BUSINESS( NO PLAGARISM A+ WORK, ON TIME)

Demand-Driven_Forecasting_A_Structured_Approach_to..._----_Chapter_2_What_Is_Demand-Driven_Forecasting_.pdf

Home >Business & Finance homework help >Management homework help >BUSINESS( NO PLAGARISM A+ WORK, ON TIME)

CHAPTER 2

What Is Demand-Driven Forecasting?

Demand forecasting is a critical function that influences companies worldwide across all industries, including heavy

manufacturing, consumer packaged goods, retail, pharmaceutical, automotive, electronics, telecommunications, financial, and

others. Not only is demand forecasting critical to driving out inefficiencies in the supply chain, but it also affects all facets of the

company on an enterprise-wide basis. Predicting future demand determines the quantities of raw materials, amount of finished

goods inventories, number of products that need to be shipped, number of people to hire, number of plants to build, right down to

the number of office supplies that should be purchased. Demand forecasts are necessary because the basic operations process,

moving from the suppliers' raw materials to finished goods to the consumers' hands, takes time, particularly in our current global

economy. Companies can no longer simply wait for demand to occur and then react to it with the right product in the right place

at the right time. Instead, they must sense demand signals and shape future demand in anticipation of customer behavior so that

they can react immediately to customer orders.

To make matters even more challenging, the shift in the global economic climate over the past five years has created an

environment that is increasingly volatile, fragmented, and dynamic, making it difficult to predict demand. What's more, industry

consolidation, globalization, and the emphasis on lean manufacturing have put much stress on the supply chain, making it

difficult to respond to large swings in demand with an efficient supply response. These factors are compelling companies to shift

their attention from simple demand signals, such as trend and seasonality, to more dynamic demand signals, such as price, sales

promotions, and economic factors, to shape future demand based on sales/marketing tactics and to translate demand to create a

more accurate demand response. Simply generating demand projections based on static analytical methods that only sense

demand signals associated with trend and seasonality at the aggregate market channel level is no longer sufficient due to the

market complexity associated with increased products, services, and delivery choices, not to mention the fierce competitive

nature of the global marketplace. Achieving market and channel success requires a highly integrated customer and product

category strategy that exploits (or influences) the strength of an existing brand or segment to “pull” products through the

channels of distribution. Stand-alone product demand generation without a strong customer orientation is less valuable due to its

inability to sense and shape demand signals other than trend and seasonality. This situation requires more robust demand signals,

such as price, advertising, sales promotions, marketing events, economic factors, and other related market factors. To drive top-

and bottom-line growth, companies are deliberately focusing on the demand-driven framework of sensing, shaping, and

orchestrating demand across products, geographies, channels, and customers. They have found that improvements in supply

chain flexibility and responsiveness using such things as lean manufacturing can no longer build efficiencies and drive down

working capital without significant improvements in demand forecast accuracy and translation of demand to meet marketplace

segmentation targeting and customer needs.

Intense demand volatility combined with market dynamics is compelling companies to develop and deploy more integrated,

focused, and analytic-driven demand management processes, which require predictive analytics, market intelligence, and more

sophisticated enabling technologies to achieve their revenue growth goals and objectives. These changes in the dynamics of the

Chase, Charles W.. Demand-Driven Forecasting : A Structured Approach to Forecasting, John Wiley & Sons, Incorporated, 2013. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/apus/detail.action?docID=1315864. Created from apus on 2025-07-21 13:53:45.

marketplace are driving the process changes of predicting and orchestrating the best demand response and not simply demand

generation projections based on static analysis and gut-feeling judgment across aggregate level market and channel segments.

What's more, shrinking product life cycles combined with a demanding marketplace are sharply increasing the costs of choosing

supply to correct for the wrong demand response. As a result, companies are coming to realize that market and channel

dominance mandates a highly integrated and dynamic demand response. The strategic objective is to influence the market

strength of prevailing brands and segments to pull additional related existing and new products through the channels of

distribution. Traditional standalone product demand generation strategies are proving much less valuable and effective due to

market and supply chain complexity. One clear consequence of these marketplace conditions is that companies that are

unprepared and experiencing historically acceptable levels of forecast accuracy in the 50–60 percent range are getting punished

financially and, as a result, can no longer be tolerated by senior management. Companies across all industries face potentially

severe loss of competitive market position from a failure to adequately sense, shape, and translate demand signals associated

with market success breakthroughs or to rebound from market failures. Similarly, demand forecasting on a rigid monthly

schedule is proving inadequate to capture the changing market dynamics. “Real-time” demand forecasting is based on the market

volatility and dynamics, which has made it mandatory to sense demand signals weekly and manage demand orchestration daily

for rapidly changing markets.

TRANSITIONING FROM TRADITIONAL DEMAND

FORECASTING Demand management done well encompasses more than just forecasting. It incorporates sensing, shaping, and translating a

demand response into a decision-making cycle that demand-driven practitioners continuously fine-tune (or shape) based on key

performance indicators (KPIs) using the combination of data, analytics, technology and domain knowledge. Unfortunately,

demand forecasting at most companies is based on an expert's gut-feeling judgmental override to a simple baseline statistical

forecast that is built on altered sales history (adjusted for various reasons depending on the purpose of the forecast). It's a

politically correct and effective planning method for demand generation that stays within narrow boundaries, assuming that what

will happen next week will be more or less the same as what happened last week, with some incremental adjustments based on

assumptions related to business goals and objectives rather than current market conditions. Characteristics of evaluation vary

across forecasting methods and can include most recent business patterns, historic seasonality, trends, and/or responses to sales

/marketing promotional events. The inflexibility to analysis of any outside information, whether it is left or right of their personal

perceptional viewpoints, drives some companies to incorporate rough estimates of previous lost sales (e.g., unmet demand also

referred to as backlog) to get estimates of “true demand.”

WHAT'S WRONG WITH THE DEMAND-GENERATION

PICTURE? There are a number of things wrong with this demand-generation picture. First, a historical-based approach to generate demand,

particularly when companies use their past supply responses as a proxy for market demand, is not an accurate view of true

customer demand. A supply response using shipment history is far from an accurate representation of demand, since it was likely

based on many internal factors at the time, including constrained supply and capacity and/or internal employee performance

metrics. For example, the spike in sales shipments activities at the end of the quarter has less to do with true market demand than

with the sales group's compensation plans and what was most likely the result of loading the customer's warehouses with excess

inventory to make sales targets. Second, attempting to assess the impacts of traditional forces such as price, advertising, in-store

merchandising, sales promotions, marketing events, and other related factors on demand versus any variations caused by macro

influences, such as weather, catastrophic events, geopolitics, or competitive factors, is challenging. Third, combine this impact of

consumer sentiment within social communities, and other issues create further challenges when creating an accurate demand

response. Plus, macroeconomic factors don't have a direct cause and effect but do have what is called a halo effect (indirect

effect) that can be measured and utilized to develop a more true demand response.

MY GUT-FEELING JUDGMENT SAYS . . .

Trying to overcome these challenges using gut-feeling judgment does not work. An in-depth study was conducted a few years ago and published in the

fall 2007 issue of Titled “Good and Bad Judgment in Forecasting: Lessons from Four Foresight: The International Journal of Applied Forecasting.

Companies” by Robert Fildes (director of the Lancaster University Centre for Forecasting) and Paul Goodwin (professor of management science at the

University of Bath in England) and based on their ongoing five-year investigation into corporate forecasting practices, the authors uncovered evidence

of excessive use of judgmental adjustments to statistical baseline forecasts. In their report, they documented the extent of the problem in four large

companies (pharmaceutical, food manufacturer, domestic cleaning products manufacturer, and retailer). They explored the motivations that led

demand forecasters to this sometimes-counterproductive behavior and offered a series of recommendations to ensure that forecast adjustments are

made for the right reasons. After studying these four companies over five years, they found that adjusting forecasts using gut-feeling judgment was a

popular activity. In fact, demand forecasters spent so much time making manual adjustments to their corporate forecasts that they were probably

making a significant contribution to world demand for headache pain relief tablets. The percentage of forecasts adjusted each forecast cycle ranged

anywhere from 8 to 91 percent, with an average of 75 percent. So, each forecast cycle (normally monthly), an average of 75 percent of product

forecasts were manually adjusted using gut-feeling judgment. Furthermore, 25 percent of the forecasts were based only on simple statistical methods,

usually exponential smoothing. Judgment either was used exclusively (25 percent) or combined with a statistical forecast method (50 percent), which

was identified as important or very important by most of the survey respondents.1

Although the forecasters usually felt that they had good justifications for making adjustments, Fildes and Goodwin found them overly confident that

their

adjustments would improve forecast accuracy. In fact, the study showed that large adjustments did tend to be beneficial but small adjustments did not

materially improve forecast accuracy and sometimes made accuracy worse. Subsequently, negative (downward) adjustments were more likely to

improve forecast accuracy than positive (upward) adjustments. Making a large adjustment requires a lot of confidence and some nerve; as a result,

larger adjustments are likely to be made only for very good reasons. These are the adjustments that are potentially worth making. However, the authors

found that overoptimism tends to lead to erroneous positive adjustments, while negative adjustments are based on more realistic expectations. Finally,

Fildes and Goodwin found a bias toward “recency”—that is, emphasizing the most recent history while treating the more distant past as bunk. This

focus on recency tended to undermine the process of statistical forecasting.

Henry Ford was alleged to have said that history is more or less bunk or worthless. Many senior-level managers in companies

today tend to have similar beliefs and in many cases the same philosophy as described in Fildes and Goodwin's study. In their

monthly review meetings, these same executives examine most recent movements in sales data with a strong focus on the most

current while often ignoring earlier data. Fildes and Goodwin stated that many forecasters told them that they never fit their

statistical methods to demand data that is more than three years old because “back then the trends were different.” Sometimes the

software they had bought seemed to share the same attitude, as the active database went back only three years.

According to a recent survey, data are still a challenge for many companies. Also, in 2011,Supply Chain Magazine Review

the most common data source being used by the companies surveyed was customer orders, which were mentioned by close to

three out of four respondents. Another common data source is customer shipments, or replenishment, reported by 42 percent of

the companies. Point-of-sales (POS) data have become increasingly important in recent years as a source of demand data.

However, most companies still have not integrated POS data into their demand management process. Close to 33 percent of

respondents now say that they use POS data to help them drive their demand forecasts.

There was also a correlation between the use of POS data and the level of demand forecasting and planning systems

integration. Of those responding companies using POS data, 31 percent said that they were very or extremely integrated with

their enterprise resource planning (ERP) systems. This compares to only 12 percent at this integration level among companies

not using POS data. Furthermore, over 60 percent of companies using POS data for forecasting rated their demand planning

systems as moderately integrated or better, compared to only 31 percent for the nonusers. External sources of data are becoming

more popular in demand forecasting as well. Among the most commonly internally used key performance indicators are sales

promotions and price, while externally used data includes Consumer Price Index, gross national product growth, and the

Consumer Confidence Index.2

FUNDAMENTAL FLAW WITH TRADITIONAL DEMAND

GENERATION Traditional demand forecasting and planning systems purchased over the past 20 years are more focused on planning than on

sensing demand signals shaping and translating demand into an accurate demand response. Those same solutions were designed

to predict replenishment during a time when customer shipments were more stable and supply chains were less complicated with

shorter lead times. As industries have consolidated—becoming more global with a focus on lean management—the slightest

disruptions in demand have become difficult to manage with an efficient supply response. Those same traditional demand

generation systems were developed to support a misguided flawed process that relied on simple mathematical methods, such as

exponential smoothing, which can sense only those demand signals associated with trend/cycles and seasonality. What was left

over was called “unexplained” and was addressed by making manual judgmental overrides based on gut-feeling judgment, not

true domain knowledge. This simple quantitative forecasting approach is based on the premise that when an underlying pattern(s)

in the historical customer shipment data for a product can be identified, any leftover unexplained patterns are merely random, or

unexplainable. These same systems assume that the patterns (demand signals)—in this case only trend/cycle and/or seasonality—

will continue into the future, which is used as the statistical baseline forecast.

The objective of all mathematical models is to maximize users' ability to explain all the underlying patterns in the historical

demand and to minimize the unexplained. We can simply write the overall mathematical formula as:

Forecast = Pattern(s) + Unexplained

Although these traditional demand generation systems claim to have 20 or more different statistical methods, they all focus

primarily on measuring patterns associated with trend/cycles and seasonality within the demand history of the product being

forecasted and assume (predict) those underlying patterns will continue into the future. What happens when the historical trend

/cycle and/or seasonality patterns are disrupted by a global economic downturn, or market changes are influenced by consumer

preferences and behaviors? The ability of these traditional systems to measure demand signals associated with trend/cycle and

seasonality diminishes, driving up the unexplained component and requiring more judgmental overrides to the statistical baseline

forecast in an attempt to explain away the unexplained. is a simple illustration of this scenario.Figure 2.1

Figure 2.1 Traditional Demand Generation Model

As Fildes and Goodwin suggested, this demand generation model has severe challenges when too much judgmental bias

enters into the process. Over the past 60 months, the dynamics of the global marketplace has changed dramatically, making it

difficult to use the traditional approach to demand generation. Not only are the analytics and process flawed, but the data and

enabling technology need to be updated and integrated with existing ERP systems.

RELYING SOLELY ON A SUPPLY-DRIVEN STRATEGY

IS NOT THE SOLUTION Most companies facing serious challenges with forecast accuracy due to market volatility tend to abandon trying to improve

forecast accuracy and throw in the towel on demand forecasting. These same companies turn instead to supply flexibility and

responsiveness. Over the past decade, evidence has clearly shown that this strategy is fundamentally flawed and is doomed to

failure. Companies quickly come to realize that with shrinking market demand for their products as a result of the Great

Recession, supply capacity has significantly outstripped demand. There is more pressure to better understand the dynamics of the

market and influence future demand to fill the need for supply. In fact supply only exists to fulfill demand, and no amount of

rapid responsive or manufacturing flexibility can rescue a company from devastatingly lackluster customer demand. If your

forecasts consistently fall in the inaccurate normal 50–60 percent range, continued poor customer service or high expediting and

inventory costs will persist. Those companies that respond to this challenge do so by investing in data, analytics, and

technologies that improve their forecast frequency and accuracy with a what-if scenario–based iterative demand planning process

supplemented by supply-enhanced solutions.

It is not uncommon for companies to adopt a supply strategy when experiencing stock-keeping unit–level forecast errors

running on average of 50 to 150 percent, and considering demand management a “waste of time and effort.” They eventually

invest heavily in lean manufacturing and supply chain planning driven by inventory optimization solutions aimed at dealing with

the challenge entirely from a supply-driven perspective. Although this does improve manufacturing efficiencies, companies

quickly realize that it does nothing to improve customer service or reduce excess finished goods inventories and increase

working capital. After several unsuccessful years, most companies begin to augment their supply-centric initiatives with one

specifically targeted at improving forecast accuracy and enabling a rapid demand response that is based on prioritizing different

market segments. Within six to nine months of doing so, forecast error is generally reduced from an average of 100 to 50

percent, at which time the companies begin to become more confident that a target of 20 percent can be reached within the

following year. Segmenting and prioritizing the market has also been a critical factor to success, allowing companies to achieve

their targets in terms of inventory reductions and improved customer service. It is now believed that if a company begins with a

forecast more reflective of the marketplace, it may not need quite as high a level of sophistication on the supply side. Based on

empirical observations, it is clear that superior demand forecasting and orchestration with the increased frequency (weekly

versus monthly forecast cycles) dictated by market dynamics is a prerequisite to an effective demand management strategy.

WHAT IS DEMAND-DRIVEN FORECASTING? Demand-driven forecasting is the set of business processes, analytics, and technologies that enable companies to analyze, choose,

and execute against the precise mix of customer, product, channel, and geographic segments that achieves their customer-facing

business objectives. Based on recent observations and research, demand-driven forecasting on average is driven 60 percent by

process, 30 percent by analytics, and 10 percent by enabling technology, depending on the industry, market, and channel

dynamics that influence how companies orchestrate a demand response. Although enabling technology represents only 10

percent, the other 90 percent cannot be achieved without the enabling technology due to scalability and analytical requirements,

not to mention data integration requirements that span across the global corporate enterprise. The need for an improved demand

forecast focuses not only on process, analytics, and technology but also the importance of integration across the global enterprise.

Demand-driven forecasting utilizes data from market and channel sources to sense, shape, and translate demand requirements

into an actionable demand response that is supported by an efficient supply plan, or supply response. A true demand-driven

forecast is an unconstrained view or best estimate of market demand, primarily based on corporate specific historical sales

demand, preferably POS, sales orders, and shipment information. Demand shaping uses programs, such as price, new product

launches, trade and sales promotions and incentives, advertising, and marketing programs, in addition to other related sales and

marketing information, to influence what and how much customers will buy.

WHAT IS DEMAND SENSING AND SHAPING? Demand sensing and are common terms that have been loosely used over the past several years with different definitionsshaping

depending on the industry and purpose. The most common definitions are associated with the consumer product goods (CPG)

industry. Demand sensing, especially in recent years, has come to denote using granular downstream sales data (sales orders,

preferably POS data) to refine short-term demand forecasts and inventory positioning in support of a one- to six-week supply

plan. It is slowly being expanded to cover medium-term operational and inventory replenishment plans that require a one- to 18-

month demand forecast. Eventually it will also include long-term strategic forecasting and planning (two years into the future

and beyond). The term often describes measuring the relationships of customer (or consumer) demand withdemand shaping

sales promotions and marketing events or price discounts and then using those influence factors to shape future demand. These

new, much broader definitions and needs for demand sensing and demand shaping have been at the forefront of many

conversations with senior executives across all industries globally.

Demand sensing is the translation of downstream data with minimal latency to understand what is being sold, who is buying

the product (attributes), and how the product is impacting demand. Overall, three key elements define demand sensing:

1. This requires the ability to collect and analyze POS dataUse of downstream data (for demand pattern recognition).

across market channels, geography, and so on to understand who is buying what product and in what quantities.

2. This refers to the ability to analytically measure and determine theMeasuring the impact of demand-shaping programs.

impact of demand-shaping activities, such as price promotions, sales tactics, and marketing events, as well as changes in

product mix, new product introductions, and other related factors on demand lift. It also includes measuring and assessing

the financial impact of demand-shaping activities related to profit margins and overall revenue growth.

3. This refers to the ability of modeling and forecasting demand changes on a moreReduced latency/minimal latency.

frequent basis. Traditionally, demand forecasting is done on a monthly or longer basis. Demand sensing requires that the

demand be modeled on a shorter-term basis—weekly or even daily, depending on the frequency of new information—and

that the changes in demand be reflected on a daily (or whatever is the frequency of new information) basis.

Demand sensing utilizes downstream data to communicate what products and services have been sold, who is buying the

products and services, and the impact of sales and marketing activities on influencing consumer demand. These three demand

elements are used to shape future demand, which is translated into demand requirements to create a profitable demand response

through internal processes or tools designated to translate this information into demand. Although many companies have

developed demand processes to capture volume information and replenishment (shipments) within their supply chain networks, it

is the responsibility of sales and marketing to capture demand insights in regard to what sales promotions and marketing

activities have influenced consumers to purchase their products. The information translated into a demand response by sales and

marketing is used to adjust prior predictions of future unconstrained demand. Traditional sources have yielded structured data,

but unstructured sources, such as weather patterns and chatter on the social Web, are increasingly important sources of insight.

Today's supply chains still respond to demand. They simply do not sense demand. Despite the exponential investment in

sensing technologies, such as RFID, 2-D bar codes, temperature sensors, Global Positioning Systems, and Quick Response

codes, today's supply chain focuses on customer orders and shipments. Furthermore, traditional demand forecasting and planning

systems cannot scale to use the exploding volume of unstructured data and combine that data with output from the number of

sensors being installed. Additionally, supply chain latency is accepted and not questioned. We have not conquered the bullwhip

effect (the ripple effect throughout the supply chain that causes inefficiencies that could have been avoided), and the translation

of demand from retail shelf to a manufacturer's replenishment to retailer warehouses remains unchanged. The result, companies

have built long supply chains that translate, not sense, demand. The use of sensor data, market data, temporal data (weather,

traffic, etc.) to sense and reduce latency remains an opportunity. Social/mobile/digital/ecommerce convergence is changing the

“heart” of the supply chain. Supply chain leaders must combine transactional data with unstructured data to sense market to

market outside in with near-real-time latency. Laggards will continue to be squeezed from both ends.

SENSING DEMAND SIGNALS IN THE CPG INDUSTRY

Companies in the CPG industry and other industries have taken demand sensing to the next level by leveraging POS and downstream data to better

understand customer demand and to use this information to make better business and operational decisions. They use a structured approach to

transform terabytes of store-level data into actionable information across their businesses. Typically, downstream data are used in an ad hoc way—for

example, to check the lift from a specific promotion. However, these same companies employ a more structured approach to use downstream data on a

daily and operational basis to drive supply chain performance.

These same companies use downstream data to improve their short-term statistical demand forecasts. They normally define short term as one to six

weeks into the future. Their process and enabling technology provides daily forecasts by item and location level, using downstream data to improve

short-term execution (replenishment and deployment) supporting an end-to-end supply chain network. The short-term statistical demand forecast does

not replace the operational demand forecasting and planning system. These companies use the demand planning forecast for intermediate- and longer-

term planning. A benefit of using a short-term statistical forecast allows these companies to expand their sales and operation planning (S&OP) horizon

from short-term execution to longer-term tactical, operational, and strategic planning. Their downstream data process provides daily forecast revisions.

The analytical models determine the best predictive signal—that is, shipment, order, and customer data—to determine the best tactical demand

forecast.

Summary

The improvement in short-term tactical demand forecast accuracy using demand sensing is significant, and companies in the CPC industry are able to

further improve forecast accuracy when utilizing downstream data as part of their short-term statistical forecast. The business benefits have been even

more compelling, as in many cases these same companies have been able to reduce finished goods inventory while becoming even more agile by

sensing and reacting faster to changes in unpredictable demand.

In addition to improving operational efficiency, downstream data were useful to flag operational issues that would otherwise impact revenue and

service levels. For example, downstream data alerted them to an unusually high bias for a large customer at a high-volume distribution center. As it

turned out, there was a problem with the ordering system, and orders had not been placed for several days. The advance warning by downstream data

gives companies the ability to resolve issues before consumers are impacted by out-of-stocks.

Critical Success Factors

Large CPG companies work hard to build a joint value equation with their customers. Because of this approach, customers can share their downstream

data because they can articulate how the data can be used and, most important, how the data will drive business results and build value for both the

CPG manufacturer and the retailer. Customer support is critical to CPG companies. Customers will share downstream data if companies can

demonstrate their maturity in using downstream data and explain how the data will be used to create value. Having a clear supply chain vision is also

critical. Knowing where demand sensing fits within your supply chain processes is important, and believing in the short-term statistical forecast is

essential.

Large CPG companies continue to add customers to improve their demand-sensing capability, and they will continue to expand their downstream data

capabilities to more business units. These companies will look to improve their transportation planning and enhance customer collaboration using

downstream data. Integrating their suppliers to a demand signal brings interesting challenges and opportunities for future success.

Key Points

Leading within the CPG industry and knowing what products to make when consumers want to purchase them is a competitive advantage for CPG

companies. These companies use demand-sensing capabilities to delight customers, improve supply chain performance, and create value with trading

partners. The work that they are doing to sense demand can best be summed up by saying that CPG companies used to make what they thought they

would sell. Now they make what they they will sell. In an age where demand volatility remains high and companies have struggled to predict know

demand during economic uncertainty, the larger risk may be the inability to sense the upturn. Companies with the ability to sense and respond to

demand using downstream data are best suited to meet the inevitable surge and capture upside revenue while lowering inventories, waste, and working

capital and maintaining high customer service levels.

Demand shaping is the ability to increase or decrease the future volume and profits of goods sold by orchestrating a series of

marketing, sales, and product tactics and strategies in the marketplace. Several key levers can be used in the development of

demand-shaping strategies. These are:

New product launch (including the management of categories)

Price management (optimization)

Marketing and advertising

Sales incentives, promotions, trade policies/deals

Product life cycle management strategies

True demand shaping is the process of using what-if analysis to influence unconstrained demand in the future and matching

that demand with an efficient supply response. Based on various industry research studies conducted over the past several years,

demand shaping, just like demand sensing, includes three key elements:

1. Ability to increase or decrease volume and profit of goods sold by changing sales, product, and marketing tactics and

This can be achieved by enabling companies to perform what-if analysis so that they can understand the impactstrategies.

of changing price, sales promotions, marketing events, advertising, and product mix on demand lift and profitability to

make optimal demand-shaping decisions into the future. It usually refers to the shaping of unconstrained demand (i.e.,

demand shaping independent of supply constraints).

2. This refers to how much can be made based on existing capacity, and where,Supply plan/supply supportability analysis.

when, and how fast it can be delivered.

3. This refers to the ability to promote another product as a substitute if the product originallyDemand shifting (steering).

demanded was not available and/or move a sales and marketing tactic from one period to another to accommodate supply

constraints. It is especially useful if demand patterns or supply capacity changes suddenly to steer customers from product

A to product B or shift demand to a later time period. There are two types of demand shifting.

a. occurs when a company influences a customer to purchase an alternativeDemand shifting at the point of sale

product using sales and marketing incentives when a product is out of stock or backlogged.

b. is when the operations planning and manufacturing teams negotiate with theDemand shifting at the point of supply

sales and marketing teams during the S&OP process to shift unconstrained demand in the future due to supply

capacity constraints.

Over the past several years, many executives have begun to invest in demand-sensing and shaping processes along with

enabling technology. However, in almost every case, they are doing demand shifting rather than true demand shaping. If

anything they are only doing short-term demand sensing (one to six weeks into the future).

SHIFTING DEMAND AT POINT OF SALE

A good example of demand shifting is an electronics company that utilizes a demand forecast to determine the quantity of the components that make

up all the various laptop configurations. The electronics manufacturer accepts the majority of its customer orders through its Web site and then

packages to order (assembles the laptop) within three to five working days. Using demand shifting at the POS, this electronics manufacturer can shift

demand away from laptop A to laptop B due to a shortage of components that make up laptop A (backordered for various reasons due to product

component availability or inaccurate demand forecast). For example, let's say you decided to go online to the electronics manufacturer's Web site to

purchase an M125 laptop. You may find a sales promotion pop up saying for today and today only you can purchase an M135 laptop with a bigger

screen, more processing capacity, bigger hard drive with expanded memory, and additional software for a reduced price. Someone may have under-

forecasted demand for the key components that make up the M125 laptop, or there is excess inventory for the M125 laptop due to the M135 being

over-forecasted and the need to deplete the excess inventory. The electronics manufacturer inserted a sales promotion in an attempt to shift demand

away from the M125 laptop to the M135 laptop so as not to lose a customer and until the components for the M125 laptop are available.

Demand shaping happens when companies use sales and marketing tactics like price, promotion, new product launch, sales

incentives, or marketing programs to increase market share, or share of wallet. The use of these tactics increases demand

elasticity. All too many times, companies believe that they are shaping demand but find that they are really just shifting demand

(moving demand from one period to another). Moving demand from one period to another and selling at a lower margin without

improving market share and revenue growth creates waste in the supply chain. The first step in the demand-driven forecasting

process is sensing market conditions based on demand signals and then shaping future demand using technologies such as price

optimization, trade promotion planning, new product launch plan alignment, and social/digital/mobile convergence. Demand

sensing reduces the latency of the demand signal by 70–80 percent allowing the company to better understand and see true

channel demand; demand shaping combines the tactics of pricing policies, sales promotions, sales and marketing incentives, and

new product launches to increase demand, as well as the cause of the lift.

In other words, the more sensitive and responsive a forecast is to trends and seasonality, the more value it has to supply chain

management because it is more stable and consistent overtime, which makes matching demand to supply easier. The ability to

detect changes in demand rapidly gives companies greater flexibility to accommodate those changes or influence overall

demand. However, according to a magazine survey, most respondents were not satisfied withSupply Chain Management Review

the ability of their demand forecasting and planning systems to sense short-term demand signals associated with trend and

seasonality. Well over 50 percent of those surveyed rated their demand forecasting and planning system as either not at all or

only slightly responsive to short-term trends and seasonality while only 20 percent described their system to be very or extremely

responsive. This is primarily due to the economic downturn that has disrupted past trends and seasonality, making it difficult to

use past trends and seasonality alone to predict future demand. Most companies attempt to influence demand through demand-

shaping activities, such as pricing, sales promotions, and/or new product introductions. Those same legacy demand planning

systems are unable to predict the impact of sales promotions, pricing changes, marketing events, and other related sales

/marketing activities. As a result, fully 37 percent of respondents rated their systems as not at all effective in measuring the

impact of their demand-shaping activities. Another 30 percent rated their systems as only slightly effective in measuring demand-

shaping activities. Only 10 percent of respondents rated their demand forecasting and planning system very effective in

measuring their demand-shaping activities.3

As we discussed earlier in this chapter, traditional demand forecasting and planning systems were not designed to sense

demand patterns other than trend/cycle and seasonality. For that reason, it is difficult for these systems to measure demand-

sensing and -shaping activities associated with price, sales promotions, channel marketing programs, and other related factors.

As the global marketplace has become increasingly volatile, fragmented, and dynamic and as supply chain lead times have

become overextended, companies are quickly coming to the realization that their demand forecasting and planning systems are

no longer adequate to sense demand signals and use those demand signals to shape future demand. Two primary factors have

contributed to this situation:

1. Limited statistical methods available in traditional demand forecasting and planning systems

a. Traditional demand forecasting and planning systems are designed to sense and predict stable demand that is highly

seasonal with distinct trend patterns

b. Traditional demand forecasting and planning systems primarily use only one category of statistical models, called

time series methods, with a focus on exponential smoothing models, such as simple exponential smoothing, Holt's

two-parameter exponential smoothing, and Winters' three-parameter exponential smoothing.

2. Process requires domain knowledge versus judgment to:

a. Define data availability, granularity and sourcing,

b. Assess the dynamics of the market and channel segments to identify those factors that influence demand.

c. Run what-if analyses to shape future demand based on sales and marketing tactics/strategies.

Figure 2.2 illustrates the need for demand forecasting–enabling technology that includes all four categories of statistical

methods: (1) time series, (2) intermittent demand functions, (3) autoregressive integrated moving average (ARIMA), and (4)

causal models (e.g., multiple regression, and autoregressive integrated moving average with exogenous input [ARIMAX]).

Although all four categories are required to accurately model all the products within a corporate product portfolio, it is the fourth

category of methods, the causal models, that are designed to sense and shape demand signals other than trend/cycle and

seasonality; these signals include price, sales promotions, marketing programs as well as other related factors. Figure 2.2

illustrates the new demand-driven management model.

Figure 2.2 New Demand-Driven Management Model

Research continues to show that there is a strong correlation between demand visibility and supply chain performance. As

demand visibility yields higher accuracy in assessing demand, efficiencies continue to accumulate throughout the supply chain.

Yet in most companies, there is still a wide gap between the commercial side of the business, with its understanding of the

market and plans for demand sensing and shaping (e.g., sales/marketing tactics and strategies, new product commercialization,

end of life, and social media), and the supply chain organization, with its ability to support those efforts. Demand sensing as a

core capability isn't new; retailer POS data, syndicated scanner data, customer insights, and focus groups have guided marketing

and sales promotional programming for over two decades. The challenge is how to translate these demand insights into actions

that drive an efficient profitable supply response. The ability to sense, shape, and translate demand into an accurate demand

forecast and a corresponding supply response will require more transparency and collaboration between the organization's

commercial and supply chain function. It will require a new demand-driven management process that utilizes true historical

demand and market intelligence.

Demand shaping is the proactive process of varying (increasing or decreasing) the elements that influence demand volumes

and the corresponding revenue and profit of the products sold by a company based on internal sales and marketing strategies and

tactics as well as external factors in the marketplace. The internal sales and marketing strategies and tactics normally involve

new product launches, pricing policies, messaging and advertising, sales incentives, sales promotions, marketing events, trading

polices and discounts, and product life cycle management. External factors could be competitors' activities, weather-related

events, commodity prices, interest rates, and other economic factors.

The key to demand shaping is cross-functional collaboration between sales and marketing and among the other members of

the supply chain (e.g., finance and operations planning) by coordinating and agreeing on demand-shaping programs. The core

purpose of such programs is to drive unit volume and profitability among the company's brands and products. At first, these

activities typically are monitored and managed independently by each functional department, such as sales, strategic marketing,

and product management, with little cross-functional integration. For example, a price change occurring simultaneously with a

product sales promotion could erode the profitability of the product or create an unexpected out-of-stock situation on the

retailers' shelf. Cross-functional collaboration among sales and marketing requires companies to shift to a cross-departmental

market orientation that balances the trade-offs of each tactic and focuses on spending efficiencies and profit generation.

To better understand the dynamics of demand shaping, we need to break down the demand management process into a

capability framework made up of five key components. These are:

1. A set of more sophisticated statistical models is a key requirement to enable demandSophisticated statistical engine.

shaping. Such models measure the effects of different sales and marketing events and enable a better understanding of the

incremental volume that is associated with them. The ability to measure past events over time and clearly identify which

ones are profitable helps companies avoid unexpected planning events that produce negative returns and exploit those

identifiable events that are more profitable in driving incremental demand and profit.

Companies can proactively influence the amount and timing of consumer demand by varying the marketing mix elements

that influence demand for a product through the use of what-if analysis. For example, varying the price, sales promotions,

and levels of merchandising and advertising can influence consumers to demand more of a company's product. More

advanced methods, such as ARIMAX and dynamic regression modeling, utilizing downstream POS data, can help sales

and marketing analysts (demand planners) better understand consumer demand insights and uncover such things as price

elasticity. Combining these more advanced statistical techniques with decision-support tools like what-if analysis enables

sales and marketing analysts (demand planners) to determine the right trade-offs within the marketing mix by market,

channel, brand, and product that will drive incremental unit volume and profit. Senior managers are moving toward the

use of downstream data and consumer demand insights to capture and build on current trends and seasonality, utilizing

marketing programs based on the combination of historical data and domain knowledge, not gut-feeling judgment.

2. BI capabilities combine the power of analytics with monitoring, tracking, andBusiness intelligence (BI) solutions.

reporting with user-friendly interfaces. BI portals/dashboards allow sales and marketing personnel to collect, integrate, and

apply data from the statistical engine and the field to support business activities, such as planning pricing strategies, sales

promotion events, and measuring results against strategic and tactical business plans. Demand shaping can be used to

reduce demand volatility, thereby reducing the need for supply network agility. For example, corporate leaders in various

industries (e.g., food services, spare parts planning, and electronics) are looking to use Web channels to sense demand

signals and shape future demand using distributor networks.

3. It is important to measure demand-shaping programs after each completedMeasure demand-shaping performance.

demand forecasting cycle to determine the success or failure of the programs implemented to drive demand. Historically,

it took weeks to review and assess the success or failure of a sales promotion after its completion. With new enabling

technology along with downstream data collection and synchronization processes and market sensing capabilities, today it

is much easier and faster to monitor, track, and report on the effects of a demand-shaping program. This allows companies

to manage the demand-shaping process around real-time demand signals. Adjustments can be made to demand-shaping

programs within a daily or weekly period to better manage the outcome.

4. Establish clear decision criteria, empower senior managers andExecutive alignment to support change management.

their staff, and develop an appropriate incentive program that also includes rewards for accurate demand forecasts.

Decentralize tactical knowledge-based decision making while balancing corporate strategic unit volume and profit

objectives. Stress the importance of building a demand forecast based on sales and marketing programs that are profitable,

not just volume generators. Then focus on traditional supply chain processes that match demand with supply under the

mandate of managing inventories to ensure that out-of-stocks will no longer need to be the focal point. There will be a

paradigm shift, moving from a view of unit volume in isolation of profitability (not considering profit, but only

incremental volume for trial purposes) to a more focused view of how unit volume increases can affect profitability.

5. . Short- and long-range business strategy and planning, operational tacticalContinuous business process improvements

planning, and post-event analysis must be coordinated in the organization. Sophisticated analytics shared across the

various departments within a company through well-designed decision-support networks will provide more consistency

and alignment of internal processes and work flow to drive profitability.

Demand shaping focuses on creating an unconstrained demand forecast that reflects the sales and marketing activities that

shape demand rather than using supply to manage demand. It is a process that aligns customer demand at strategic and tactical

levels with a company's marketing capabilities, resulting in improved revenue and profitability. It also helps to optimize use of

sales and marketing resources to reduce excess finished goods inventory and improve overall supply chain efficiencies. At the

strategic level, the emphasis is on aligning long-term marketing investment strategies with long-term customer demand patterns

while maximizing marketing investment efficiencies. At the tactical level, the focus is on understanding consumer demand

patterns and proactively influencing demand to meet available supply, using the marketing mix to sense and shape price, sales

promotions, marketing events, and other related factors to influence profitable demand.

THREE-SCOOP ICE CREAM APPROACH TO DEMAND FORECASTING

A large frozen food manufacturer improved its forecasting accuracy over 4 percent and increased service levels 6 percent as a result of the

implementation of a demand-driven forecasting and planning solution. Despite growing revenues, the company was able to hold inventory costs flat,

and its sales force was better equipped to plan profitable sales promotions. According to several company officials, the frozen food manufacturer's

savings exceeded the company's expectations.

This manufacturer has one of the largest U.S. frozen distribution store delivery networks responsible for distributing its frozen pizza and ice cream

products to thousands of stores nationwide. Pizza and ice cream are seasonal items and are highly promotion driven, with variety exploding in recent

years. Twenty years ago, consumers could choose from a dozen pizza varieties and similar number of ice cream flavors. Today, there are whole-wheat

crusts, gourmet toppings, no-sugar-added ice cream blends, and seasonal flavors like pumpkin. With so much variety, shelf space is at a premium. It is

critical for producers to ship the right amount of product to the right store at the right time.

Meanwhile, this food manufacturer must make sure product doesn't sit too long at distribution centers, not just to reduce carrying costs but also

because food is perishable.

Scoop 1: Managing a Promotion-Driven Business

The heavy promotional nature of ice cream and pizza also causes demand to wax and wane by store or region. Before using their new demand-driven

forecasting and planning solution, company planners struggled to factor in seasonality. The sales force was also guessing at how much product needed

to be stocked for special promotions designed to drive volume and even what price point to select to drive enough volume to turn a profit.

“Our existing solutions did a poor job of forecasting demand around promotions,” explains the director of demand and supply chain planning.

Data were also scattered in numerous locations. Some data were sitting in spreadsheets in regional offices and might get sent in once a week, if that. In

the division that handles delivery to drugstore chains (a growing business), forecast accuracy was decreasing by the year.

“It was driving a lot of service issues and increasing our carrying costs,” explains the senior manager for strategic sourcing.

Scoop 2: Choosing a Demand-Driven Solution

The food manufacturer wanted a robust, scalable, high-speed solution that didn't require planners to spend the majority of their time administering the

data, and a user-friendly reporting system. It was also looking for a vendor known for investing in customer success and a solution that is workable for

a demand planner, not a statistician.

“At that point the vendor list got pretty short,” said the director of supply chain, adding that the company chosen had to show it had successfully

provided complex, demand-driven forecasting solutions to multiple enterprises of similar sizes.

As part of the selection process, the software vendor ran a proof of concept that quickly suggested the potential of its solution.

No Second Guessing

“When we switched to our new demand-driven forecasting and planning solution, we saw our forecast accuracy improve immediately, we saw service

levels take off in a positive way and our inventories decreased,” says the senior manager for strategic sourcing. “We actually exceeded our original

projections,” says the director of supply chain integration, who added, “Forecast accuracy drives safety stock reduction, inventory days on hand,

storage costs, and freight costs. By gaining a few points of accuracy at the national level, you can immediately see the savings.” The accurate forecasts

have even impacted areas like planning efficient truck routes.

The accuracy is driven by a change from a 50,000-foot view of forecasts to a more detailed look. “We can talk about a particular deal with a retailer

and know what kind of lift is generated, then that drives the supply chain,” the senior manager for strategic sourcing explains. “There is no second

guessing.”

Scoop 3: Creating Synergy between Sales and Planning

The new demand-driven forecasting and planning solution's supply chain intelligence center provides an interactive portal where salespeople enter the

attributes of a promotion they'd like to run and the demand planners provide a variety of scenarios (demand shaping) to help sales decide whether the

promotion will be profitable. The sales teams have the capability to measure the impact of in-store merchandising vehicles, such as end-cap displays,

to determine the incremental unit volume impact, as well as revenue impact within designated market channels (i.e., retail grocery channel).

Using the new demand-driven forecasting and planning solution, the demand planners can make calculations and let salespeople know, for instance,

that there isn't enough pepperoni in the pipeline near their territory to meet the estimated volume that the promotion will generate. Then the demand

planners can work with sales to find a better promotion (demand shifting). They also can help the salespeople calculate the lift for a promotion and

whether that sales increase, at that price, will make the promotion profitable.

“It takes the subjectivity out of it,” says the senior manager for strategic sourcing.

Critical to this service is that the demand planning staff doesn't need from six hours to an entire weekend to gather the data, including tens of

thousands of time series calculations. Gathering used to take so long, and information was released on such a rigid schedule, that the information was

sometimes out of date by release date. With the new demand-driven forecasting and planning solution, it takes only a few minutes to update

information, so planners publish as they update.

“It's a better answer that gets out faster,” says the director of supply chain.

Achieving Lasting Return on Investment

The food manufacturer wants to use its demand-driven forecasting and planning solution to build three additional attributes into its forecasts:

competitor activities, weather conditions, and whether a promotion cannibalizes existing sales.

“We are trying to understand if our promotions are impacting sales of other products,” explains the senior manager for strategic sourcing.

Although overall forecast improvement is at 4 percent, some specific forecasting projects netted forecast accuracy increases of 7 percent or better.

Meanwhile, as the company has acquired additional frozen food brands and grown revenues, carrying costs have remained flat.

With confidence in the forecasts, “People are letting that number drive through the whole organization, affecting what we produce, where we're going

to ship, all the way up to our top-line financial commitments. This drives all facets of our business,” says the director of supply chain.

“With our new demand-driven forecasting and planning solution, we're able to accomplish our goal of right flavor, right time, right store,” explains the

director of supply chain. “It's hard to put a price tag on it, but it is really invaluable in terms of running the business effectively and better serving the

customer.”

Demand shaping is becoming an essential part of the sales and operations planning process. From a tactical standpoint,

demand shaping enhances the demand and supply planning process by improving demand/supply balance. Although most

companies use demand forecasting to plan for customer demand, they need to use demand shaping and shifting to close the gap

between unconstrained demand expectations and supply availability.

CHANGING THE DEMAND MANAGEMENT PROCESS IS

ESSENTIAL The demand-driven forecasting process is a four-step process that allows companies to:

1. Sense demand signals through the synchronization of internal and external downstream data.

2. Shape future demand using advanced analytics to create a more accurate unconstrained demand forecast.

3. Shift (or steer) demand based on collaboration with sales, marketing, finance, and operations planning based on

capacity constraints.

4. Create a final constrained demand response.

Using what-if analysis, demand forecasters can shape unconstrained demand based on current sales and marketing activities as

well as external factors affecting demand, such as weather, special events, and economic conditions. In this way, they can

optimize volume and revenue while minimizing marketing investment. This structured approach puts the burden of

accountability on the sales and marketing organizations to produce a more accurate unconstrained demand forecast that reflects

current market conditions—assuming there is unlimited supply. illustrates the four key steps in the demand-drivenFigure 2.3

forecasting process.

Figure 2.3 Demand-Driven Forecasting Process

This change in the process signals a radical shift in the way companies view their demand forecasting and planning process

today. Most demand forecasting processes are supply driven with little emphasis on predicting unconstrained demand, let alone

shaping future demand.

Over the past five years, all the companies that I have worked with have tended to shift demand to meet supply constraints,

thinking they are shaping future demand. In fact, they are fitting demand to supply rather than supply to demand. Demand-driven

forecasting is a proactive structured process that senses demand signals and shapes future demand based on sales and marketing

strategies and tactics rather than reacting to past supply constraints. It puts more emphasis on downstream activities that directly

affect consumer demand, thus creating a more practical view of true unconstrained demand. It is recommended that companies

use POS data to forecast demand when it is available. POS reflects the true consumer purchase behavior at the point of sale

rather than manufacturer replenishment shipments or depletions from wholesaler or distributor warehouses that support

replenishment of retail store inventories.

There is no established operational definition of Since we can't measure it reliably, we can only relyunconstrained demand.

on POS data, syndicated scanner data, and/or customer orders (sales) as the most reliable source of unconstrained demand. We

need to acknowledge that, in many cases, the actual demand (sales) could be higher or lower if backorders and/or reorders did

not occur due to production and/or delivery issues. However, wherever possible, companies should use available demand data,

such as POS, syndicated scanner data, or customer orders. Most companies are now collecting some form of demand data

directly from their customers via electronic data interchange (EDI) and third-party resources (Nielsen Company, Information

Resources Inc., and Intercontinental Marketing Services). These data are available for a large majority of products across4

numerous distribution channels and market areas for the CPG, pharmaceutical, automotive, and many other manufacturing

industries. For example, automobile manufacturers are now capturing retail car sales (POS data) directly from retail car dealers

on a daily and weekly basis. In almost all industries, customer orders are readily available for most products. For the products,

channels, and market areas where there is a lack of sales (demand) data, either customer orders or replenishment shipments are

the only reliable data sources for demand. Throughout the remainder of this book, unconstrained demand is implied as sales

(demand) to the consumer from the retailer unadjusted for back orders and reorders.

Figure 2.4 illustrates the compared weekly syndicated scanner data provided by IRI for a CPG beverage product to the

manufacturer's shipments in the Miami market in the grocery channel. Although the data are very close, there are obvious

periods where supply is greater than demand and vice versa, making it difficult to predict true demand using shipments alone.

Figure 2.4 Weekly Demand for a CPG Beverage Product versus Supply (Shipments) Response

The so-called bullwhip effect occurred frequently in earlier weekly periods before the CPG manufacturer began to introduce

demand-driven forecasting practices. It also occurred in later periods, as the process, analytics, and enabling technology were put

in place and unconstrained demand and supply begin to merge, eliminating the effect. As the manufacturer begins to integrate the

syndicated scanner data (consumer purchases at the retailer), and link it to shipments the bullwhip effect tends to be minimized

over time.

Seasonality, sales promotions, and marketing activities (e.g., pricing policies, advertising, in-store merchandising, etc.) and

unexpected external events (e.g., hurricanes, strikes, oil price increases, etc.) that directly affect demand are difficult to forecast

accurately using pure judgment or a jury of executive opinion exclusively. Even when historical sales patterns are consistent,

forecasts created using someone's opinion or judgment have been proven to be inaccurate. As a result, an unconstrained demand

forecast should be created based on statistical analysis using predictive analytics applied to historical time series data. These data

are used to establish a statistical baseline forecast of demand that provides the necessary means to initiate fact-based discussions

centered on data and analytics to improve the quality of the unconstrained demand forecast.

The demand-driven forecasting process design should include an iterative framework that combines analytics and domain

knowledge with financial assessment of sales and marketing strategies and tactics. The objective of this demand-driven process

approach is to develop an accurate prediction of future unconstrained demand. It does this based on historical patterns associated

with trend, seasonality, price, sales promotions, marketing programs, and new product commercialization combined with domain

knowledge acquired through experience by sales and marketing personnel to support the strategic, operational, and tactical

business plans. The primary purpose is to help senior managers better understand the dynamics surrounding the marketplace and

to influence business decisions that drive incremental unit volume growth and profitability. The overall demand-driven

forecasting process design must combine quantitative discipline with sales and marketing knowledge and experience through a

collaborative process that captures knowledge across the enterprise to gain consensus. The consensus forecasting building

process supports the overall demand-driven forecasting process. It allows the demand forecasting process owner, usually the

director or manager of demand planning, to view departmental forecasts from various functions that have different perspectives

of the state of the business based on their views of the marketplace. By integrating sales and marketing inputs into one demand

view, the demand forecasting process owner can provide participants in the weekly/monthly consensus forecasting meeting with

information that enables them to compare the departmental inputs to identify, discuss, and close gaps. The result is a more

realistic prediction of unconstrained demand that reflects the true market opportunities that will improve overall volume growth

and profitability.

Financial assessment is necessary throughout the demand-driven forecasting process to evaluate the profit potential and

impact of various sales and marketing strategies and tactics that are designed to drive incremental demand. Finance's role is more

of a support function that assesses the revenue implications of sales and marketing activities that are used to shape future

demand. In many cases, the payback, or revenue potential of sales promotions and marketing events, is minimal at best. These

activities can cause huge volume swings in demand, which then create havoc throughout the supply chain, shifting resources and

adding unnecessary costs. They result is the bullwhip effect.

The finance department tends to support sales and marketing programs that unwittingly drive unprofitable short-term

incremental demand. This behavior reduces margins and subsidizes existing brand-loyal consumers who normally would

purchase the company's products at the regular price. Although the intent of these programs is to lure new consumers to the

company's brands and products, analytics has proven that the large majority of marketing programming only shifts demand. Over

time this erodes brand equity and the overall health of the business. The finance department needs to ensure that such programs

are thoroughly assessed financially to reduce unnecessary swings in demand that are not profitable and communicate it to senior

management. Overall, the finance department is far more effective in assessing demand-shaping activities than in creating

another input or departmental view of the demand forecast.

Unfortunately, in many companies, the finance department's role in the demand management process is more influential in

maintaining strict adherence to the original financial budget or plan. The financial budget is intended to provide a benchmark and

initial planning tool to gauge the potential health of the business. In many cases, the financial plan is created six months to a year

in advance, making it obsolete after the first demand forecasting cycle update. By the time the first period of the plan is reviewed

and updated, more new, relevant information has become available that should be used to assess the variance between the

original plan and current market conditions. That same information can be used to influence demand-shaping activities by

assessing the profit impact and supporting marketing programs that can close those gaps during the demand-shaping activities.

The finance department should influence the deployment of marketing programs to ensure the sales department has adequate

time to implement the programs. For example, if demand is tracking above the plan, a decision may be made to either increase

supply or reduce marketing activities (or increase price) to lower demand back to the level of the original plan. However, it is

unrealistic to expect to influence demand only one month into the future with a new sales and marketing campaign when it takes

a minimum of three to six months—and in some cases a year—to purchase the promotional materials, schedule advertising

campaigns, and gain agreement from key retail customers to execute the program. Nevertheless, in many companies, during the

consensus demand forecasting meeting, the finance team supports unrealistic timing of discretionary marketing program

deployment to adhere to the original financial budget or plan. The finance department would add much more value to the overall

demand-driven forecasting process by supporting demand-shaping activities that impact the profitability and revenue generation

associated with sales promotions and marketing activities.

The ideal demand management process must:

Be iterative by design and include statistical what-if analysis and planning with financial assessment to determine total

unconstrained demand.

Consider the impact of sales and marketing budget constraints to meet those opportunities, such as sales and marketing

investment spending and the profitability impact on incremental unit volume growth.

Identify alternative strategies and make decisions that improve profitability while maintaining stable unit volume growth.

The ideal demand management process should focus on being demand driven to include the evaluation of strategic,

operational, and tactical plans to consolidate departmental inputs by identifying, assessing, and closing any financial gaps. Thus,

it should provide a realistic view of true unconstrained demand. It is based on a sales and marketing interdepartmental consensus

that has been financially analyzed for further refinement to be used in support of the S&OP planning process. If supply

constraints are imposed on the unconstrained demand forecast, then shift demand, if necessary, to allow a more efficient and cost-

effective supply response to meet demand. illustrates this process with a focus on sensing demand signals, shapingFigure 2.5

future demand, and financial assessment to manage the effectiveness of sales/marketing strategies and tactics that influence

unconstrained demand.

Figure 2.5 Demand-Driven Forecasting and Planning Process

During the S&OP process, the operations planning and finance departments match supply to the unconstrained demand

forecast with the most cost-effective and efficient supply response to create a constrained supply plan that reflects capacity

constraints. The final constrained supply plan is then sent to the company's legacy ERP system to drive all upstream planning

processes and systems. The unconstrained demand forecast is a key input to the S&OP process. It is critical that the

unconstrained demand forecast provide some reasonable degree of accuracy, reflecting not only current market conditions but

also how internal sales and marketing programs are influencing consumers to purchase the company's products.

COMMUNICATION IS KEY The review and collaboration between sales and marketing in the demand management process is a collaborative work flow

designed to create a consensus demand response. Also, the willingness of all participants in the process to be open with the goal

of providing constructive discussion about the business issues will have a positive impact on demand. The focal point of the

discussion should be supported by BI information based on sound analytics. Not only should the BI applications and tools

provide monitoring, tracking, and reporting capabilities; they also should link KPIs and metrics directly to the demand

management process. Unfortunately, in many organizations, the consensus demand forecasting meeting becomes an opportunity

to vent with internal departments pointing fingers at one another and/or individual participants, which leads to unproductive

discussions that add minimal value to the overall process.

Communication through collaborative work flow is a critical element in any process. Communication provides the means to

gain additional insights and understanding and, ultimately, final agreement, all necessary in order to make better-informed

business decisions to create an accurate demand response. Communication within the demand management process occurs not

only at the sales and marketing department level between analysts and planners but throughout the management organization

structure within a work flow environment. Communication begins with the analysts (or planners) and eventually escalates to

senior-level managers through discussions leading up to approval of the final departmental view of demand. The initial

discussions focus on the review of the prior period's inputs during the last formal monthly (or weekly) consensus demand

forecasting meeting. Often this is referred to as the collaborative/consensus demand forecasting process, as the various inputs

from the sales and marketing departments (and in many companies the finance and operations planning departments) are

consolidated and compared to create the final unconstrained demand forecast. includes the collaborative work flowFigure 2.5

required to create a final consensus demand response that captures sales and marketing inputs incorporating strategic and tactical

execution of various assumptions and plans.

Communication through collaborative work flow among all the participants is extremely important throughout the process. It

culminates at the actual monthly (or weekly) consensus demand forecasting meeting. This meeting is critical to facilitating the

dissemination of vital information to create the final unconstrained demand forecast. An environment that is conducive to free-

flowing information and learning will help to build a knowledge base that will continuously improve the process and encourage

participants to produce a more accurate unconstrained demand forecast. This is accomplished through building trust among the

participants, in the quality of the analytics, and the improved results.

Traditional supply chains are operationally disconnected and reactive to demand. Demand volatility and operational

complexity require supply chains to become more resilient. The demand-generation process should be structured around a

demand-driven value network model that provides a framework and describes the capabilities that enable a more profitable and

agile response to demand. Demand-driven value networks begin with conscious choices that integrate and synchronize supply

with demand channels and product portfolios. Companies also must develop the capabilities to make cross-functional trade-offs

on an ongoing basis to find and maintain the right balance of quality, speed, cost, and service. The move from traditional supply-

driven networks to demand-driven value networks requires companies to think differently on several levels:

Metrics must reflect a focus on customer value rather than supply orientation.

Operations must move beyond execution excellence to include the ability to sense, shape, and translate demand into a

profitable and agile supply response.

Technology must go beyond supporting transactions to enabling more robust analytics and collaborative relationships.

MEASURING DEMAND MANAGEMENT SUCCESS The measure of success of any process is the process members' strong commitment to it. This is also true for the demand

management process, which focuses on being demand driven. As in most process design flow models, the burden falls on the

shoulders of the demand forecasting champion to gain acceptance of the new process, because in many cases it will be radically

different from what was used in the past. As the demand-driven management champion, the person will need to directly

influence participant behavior and set the proper expectations. For this reason, he or she will need to be a member of the senior

management team, directly reporting to the chief executive officer, president, or senior vice president responsible for supply

chain management.

A large amount of enabling demand forecasting technology is on the market today. Selection of an enabling software solution

should be made based on components that enhance the effectiveness and maximize the impact of demand-driven forecasting in

support of the S&OP process. The demand-driven management process can benefit from an enabling solution that incorporates

these capabilities:

Extract, ransform, and oad (ETL) data intelligence capabilitiest l

Scalability to create product hierarchies for thousands of products

Ability to sense demand signals short, medium, and long term

Advanced analytics with optimized model selection to sense demand signals other than just trend and seasonality

What-if analysis capabilities to shape future demand

Ability to support collaborative/consensus demand forecasting and planning

Exception forecasting, monitoring, and reporting with alerts

Reduced forecast cycle times

A good demand management process will enhance the S&OP process by providing a consensus demand forecasting

environment that incorporates statistical methodologies, dashboarding capability, and work flow to create a more accurate

unconstrained demand forecast.

BENEFITS OF A DEMAND-DRIVEN FORECASTING

PROCESS A demand management process and enabling technology supported by demand-driven forecasting principles will have a

significant impact on a company, whether that company sells products or services. Companies that have implemented a demand-

driven forecasting process have experienced three key benefits:

1. Due to improved demand forecasting results, more effective upstream planning that focuses on unconstrained demand

results in a more accurate demand response. The benefits of more effective upstream planning include a reduction in out-

of-stocks on the shelf at retailers; a significant reduction in customer back orders; a reduction in finished goods inventory

carrying costs; and consistently high levels of customer service levels across all products and services. High customer

service results in high customer retention due to having the right product in the right place at the right time.

2. Due to improved collaboration, senior managers have a better understanding of what drives profitability, resulting in

tighter budget control and more efficient allocation of marketing investment dollars. This results in a better understanding

of product, customer, and market profitability, allowing the creation of more focused strategic and tactical plans to allocate

resources across brands and products to drive incremental unit volume growth and profitability.

3. As all stakeholders in the process begin to trust the demand-driven forecasting process and enabling solutions, they

become more tightly aligned, driving quality collaboration among sales, marketing, finance, and operations planning as

well as external stakeholders. The building of quality relationships translates into stronger network integration, which

helps to minimize the pressures of the market dynamics surrounding the company's brands and products.

A demand-driven forecasting process and enabling solution is introduced not just for cost savings. Demand-driven-based

value networks can create a competitive advantage in providing higher-quality demand forecasts to improve customer service

over competitors to increase market share for a company's products and services. Before a company actually begins to implement

a demand-driven forecasting process, it should decide who will own the process and the enabling solution. That department and

/or department head should also be the champion who will drive the change management activities needed to create the

appropriate environment. The demand-driven management champion must be a senior-level manager at the director level or

higher, preferably the vice president of supply chain or other functional area responsible for improving the accuracy of the

unconstrained demand forecast. Ideally, the vice president of sales and marketing should be ultimately accountable for delivering

the unconstrained demand forecast. All these roles need to be determined before a company begins to consider evaluating a new

enabling solution.

To gain acceptance of this radical change to the demand management process, all stakeholders should be involved.

Stakeholders include the consumers of the demand forecasts and especially those who provide those forecasts as inputs to the

consensus demand forecasting work flow. Communicating clearly who will be accountable and how the results will affect the

business is critical to gaining acceptance and improving the overall accuracy of the demand response. It is also important to

identify and agree on a clear set of common demand management KPIs to be used to monitor, track, and report the results for

performance management and compensation purposes. Doing this will create more interest on the part of senior-level managers

regarding the importance of demand management, particularly as they begin to see more accurate demand forecasts and as the

demand-driven management process becomes integrated into the company culture. Finally, more accurate demand forecasts will

reduce the pressure to manipulate and shift demand or to introduce more discretionary sales and marketing programs that create

huge swings in demand and enhance the bullwhip effect. Sales promotions and marketing programs eventually will become tools

to improve the health of the company's brands and products.

KEY STEPS TO IMPROVE THE DEMAND

MANAGEMENT PROCESS There are several key steps a company can take to begin the transition to a demand management process that is demand-driven

focused:

Increase across the organization, both internally and externally among all the functional departments, collaboration

including key external customers.

Introduce an supported by a strong demand-driven forecasting process that focuses on data and analytics to S&OP process

sense demand signals and shape and translate demand to create a more accurate demand response.

Increase to reflect the business hierarchy segmenting products based on key profit implications.granularity of data

Leverage a demand management–enabling solution that is allows the automation of demand driven focused and

forecasting work flow to create a more accurate unconstrained demand forecast using a consensus demand forecasting

framework.

Choose an enabling solution that provides a user-friendly interface that allows nonstatistical users (or planners) to

systematically run to shape future demand up/down a business hierarchy for thousands of products.what-if analysis

Investigate integration of , like POS data, to provide a better source of true demand.consumer demand data

Invest in for sales and marketing personal as well as others involved in creating the unconstrained statistical training

demand forecast to improve their forecasting skills.

Consider implementing an that would be responsible for creating and maintaining the independent analytics department

statistical baseline demand forecast models.

Seek out an internal demand management to help drive the necessary change management requirements to champion

transition the corporate culture.

Even with the best intentions, companies are still having problems generating accurate demand forecasts. Accurate demand

response continues to be one of the most sought-after objectives for improving supply chain management. Companies have

ignored demand forecasting and chosen to improve upstream efficiencies related to supply-driven planning activities. It has

become clear that demand forecasting affects almost every aspect of the supply chain. Now that many of the upstream planning

inefficiencies have been resolved, there is a renewed focus on improving the accuracy of companies' demand responses.

WHY HAVEN'T COMPANIES EMBRACED THE CONCEPT

OF DEMAND-DRIVEN? Demand-driven value networks have evolved from a traditional supply-driven conceptual design and, as a result of the global

marketplace, have become more sophisticated by synchronizing demand and supply. Orchestrating demand at the mature stage of

the demand-driven transformation process allows companies to better balance growth and efficiency, costs, and customer

service, and demand fluctuations while reducing working capital. When demand-driven maturity is achieved, there is not only

better balance but also greater agility across the supply chain. Based on research conducted since the 2009 global economic

meltdown, those companies that have become demand driven were able to sense market changes five times faster and align their

value networks three times quicker to changes in demand. This quicker alignment enables better customer service with5

substantially less inventory, waste. and working capital. If this is true, then, why aren't more companies demand driven?

The simple answer is implementation challenges associated with change management issues. In some cases, a complete

corporate cultural change is required, which can be enormous from a process, data, analytics, and technology standpoint.

Companies that attempt to navigate a demand-driven transformation process must tackle corporate cultural changes head-on. The

most important changes are:

Incentives: The role of the commercial teams. As long as sales is incented only for volume sold into the channel and

marketing only for market share, companies will never become demand driven. To make the transition to demand-driven,

companies must focus on profitable sales growth through the channel.

Traditional view of supply chain excellence. For demand-driven initiatives to succeed, they must extend from the

customer's customer to the supplier's supplier. In 92 percent of companies surveyed in Fall 2010, supply chain models

encompassed only deliver and make. Customer and supplier initiatives usually are managed in separate initiatives largely 6

driven by cost.

Leadership. The concepts of demand latency, demand sensing, demand shaping, demand translation, and demand

orchestration are not widely understood. As a result, they are not included in the definition of corporate strategy.

Focus: Inside out, not outside in. Process focus is from the inside of the organization out, as opposed from the outside

(market driven) back. In demand-driven processes, the design of the processes is from the market back, based on sensing

and shaping demand.

Vertical rewards versus horizontal processes. In supply-based organizations, the supply chain is incented based on cost

reduction, procurement is incented based on the lowest purchased cost, distribution/logistics is rewarded for on-time

shipments with the lowest costs, sales is rewarded for sell-in of volume into the channel, and marketing is rewarded for

market share. These incentives cannot be aligned to maximize true value.

Focus on transactions not relationships. Today, the connecting processes of the enterprise—selling and purchasing—are

focused on transactional efficiency. As a result, the greater value that can happen through relationships—acceleration of

time to market through innovation, breakthrough thinking in sustainability, and sharing of demand data—never

materializes.

The demand-driven value network implementations are not a traditional approach of adding ERP + Advanced Planning and

Scheduling /Customer Relationship Management + Supplier Relationship Management, and shake until well blended. In fact,

some of the most demand-driven companies have legacy systems. Instead, the focus is on:

Process. The implementation requires a focus on the processes: revenue management, new product launch, channel data

management, and use of demand insights.

Network design. The design of the network is an essential element to actualizing this strategy. Demand-driven companies

have deep investments in supply chain modeling software—optimization and simulation—and actively model scenarios

for the network reflecting changes in both demand and supply.

Sensing. These companies also have a control tower to actively sense network changes and adapt the network for changes

in market demand, constraints, and opportunities. This overarching group crosses source, make, deliver, and sell to work

hand-in-hand with customer service to maximize the use of resources while minimizing costs and maximizing

profitability.

So, does this mean that we give up on demand-driven concepts? The answer is emphatically . It is the right concept, but itno

will take more time and investment in process, analytics, and technology.

Key Points

Intense market volatility and fragmentation are compelling companies to develop and deploy more integrated, focused,

and demand-driven processes and technologies to achieve best-in-class performance.

Demand management is all about dynamic marketplace choices to drive profitability in the existing and emerging winning

segments.

Market uncertainty requires next-generation right-time demand forecasting. Relying only on supply responsiveness is a

recipe for failure.

Implementing a demand-driven value network in support of new demand-driven management requires investment in

process, analytics, and technology.

SUMMARY Traditional demand management processes lack the visibility necessary to manage today's demand volatility. This results in

increased margin pressures and poor service or bloated inventories impacting cash flow and working capital. Shipment history

alone is no longer a predictor of the future, and companies can no longer afford to rely on historical forecast methods (e.g.,

exponential smoothing) to translate demand. Those that do rely merely on historical forecast methods risk not competing in

tomorrow's marketplace and will lack the differentiation necessary to compete effectively in the marketplace. Companies will

need to move beyond traditional/historical practices of forecasting demand to a demand-driven structured process. Those

companies that embrace demand-driven forecasting will surpass the competition and gain a real competitive advantage in the

marketplace.

Demand-driven forecasting is the use of data from market and channel sources to sense, forecast, and shape future demand

and to translate demand requirements into an actionable supply plan. Demand forecasting needs to be an unconstrained view or

best estimate of market demand, primarily based on a holistic assessment that combines downstream data (POS/syndicated

scanner) with shipment and order information. Demand shaping uses sales and marketing programs, such as price, new product

launch (NPL), trade and sales incentives, promotions, and marketing programs, to influence what and how much consumers will

purchase.

Demand and are common terms that have been loosely used for years. The most common definition issensing shaping

associated with the CPG industry. Demand sensing, especially in recent years, has come to signify using granular upstream

shipments (replenishment) data to refine short-term forecasts and inventory positioning. This is a supply-centric view of demand

sensing. Demand shaping is often described as demand shifting, rather than true demand shaping using price, sales promotions,

marketing events, and other related factors to influence future unconstrained demand.

Implementing the right combination of demand sensing, shaping, and forecasting activities will be critical to companies'

future success. The message is clear: Corporate leaders understand that they must balance demand-sensing and shaping activities

within the demand management process based on product characteristics across the organization to optimize demand planning

excellence. These same leaders also recognize the importance of working together with sales and marketing and the supply chain

to identify the appropriate sensing and shaping response to optimize business results.

Leading companies that have built a demand-driven, pull-based forecasting process are redefining data, analytics, and

collaborative practices to drive more value in their organization, as well as with their key trading partners, based on shared

demand visibility.

NOTES 1. Robert Fildes and Paul Goodwin, “Good and Bad Judgment in Forecasting: Lessons from Four Companies,” Foresight: The

(Fall 2007): 5–10.International Journal of Applied Forecasting

2. Berry Gilleon and Michael Shea, Supply Chain Management Review, “The Powerful Potential of Demand Management”,

May-June 2011, 18–27.

3. Ibid.

4. Nielsen Company: ; IRI: ; Intercontinental Marketing Services: www.nielsen.com www.infores.com www.imshealth.com

5. Lora Cecere, “What Happened to the Concept of Demand-Driven?,” Supply Chain Shaman( ), www.supplychainshaman.com

January 12, 2011

6. Lora Cecere and Charles Chase, Bricks Matter: The Role of Supply Chains in Building Market-Driven Differentiation

(Hoboken, NJ: John Wiley & Sons, 2012), 109–146.

7. “Leveraging Customer Demand Signals,” Consumer Goods Technology ( ), January 2012, http://consumergoods.edgl.com

28–29.