Guia practica y elemental sobre Planificación de la Demanda

1. 40 Supply Chain Management Review March/April 2013 www.scmr.com www.scmr.com Supply Chain Management Review March/April 2013 41
APractitioner’s
GuidetoDemand
Planning
By Lora Cecere
Lora Cecere (lora.cecere@
supplychaininsights.com) is the founder
of Supply Chain Insights, a research
and advisory firm specializing in supply
chain management. She is co-author with
Charles W. Chase Jr. of the new book
Bricks Matter (John Wiley & Sons, 2013).
Effective demand planning
doesn’t just happen, it requires
work.To move forward,
companies have to admit the
mistakes of the past, implement
continuous improvement
programs to drive discipline,
and carefully re-implement
demand planning technologies
to sense and shape demand.
Here’s a guide to making sound
demand planning a reality.
organizational improvement was on specific functional
areas such as manufacturing, procurement, or logistics.
This “siloed” approach gradually gave way to more inte-
grated operations, which lead to the concepts of demand
planning and integrated supply chain planning.
In this article, demand planning is defined as the use
of analytics—optimization, text mining, and collaborative
workflow—to use market signals (channel sales, customer
orders, customer shipments, or market indicators) to
predict future demand patterns. This forward period for
demand planning will vary by company, but it is a tactical
planning process typically stretching across the period of
10 months to 18 months. Note that as companies mature,
the use of the forecast becomes more comprehensive
and is woven into a number of processes culminating
in a more holistic end-to-end process termed demand
W
ithin most organizations, the words
“demand planning” cause a reaction—and
typically not a mild one. It is character-
ized by emotional extremes like anger,
despair, disillusionment, or even hopeless-
ness. Seldom do we find a team excited or
optimistic about their chances to improve
demand planning processes.
After two decades of process and technology refinement,
excellence in demand management still eludes supply chain
teams. In fact, it is the supply chain planning application with
the greatest gap between performance and satisfaction. At the
same time, it’s the application with the greatest planned future
spending. For most teams, demand planning is a conundrum, a
true love-hate relationship. They want to improve the demand
planning process, but remain skeptical that they can ever do so.
In our research at Supply Chain Insights, we find that
demand planning is the most misunderstood—and most frus-
trating—of any supply chain planning application. While com-
panies are the most satisfied with warehouse and transportation
management, they are the least satisfied with demand planning.
Teams are also confused about the demand planning pro-
cess. They are unclear on how to move forward. What drives
process excellence is not clear. And well-intentioned consultants
brought in to help achieve that clarity often give bad advice. In
this article, we share insights on the current state of demand
planning and give actionable advice that supply chain teams can
implement to make real improvements.
Getting Past the Plateau
The first use of the term supply chain management in the
commercial sector was in 1982. Until that time, the focus of
BALANCE EFFICIENCY COLLABORATION LANGUAGE SIGNALS
Matt Herring

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Demand
plexity. Each supply chain has a unique potential based
on the trade-offs of these factors. As a result, compa-
nies cannot make improvements in operating mar-
gin without affecting inventories unless they improve
the supply chain potential. One of the most effective
ways to increase the potential of the supply chain is to
improve the demand signal.
The challenge of getting past this plateau is as impor-
tant as it is formidable. Supply chains are becoming
more complex. As products proliferate, channels become
more specialized and as companies span global geogra-
phies, demand planning principles grow in importance.
Organizational design, process design, and how the data
is used in demand processes all play a major role in
defining the differences between leaders and laggards.
An important factor in making demand planning
progress is the design of reporting relationships. Based
on our work with more than 300 companies, we have
concluded that a reporting relationship to a central
group or a supply chain center of excellence gives com-
panies an advantage in getting past the plateau. By con-
trast, companies with reporting relationships to the sales
organization tend to post the worst results in demand
planning. These organizations are plagued by high, and
often uncontrolled, bias. Reporting relationships to mar-
keting and manufacturing are similarly problematic. The
bias in these relationships is not as high as it is with
sales reporting, but it is higher than in organizations with
reporting relationships to a neutral, cross-functional ana-
lytics group.
Our research shows that
64 percent of companies
today have a supply chain
center of excellence. As
shown in Exhibit 2, these
teams take responsibil-
ity for global planning and
global/regional governance
of demand insights and data
integrity for the organization.
Data governance in
design of the global supply
chain is another key suc-
cess factor. Often there
are demand teams in the
dispersed regions send-
ing market signals into the
plan and global teams at
corporate that are gather-
ing information on glob-
al programs. Both are
important, but they need
to know how to work together. This does not happen by
chance; it requires clear and careful design. Corporate
organizational design needs to carefully architect the rela-
tionships between global planning and regional execution.
While form should follow function, a clear understanding
of supply chain basics is essential. Too often, companies
overlook this important design element of demand data
governance and the roles of regional/global governance.
Demand Volatility: A Rising Concern
As shown in Exhibit 3 (top 10 pain points shown), sup-
ply chain leaders are becoming increasingly concerned
over demand volatility. Global market expansion and
specific regional needs heightens this concern. Further,
the proliferation of products and the changing needs of
customers make it more critical than ever to sense mar-
ket demand and quickly translate the requirements into
the supply chain response. The “forecastability” (ability to
use optimization techniques to improve forecast accuracy
numbers) of products is getting more difficult. Demand
volatility is growing, demand data is becoming more com-
plex, and the usual responses are less effective.
The traditional supply chain design is unable to
sense and adapt to rising demand volatility. Demand
management systems were designed to optimize demand
signals periodically (weekly or monthly) from order or
shipment data. Order data, by definition, carries latency.
This latency can be substantial—from one week to five
weeks—based on the time that it takes to roll up channel
pull into minimal order quantities across the channel.
management. (We dis-
cuss this more fully in the
accompanying sidebar.)
The first demand plan-
ning applications were intro-
duced late in the 1980s.
Today, there is a convention-
al view that as these appli-
cations evolved, companies
have steadily reduced costs,
improved inventories, and
sped time to market. The
actual balance sheet results,
however, show the opposite.
Too few supply chain teams
have successfully posted
improvements to their bal-
ance sheets through demand planning initiatives.
Improvements were made in specific projects and in
isolated parts of the business, but progress has slowed
over the last 10 years resulting in a supply chain plateau.
Growth has been slowing, inventories climbing, and
costs escalating. Getting the basics right in the demand
planning process is essential to moving supply chain
results past the current plateau. However, too few com-
panies know what to do or how to do it.
Evidence of this plateau is shown in Exhibit 1. In the
process manufacturing sectors shown, where demand plan-
ning should make a dramatic difference, companies are
going backward not forward in driving meaningful results.
Ironically, most companies do not realize that they have
reached a supply chain plateau because they have not
looked at year-over-year financial balance sheet results.
The supply chain is a complex system that has
grown even more complex over the decade. Most com-
panies understand that it is complex, but they do not
see it as a complex system. In a complex system there
are finite trade-offs between areas. In the supply chain,
these trade-offs include growth, costs, cycles, and com-
The concept of demand manage-
ment includes demand sensing,
demand shaping, demand translation,
and demand orchestration throughout
the value network.
Demand Sensing: Shortening
the time to sense “true” market data
to understand “true” market shifts in
the demand response. This is in con-
trast to using order-to-shipment data
that can have 1-3 weeks latency in
translating “true” market (or channel)
demand to action.
Demand Shaping: Applying
techniques to stimulate market
demand. This includes new product
launch, price and revenue manage-
ment, assortment, merchandising,
placement, sales incentives, and mar-
keting programs. These techniques
to lift demand are seldom deployed
singularly. Instead, they are usually
deployed into the market together.
Demand Translation: Translating
demand outside-in from the market
to each role within the organization.
The system design recognizes that the
requirements for each—distribution,
manufacturing and procurement—
are different. In this process, the fore-
cast is based on “the selling unit” into
the channel with “ship-to modeling.”
The demand is then translated to
“ship-from” views based on the needs
of the specific role.
Demand Orchestration: Making
trade-offs market-to-market based
on the right balance of demand risk
and opportunity. These trade-off deci-
sions depend on the use of advanced
analytics to sense and shape demand
simultaneously.
As companies mature, they must
actively manage demand signals. In
other words, they need to shape not
shift demand. Practices like shifting
demand from one period to another
through advanced shipments or mov-
ing more products into the channel
without stimulating base demand
lead to supply chain waste.
The movement from demand
planning to a more holistic process
for demand management is depen-
dent on the organization’s maturity
in building demand-driven or mar-
ket-driven value networks. No matter
what the maturity level of the sup-
ply chain organization, the starting
place to improve its potential is to
reduce the bias and error in the tacti-
cal forecast. Demand planning is the
foundation.
More on Demand Management
EXHIBIT 1
Trends in Supply Chain Financial Ratios
2000-2011
Source: Supply Chain Insights LLC, Corporate, Annual Reports (2000-2011)
Negative Values: RED
Chemical
Consumer Packaged Goods (CPG)
Food
Pharmaceutical
Industry
0.09
0.16
0.15
0.24
Average
Operating
Margin
1%
2%
1%
4%
Operating
Margin
1%
0%
1%
1%
SG&A
Margin
2%
2%
2%
6%
Return on
Assets
16%
29%
29%
46%
Revenue per
Employee (K$)
Average Changes in Financial Ratios over the Period
EXHIBIT 2
Role of the Supply Chain Center of Excellence
Functions of Center of Excellence (Average Number: 5.9)
Source: Supply Chain Insights LLC, Cross-Survey Analysis 2012 (Mar.-Dec. 2012)
Definition of Best Practices for Process
Definition of Supply Chain Metrics
Supply Chain Planning
Inventory Strategies
Network Design
Facilitation of Horizontal Processes,like S&OP
Evaluation of New Technologies
Establishment of Goals
Supply Chain Strategy
Supplier Development
84%
79%
67%
66%
56%
55%
53%
52%
44%
23%

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Demand
ning technologies and improve forecasting processes,
but not improve their supply chain results. The issue is
the lack of training on how to “use the better forecast
signal.” Most supply chain teams do not know how to
use the forecast effectively. Instead of trying to correct
inaccurate data and focus on a finite number, the teams
need to use the probability of demand in their network
design and supply planning models. This takes training.
The adoption of CPFR: Collaborative Planning
Forecasting and Replenishment, or CPFR, saw its greatest
adoption in the consumer packaged goods industry. The idea
was for manufacturers to collaborate with their retail partners
on the building of a demand plan for the extended network.
CPFR was designed to align the manufacturer’s demand
plan to the retailer’s and reduce the bullwhip effect. The
assumption was that the retailer’s forecast would provide bet-
ter insights. The problem, however, was that the maturity of
the retailer forecast was never considered. The reality is that
most retailers have poor forecasts, and the CPFR process
never accounted for the inherent bias and error in their fore-
casts. There’s really only one retailer that measures forecast
accuracy and accounts for bias and error: Walmart.
Step 2:
Buy the Right Demand Planning System
Our research shows that demand planning, ERP
(Enterprise Resource Planning), and order management
rate as the most important
supply chain technologies
to supply chain leaders.
However, these areas also
show wide gaps between
system importance and cur-
rent level of satisfaction (see
Exhibit 4). Ironically, they
are the technologies with
the greatest planned spend-
ing, too. Of all of the supply
chain planning applications,
success in demand planning
remains the most elusive.
And despite the evolution of
technology capabilities for
in-memory processing, cloud-
based analytics, and deeper
optimization, very little has
changed in most demand
planning applications.
Consultants will often
defer to the ERP system
demand planning technol-
ogy in helping companies
with the selection processes,
using the rationale that “80 percent fit of the solution is
sufficient.” But in reality this is the exception, not the
rule. Unfortunately, most consultants have a limited
view of what is available and are incented to implement
the “most expensive” vs. the “best” solutions. As a result,
the technologies with the best data model fit are seldom
considered because they are less-expensive than best-of-
breed solutions.
To ensure that you select the right technology for your
operation, it is critical to map the demand streams and the
demand drivers. Features like causality, seasonality, tops-
down and bottoms-up forecasting, and forecast-value add
analysis are essential to the selection of the technology.
Step 3:
Carefully Implement
So how do we move forward and close the gap? It starts
with the implementation. Companies that are the most
successful in demand planning tune the optimization
engines through a series of conference room pilots to
drive the highest forecast accuracy. The goal here is
not to accelerate the speed of project implementation.
Instead, it should be to drive the best results by focusing
on the following:
A well-designed pilot. To implement the demand
management system correctly, take two years to three
years of shipment, order, and channel data and use the
In short, the traditional supply chain is designed
to respond—not to sense. As market opportunities
change, this design cannot flex and adapt; the com-
panies are not well-suited for periods of high demand
volatility. Creating an adaptable system that can sense
and respond—and better manage volatility—requires
a radical departure from traditional design approaches.
The system needs to be designed from the outside-in. It
needs to embrace the concepts of demand management.
This includes sensing channel demand; using optimiza-
tion to actively shape demand; and applying advanced
analytics used to drive an intelligent response. At a foun-
dational level, demand planning, and the reduction of
bias and error, is important. This is a radical departure
from the design approach of the last decade.
So, how do companies get started? How do they overcome
the challenges? Based on our experience working with a range
of companies, we believe that it is a three-step process.
Step 1: Face the Mistakes Made in the Past
The first step in combating demand volatility is to admit
the mistakes of the past. The problems associated with
faulty technology implementations are compounded by
the adoption of bad practices. In this journey to sense
and shape and use demand information to drive a more
profitable response, leaders need to confront mistakes
made in the design and adoption of demand processes
over the course of the last decade. The most important
of these mistakes include the following:
One-number forecasting. Well-intentioned con-
sultants tout the concept of one-number forecasting.
Eager executives drink the magic elixir. Problem is, they
realize all too late that the one-number mantra actually
increases—not decreases—
forecast error. The reason:
It’s too simplistic, something
the one-number advocates
fail to understand.
A demand plan is hier-
archical around products,
time, geographies, chan-
nels, and attributes. It is a
complex set of role-based,
time-phased data. Within
this context, a one-number
thought process is naïve.
An effective demand plan
has many numbers that are
tied together in an effective
data model for role-based
planning (that is, based on
defined roles across the
organization including sales,
marketing, and supply chain) and what-if analysis.
A one-number plan is too constraining for the organi-
zation. So instead of one number, the focus needs to be a
common plan with marketing, sales, financial, and sup-
ply chain views and agreement on market assumptions.
Achieving this level of agreement requires the use of an
advanced forecasting technology and the design of the
system to deliver role-based views. This can only be found
in the more advanced forecasting systems.
Consensus planning. Over the last 10 years, the con-
cept of consensus planning has taken hold in many orga-
nizations. It’s based on the belief that each organization
within the company can add insight to make the demand
plan better. The concept is correct; but in most instances,
the implementation has been flawed. The issue is that
most companies did not hold groups within the organiza-
tion accountable for bias and error. Each group has its own
natural bias and error based on incentives. So unless there
is discipline around the incentive structure, consensus fore-
casting will distort the forecast and add error despite well-
intended efforts to improve the demand planning process.
Forecasting what to make vs. the channel
demands. The traditional technique is to forecast what
manufacturing should make as opposed to forecasting
what is selling in the channel. Contrary to what many
think, this is not a trivial difference. Forecasting channel
demand reduces demand latency and gives the organiza-
tion a more current signal. It also allows for augmenting
the forecast with demand insights to improve forecast
quality. For most companies, this requires a reconfigura-
tion of the demand planning software.
Rewarding the urgent vs. the important. Time
after time, we see companies implement demand plan-
EXHIBIT 3
Top Ten Pain Points of Supply Chain Leaders
Source: Supply Chain Insights LLC,Voice (Wave 2: Oct.-Dec. 2012)
Lack of Supply Chain Visibility
Demand Volatility
Supply Chain Complexity
Rising Commodity Prices
Data Quality Issues
Product Proliferation
Talent Shortage
Sustained Production Reliability
Compliance and Legislation
Globalization Issues
78%
75%
70%
50%
45%
45%
38%
33%
28%
25%
Top
Pain
Points
EXHIBIT 4
IT System Importance vs.Satisfaction
Enterprise Resource Planning
Order Management
74%
23%
74%
31%
62%
35%
59%
22%
59%
24%
58%
30%
53%
17%
53%
35%
-51%
-44%
-27%
-38%
-35%
-27%
-37%
-18%
Source: Supply Chain Insights LLC,Voice (Wave 2: Oct.-Dec. 2012)
Importance Satisfaction Gap Between Satisfaction
and Importance
Demand Planning
Production Planning
Manufacturing Execution Systems
Warehouse Management
Tactical Supply Planning
Transportation Planning
Most Important but Greatest Gap

4. 46 Supply Chain Management Review March/April 2013 www.scmr.com
Demand
historic data to forecast a known outcome of the lat-
est year. (For example, tune a model using 2009, 2010,
and 2011 data to forecast actual output in 2012. Keep
fine tuning the model to close the gap between the pre-
dicted and actual results for 2012.) To fine-tune the
models, divide the data into demand streams and work
each demand flow for refinement. Examples include
slow-moving seasonal items, fast-moving turn volume
products, new product introductions, special packaging
items, and special promotions.
The best demand planning implemen-
tations take time. The foundation of this
effort is a carefully crafted pilot to identify
the right market drivers and test the optimi-
zation engines.Many companies make the
mistake of rushing the demand planning
implementation and not aligning the tech-
nologies to get the best results.
Regular tune-ups. Just as a car requires frequent tune-
ups, so does the demand planning implementation. This is
not a technology implementation project that can be installed
and then forgotten. The most successful companies achieve
superior results through yearly tune-ups. These include the
cleansing of data, the alignment of planning master data, and
the fine-tuning of demand optimization engines.
A demand planning team. Another stumbling
block centers on the training of the teams that maintain
the demand planning systems. A frequent mistake is
to staff the demand planning team with junior analysts
and use the assignment as a stepping stone for greater
accountability. The companies with the greatest success
with demand planning implementations staff the group
with senior people that know the business. Further, they
reward the team members with progressive assignments
for their work. For the great majority of demand planning
positions, internal training will be required. There are
not enough trained professionals in this field available
for the number of open positions. This is a critical talent
management challenge.
A mindset change. Many supply chain profes-
sionals have given up hope that they will ever be able
to improve the demand forecast. They have witnessed
project after project to reduce forecast error and improve
inventory accuracy with no results. But progress can, in
fact, be made, by carefully articulating the path forward
to drive continuous improvement in the demand plan-
ning process and by working with supply chain teams
on how to better use the forecast. Time after time, we
see companies improve their forecast, but fail to teach
the supply chain team how to use the improved forecast.
Effecting the needed mindset change is basically a mat-
ter of education.
Bias and error reduction. The use of forecast value-
add (FVA) analysis helps companies add discipline to the
demand management process to drive continuous improve-
ment. This is best described by Mike Gilliland in his book
The Business Forecasting Deal: Exposing Myths, Eliminating
Bad Practices, Providing Practical Solutions (Wiley and SAS
Business Series). Through the use of FVA, the steps to
develop the demand plan are carefully examined to under-
stand if the process is improving or degrading the forecast.
Process redesign. To reduce demand volatil-
ity, design the process outside-in from the chan-
nel back. Focus on sensing what is being sold in
the channel. This is a radical departure from the
traditional manufacturing process of forecasting
what a company needs to manufacture. The key
steps of this design approach are:
by mapping the demand signals outside-in from the channel
back. In the conference room pilot, validate which market
signals are critical and useful for modeling. Focus first on
market data, and then on the clean-up of enterprise data.
the channel back: After mapping the market signals, build
a demand model to forecast the channel. Focus on mod-
eling the “ship-to” locations. Reduce demand latency to
sense market variations by building strong demand trans-
lation capabilities.
many companies focus on getting very specific on
demand numbers, focus instead on the probability of
demand and the understanding of demand patterns. Put
another way, “learn how to dance with gray.”
The Keys to Moving Forward
The world of demand planning has changed. The busi-
ness requirements have escalated and it matters more
than ever. To move forward, companies have to admit the
mistakes of the past, implement continuous improve-
ment programs to drive discipline, and carefully re-
implement demand planning technologies to sense and
shape demand.
The evolution to demand excellence needs to be built
on a program of continuous improvement focused on
forecast-value added techniques to reduce bias and error.
Effective demand planning doesn’t just happen, it requires
work. It is contingent on the ability to build the right teams,
and bring a new level of excitement and open mindedness
to driving a demand signal. Those that do it best are com-
fortable dancing in the world of gray where each day offers
a new opportunity and where demand holds value. ࠗࠗࠗ