demand Forecasting; (6) Intelligent Automation Is Disrupting Demand Planning; and (7) The Future Is Cloud Analytics and Analytics at the Edge. Targeting principally business executives, the main objective is to describe how the new digital economy and the disruption attributed to COVID-19 have changed the way companies deploy demand forecasting. As such, this book is very timely. Further, Chase argues for repositioning demand planning downstream in the supply chain closer to customers (ultimately consumers) to maximize sales. While not necessarily a novel concept, the emphasis on this repositioning is important operationally to firms, especially those engaged in the consumer-packaged goods industry. Additionally, the case is made for applying predictive analytics and machine learning to available data sources to ameliorate modeling efforts associated with customer demand patterns. Improvements in the ability to model demand lead to efficiencies, the reduction of costs and hence advances in the bottom line. Finally, a unique contribution of the book is the introduction of cloud analytic solutions and edge analytics, what Chase calls the future of demand forecasting and planning. On all fronts, Chase provides information on various key topics not presently evident in the extant literature.

Data are the lifeblood of the digital economy providing business insights and supporting real-time delivery of critical information to enable decision making. Massive amounts of data are routinely collected from sensors and devices operating in real-time from remote locations operating globally. As supply chain executives face the new digital economy, Chase argues that the appropriate vision for data and analytics is to harness relevant information not only to make better decisions but also to react faster to disruptions like the unprecedented COVID-19 pandemic. Chase states that “Intelligent automation supported by machine learning is changing the game, particularly for demand forecasting and planning.” Chase makes clear that “when shaping business plans and strategy, consumption-based forecasting and planning can serve as a great counterweight to gut feelings and biases.”

Given that demand forecasting and planning generally have been designated as the areas that likely will deliver the most benefits from predictive analytics, it is not unreasonable to assume that cloud computing would also be the preferred technology platform. As this technology continues to grow, Chase points out that there will be incessant debate surrounding the best approaches to utilizing cloud computing due to the demand for advanced analytics skills. Analytics at the edge is a technology-based approach to data collection and analysis where automated-analytical calculations are performed using sensors, network switches, or other devices instead of utilizing centralized data repositories.

In agreement with Chase, the virtual flood of data is changing the way businesses handle data storage, processing, and analytics. The traditional computing paradigm built on centralized data warehouses with conventional Internet connectivity is not well suited for dealing with huge volumes of data. Bandwidth limitations and unpredictable system disruptions all contribute to network bottlenecks. Chase notes that companies are responding to these data challenges related to the new digital economy by deploying edge computing applications. Chase opines that the cloud is a key component for a successful digital transformation. Further, he observes that open-source cloud solutions now allow companies to monitor consumer demand on a daily and/or weekly basis providing real-time updates regarding ever-changing consumer demand patterns based on current market conditions.

Not surprisingly, Chase astutely provides a concise and cogent blueprint for how business executives should deal with the digital economy and unexpected disruptions. Indeed, his contribution provides a wake-up call for demand management. Without question, Chase illustrates why data and analytics are important, challenging business operations to embrace consumption-based forecasting and planning. Additionally, Chase notes that artificial intelligence, machine learning, and automation are vital capabilities in the digital economy. This pronouncement is not only important to the business community but also to the academic community as well. Finally, Chase accurately surmises that applications of cloud analytics and analytics at the edge will grow in the future as businesses continue to grapple in a time-sensitive manner with the ever-present challenges of demand forecasting and planning. Simply put, business executives who read carefully and take copious notes of the concepts set forth in this book will have a decided advantage in coping with the full potential of the digital economy.

Dr. Oral “Jug” Capps, Jr.

Executive Professor and Regents Professor

Co-Director of the Agribusiness, Food, and Consumer Economics Research Center

Department of Agricultural Economics

Texas A&M University

Also Managing Partner and Co-Founder, Forecasting and Business Analytics, LLC

College Station, Texas

March 30, 2021

Preface

Retail and consumer goods executives know that when shaping business plans forecasts serve to temper and balance gut feelings and judgmental bias. Yet, most will admit that their forecasts are still disgracefully inaccurate. There are signs, however, based on early adoption of applying intelligent automation supported by machine learning and traditional predictive analytics that are changing the playing field, particularly for demand forecasting and planning. For example, a large global consumer goods company reduced its global days of finished goods inventory by 1.2 days after improving their overall forecast accuracy from 70% to 81% on average across their product portfolio. That corresponded to a 50 basis points improvement in overall customer service levels. So, you don't need to move the needle that much to gain significant improvements in overall supply chain performance.

The past year of the pandemic has highlighted that companies don't respond quickly to shifting consumer demand patterns, as well as other market disruptions. Companies were already facing many new challenges because of the new digital economy. The unforeseen disruption of COVID-19 worsened the economic uncertainty and market volatility. This perfect supply chain storm has become even more important for commercial teams to explore predictive analytics and automation. Those teams will need new systems to turbocharge their demand forecasting and planning capabilities to capture those shifting consumer demand patterns that are taking place as consumers move through the four phases of the pandemic—preliminary, outbreak, stabilization, and recovery. They will need efficient ways to generate and disseminate real-time consumer demand forecasts that reflect rapidly shifting market conditions. Likewise, it will be imperative for analysts and demand planning teams to embrace automated digital applications and dashboards to allow data to be refreshed frequently and incorporate multiple scenarios.

WHY IS THIS IMPORTANT?

We all know that not all forecasts will be 100% accurate, 100% of the time. That's also reflective of best-made plans and strategic initiatives. No statistical formula can predict the surge, outcome, or exact length of a black swan event like COVID-19—or can it? There's no data available for an unforeseen disruption—or is there? Nor will analytics generate optimal forecasts every time, maybe not when using traditional time series methods. In the wake of COVID-19, for example, retailers and consumer goods companies had to reset their traditional algorithms and data sets in an attempt to understand the effects of multiple phases of self-isolation, lockdowns, and reopenings over the past 10 months in an attempt to understand shifting consumption patterns.

The COVID-19 pandemic has disrupted the usual demand forecasting and planning processes. Consumer demand patterns for different products and services have shifted from the norm, given the uneven spread of the virus, and continuing economic and health uncertainties. Traditional statistical models that rely heavily only on shipments (supply) historical data alone were unable to capture the effects of the crisis for both current demand and into the next normal. However, some early adopter demand planning teams were using predictive analytics and were able to stress-test their demand forecasts and create “What If” scenarios. The technology allowed them to drill down on the impact of the crisis across specific product categories using different parameters. For instance, one consumer goods company used a combination of precrisis data, postcrisis assumptions across specific business drivers, and consumer-behavior research to model the shifting consumer demand patterns for their products across categories under various scenarios. One early finding showed that the next 1–8 weeks compound annual growth rate in the “pasta goods” category changed from a single-digit growth percent in a business-as-usual setting to a double-digit percent increase based on the scenarios. The behavior was linked to POS (point-of-sale), Google trends, epidemiological, stringency index, and regional economic data. By contrast, non-essential products were not influenced as much by the current situation, as demand remained unchanged across all scenarios and assumptions.

Once opportunities have been identified and benefits targeted, organizations implementing predictive analytics and machine learning on a large-scale basis must invest in the following core requirements:

Clean, quality, accessible data.

Perhaps more than other functional groups, the demand planning organization implementing or scaling up a predictive analytics process must ensure the reliability and accuracy of data. When business information isn't adequately sourced, aggregated, reconciled, or secured, demand analysts and planners spend more time on redundant tasks that don't add value. Business leaders must work with IT and the business to set the governance rules for data usage, what good data looks like, who owns the data, and who can access the data.

Organizational training, protocols, and structure.

Demand Planning, IT, and business leaders must ensure that employees at all levels are trained to understand the systems required to collect, access, and maintain the data. It doesn't matter how clean or how easy it is to access the data if the demand planning function doesn't have the right operational and organizational training and structure to implement predictive analytics programs. It needs supporting processes and protocols to gather insights from the data, share those insights, and develop action plans in unison across all the other functions.

Cultural challenges.

The executive team will also need to focus on corporate cultural challenges; for example, by highlighting “lighthouse cases” that might inspire other parts of the business to use predictive analytics. The company and demand planning team will most likely need to hire data scientists and data-visualization specialists. They will need to retrain internal demand planners to work with data scientists, as well. Otherwise, execution will stall, and in many cases, fail.

Process and model sustainability.

Analytics and machine learning models are never 100% stable over time, so they need to be adjusted continually, which strengthens the case for in-house competences. It is worth assembling a small hybrid group of data scientists and demand planners with strong business acumen to work together on special projects that make the case for deeper investments in analytics talent.

The importance of having a strategic vision.

The SVP supply chain, or CAO (Chief Analytics Officer), of companies must have a clear vision of how they will use new technologies. In my experience, CAOs are well positioned to provide that vision and to lead the widespread adoption of advanced analytics. They have most of the necessary data in hand, as well as the traditional quantitative expertise to assess the real value to be gained from analytics programs. Project teams and senior leaders may suspect that their companies could streamline processes or export products more efficiently. For example, the CAO can put these ideas in the proper context.

At investor days or in quarterly earnings reports, C-suite leaders tend to talk about analytics programs in broad terms. For instance, how they will change the industry, how the company will work with customers differently, or how digitization will affect the financials. In doing so, they can help fulfill the repeated request, from both senior management and the board, that they serve not only as traditional transaction managers but also as key strategy partners and as value managers. Of course, CAOs cannot lead digital transformations all alone; they should serve as global collaborators, encouraging everyone, including leaders in IT, sales, and marketing, to own the process. CAOs on the cutting edge of advanced analytics are positioning themselves not just as forward-thinking analytics leaders but also as valued business partners to other leaders in their companies. Those who aren't will need to think about how analytics programs could change the way they work, and then lead by example.

TRACKING SHIFTING CONSUMER DEMAND PATTERNS

Without a doubt, consumer behavior has changed several dimensions across product categories, channel selection, shopper trip frequency, brand preferences, and omnichannel consumption. These shifts, combined with projections for virus containment and economic recovery, are critical for retail and consumer goods strategies. Leading retail and consumer goods companies are using traditional predictive analytics and machine learning algorithms with multiple sources of insights including point-of-sale data, primary consumer research, social media, and online search trends to understand how consumer demand could evolve during and after the crisis at a granular level (SKU/Ship-to-location).

Leading executives are planning to rapidly adapt their sales and marketing plans to reflect changing consumption patterns as well as consumer sentiment. The overall consumer outlook seems to vary depending on the stage of the pandemic response, causing executives to adjust the intensity of their marketing, including ad copy and calls to action, and to stay in sync with the evolving situation. Changing consumer demand patterns for essential purchases and non-essentials is leading retail and consumer goods companies to consider shifting marketing spending in channels such as digital and social media. All these actions are beneficial due to real-time testing and measurement. Just because the crisis is unprecedented does not mean rapid analytic testing should be abandoned. Many companies are using it surgically to gather data regarding the effectiveness of ongoing marketing efforts and adjust promotional campaigns accordingly given the resulting insights.

Consumer goods companies can maximize the impact of their new demand plans by collaborating with key customers (retailers) to refine, deploy, and revamp commercial plans. Flexibility and compassion have been found to be important elements in this collaboration. The pandemic has changed the retail landscape, especially for smaller retail outlets that have been hit the hardest. Making daily calls and adjusting payment terms as needed are setting the right tone. Changes in sales techniques are being considered to adapt, as well. Companies are considering providing additional support and technologies to their sales force to improve virtual-selling techniques. Similarly, companies are reallocating field sales and brokerage resources to the channels, key customers, and geographies that are experiencing the highest demand. Retail and consumer goods companies who successfully execute these strategies will have a clear view of how the market will unfold and positioned to come out of the COVID-19 crisis ahead of their competitors. By contrast, those companies who wait until after the crisis to act on these opportunities will find themselves lagging their counterparts.

To be successful in the rapidly changing digital economy, companies need to properly tackle digital transformation. This is not possible if it's not part of their business agenda. The speed of digitalization will only continue to increase as consumers of demand forecasts throughout the business ecosystem mandate answers in real time. As more and more companies reinvent the way they do business, the efficiency of the digital economy will see its full potential.

This book describes the organizational, operational, and leadership requirements necessary to use predictive analytics and AI/machine learning technologies to generate more accurate consumption-based forecasts and demand plans. Some leading-edge companies are already well on their way in the digital journey, providing several case studies. Their stories and approaches will be a testament to the effectiveness of predictive analytics and machine learning providing a path forward for others.

Acknowledgments

Over the course of my career, I have had many chance meetings with others, several of whom have had a significant impact on my life, career, and this book. More than a few of those chance meetings lead to personal friendships that have spanned several decades.

First and foremost, I want to thank Dr. Oral Capps, executive professor and Regents Professor, department of Agricultural Economics at Texas A&M University, who has been a mentor and a best friend—not to mention being one of the smartest people that I have ever known. Dr. Chaman Jain, founder of the Institute of Business Forecasting (IBF), and chief editor of the Journal of Business Forecasting (JBF), who has given me a platform to write, speak, and share my experiences with others—to pay it forward.

A special thank-you to Albert Guffanti, VP/Group Publisher of Retail Information Systems (RIS News) and Consumer Goods Technology (CGT) at Ensemble IQ for allowing me to share several analytics research studies and reports that his team has created over the past six years. Also, I thank him for his support in providing another social media platform for me to publish and share my experiences and knowledge, to continue paying it forward.

For the past 18 years I have had the privilege of working at SAS Institute Inc., where I have been humbled by the sheer number of extremely smart people. Without their curiosity, passion, and innovative spirit, this book would not have been written. They have provided a steady flow of groundbreaking work in the field of statistical forecasting and machine learning that is reflected in this book. They include Roger Baldridge, James Ochiai-Brown, Brittany Bullard, Jessica Curtis, Sherrine Eid, Michael Gilliland, Sudeshna Guhaneogi, Pasi Helenius, Adam Hillman, Matteo Landrò, Valentina Larina, Kedar Prabhudesai, Varunraj Valsaraj, Dan Woo, and many others for their amazing work.

Most of all, I want to thank the best chance meeting of my life, my wife and best friend, Cheryl, for keeping the faith all these years and supporting my career. Without her support and encouragement, I would not have been able to write this book.

Charles Chase

Executive Industry Consultant and Trusted Advisor

Global Retail/Consumer Goods Practice

SAS Institute, Inc.

About the Author

Charles Chase is Executive Industry Consultant with SAS Global Retail/Consumer Goods Industry Practice. As executive industry consultant, Charles Chase is a thought leader and trusted adviser for delivering analytics-driven solutions to improve SAS customers supply chain efficiencies. Chase has more than 20 years of experience in the consumer goods industry, and is an expert in demand forecasting and planning, market response modeling, econometrics, and supply chain management.

Prior to working as executive 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 SAS Demand-Driven Planning and Optimization Solution in 2008, which is used by over 100 large corporations globally. He has also been involved in the re-engineering, design, and implementation of three forecasting/marketing intelligence process/systems. His employment history includes 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. He currently writes a quarterly column in the Journal of Business Forecasting. He also served as a member of the Practitioner Advisory Board for Foresight: The International Journal of Applied Forecasting.

He has been an invited guest lecturer at several well-known universities including the Fuqua School of Business, Duke University; the Sloan School of Management, MIT; North Carolina State University; Northeastern University; Agricultural School of Economics, Texas A&M; Institute of Retail Management, Templeton College, University of Oxford; William & Mary University; Wake Forest University; and Virginia Commonwealth University.

CHAPTER 1The Digital Economy and Unexpected Disruptions

We are experiencing unprecedented and unpredictable times where disruption has been felt globally by many companies, particularly retailers and consumer goods companies. The digital economy has had an impact on almost every aspect of our lives from banking and shopping to communication and learning. This incredible progress driven by digital technologies is affecting the world we live in by improving our lives, but also creating new challenges. The most successful organizations get ahead of an unpredictable future by being prepared for the unknown. There have been significant developments in the evolution of various disruptive technologies over the past two decades and this development brings new opportunities, both in terms of cost savings and overall value creation. The benefits of IoT, big data, advanced analytics, AI/machine learning, cloud computing, and other advanced technologies collectively can make an impact that companies can leverage to digitize their supply chains to address business challenges.

The world is changing at an accelerated pace and companies are seeing that the biggest benefits of digitization come from the ability to move faster, adapt quickly to disruptions, anticipate changes, and automatically execute information faster by managing large volumes of data more effectively—all resulting in speed of innovation and execution of those changes. As a result, companies are looking for real-time data collection across multiple media platforms that will provide actionable insights from the data to advanced analytics with easy-to-use user interfaces (UI). Additionally, these companies hope to remotely gather relevant information affecting day-to-day operations to monitor performance, make the right decisions at the right time, and improve the velocity of supply chain execution. Digital transformation will help companies establish that foundation by becoming more agile and flexible.

The consensus is that the overarching impact of digital transformation strategies and objectives will have significantly more influence than just cost savings. Companies are facing increased consumer demand for reasonably priced, high-quality products and cannot afford quality-related disruptions with their products and services. Visual depiction of a demand plan, graphical depictions of performance indicators, and better visibility of KPIs through dynamic searches and interactive dashboards and reports will enable seamless data discovery and visualization. Users need to easily compare multiple scenarios and visualize them fully for improved performance.

DISRUPTIONS DRIVING COMPLEX CONSUMER DYNAMICS

Over the past decade, consumers have been gaining power and control over the purchasing process. Unprecedented amounts of information and new digital technologies have enabled more consumer control, and now, instead of being in control, marketers have found themselves losing control. In the past several years, however, there's been a shift. Even as consumers continue to exert unprecedented control of purchasing decisions, power is swinging back toward marketers, with the help from technology and analytics that play a new and larger role in the decision-making process.

Consumers are turning increasingly to technology to help them make decisions. This has been enabled by four key disruptions.

Automated consumer engagement.

A shift from active engagement to “automated engagement” where technology takes over tasks from information gathering to actual execution.

Digital technologies.

An expanding IoT which embeds sensors almost anywhere to generate smart data regarding consumer preferences triggering actions offered by marketers.

Predictive analytics.

Improved predictive analytics or “anticipatory” technology driven by artificial intelligence (AI) and machine learning (ML) that can accurately anticipate what consumers want or need before they even know it—based not just on past behavior but on real-time information and availability of alternatives that could alter consumer choices.

Faster, more powerful cloud computing.

The availability of faster and more powerful on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user. Cloud-based demand forecasting and planning solutions that crunches petabytes of data, filters it through super-sophisticated models, and helps analysts and planners gain previously unheard-of efficiencies in creating more accurate demand plans.

Instead of merely empowering consumers, technology is making decisions and acting for them. Analytics technology will be doing more and more of the work for companies by automating activities around demand forecasting and planning in real time.

It's no longer merely about predicting what consumers want. It's about anticipating—which includes the ability to adapt marketing offers and messages to alternatives based on data from hundreds of possible sources. By anticipating, we gain a greater chance of influencing outcomes. Consumer's phones or smartwatches can deliver recommendations and offers where to go, how to get there, and what to buy based on what they are about to do, not just what they've done in the past. Anticipation is about the short-term future, or even a specific day and time. Analytics provides marketers with the ability to create contextual engagements with their customers by delivering personalized, real-time responses.

Technology is helping both marketers and customers take the next evolutionary step. Instead of merely empowering customers, it's making decisions and acting for them. Analytics technology will be doing more and more of the work for companies by automating activities around research and making actual purchases.

IMPACT OF THE DIGITAL ECONOMY