Next Generation Demand Management

People, Process, Analytics, and Technology

Charles W. Chase

• Publisher: Wiley
• Year: 2016
• ISBN:
  9781119227380
• Edition: 1
• Topic: Business
• Subtopic: Decision Making
• Index: Business

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.

Chapter 1
The 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:

1. Investing in their people's skills, which requires change in behavior
2. Reorganization around horizontal processes
3. Integrating predictive as well as descriptive analytics into the process
4. 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.

Unfortunately, the concept of the one-number forecast often does more damage than it does good, especially when taken literally. What is important is that an entire organization (sales, marketing, finance, and operations planning) is working together to achieve aligned goals based on a set of aligned and integrated plans. This can only be achieved via a well setup and functioning set of cross-functional horizontal planning processes, with plans generated from the S&OP/IBP (sales & operations planning and/or integrated business plannin...

Table of contents

• Cover
• Title Page
• Table of Contents
• Dedication
• Foreword
• Preface
• Acknowledgments
• About the Author
• Chapter 1: The Current State
• Chapter 2: The Journey
• Chapter 3: The Data
• Chapter 4: The Process
• Chapter 5: Performance Metrics
• Chapter 6: The Analytics
• Chapter 7: The Demand Planning Brief
• Chapter 8: The Strategic Roadmap
• Index
• End User License Agreement