Description
Sales forecasting is a management function that companies often fail to recognize as a key contributor to corporate success. From a top-line perspective, accurate sales forecasts allow a company to provide high levels of customer service.
Seven Keys to Better
Forecasting
Mark A. Moon, John T. Mentzer, Carlo D. Smith, and Michael S. Garver
44
S
ales forecast-
ing is a man-
agement
function that compa-
nies often fail to
recognize as a key
contributor to cor-
porate success. From
a top-line perspec-
tive, accurate sales
forecasts allow a
company to provide
high levels of customer service. When demand
can be predicted accurately, it can be met in a
timely and efficient manner, keeping both chan-
nel partners and final customers satisfied. Accu-
rate forecasts help a company avoid lost sales or
stock-out situations, and prevent customers from
going to competitors.
At the bottom line, the effect of accurate
forecasts can be profound. Raw materials and
component parts can be purchased much more
cost-effectively when last minute, spot market
purchases can be avoided. Such expenses can be
eliminated by accurately forecasting production
needs. Similarly, logistical services can be ob-
tained at a much lower cost through long-term
contracts rather than through spot market ar-
rangements. However, these contracts can only
work when demand can be predicted accurately.
Perhaps most important, accurate forecasting can
have a profound impact on a company's inven-
tory levels. In a sense, inventory exists to provide
a buffer for inaccurate forecasts. Thus, the more
accurate the forecasts, the less inventory that
needs to be carried, with all the well-understood
cost savings that brings.
The ultimate effects of sales forecasting ex-
cellence can be dramatic. Mentzer and Schroeter
(1993) describe how Brake Parts, Inc., a manufac-
turer of automotive aftermarket parts, improved
its bottom line by $6 million per month after
Excellence in sales
forecasting can boost
a firm 's financial health
and gratify customers
and employees alike,
launching a company-wide effort to improve
sales forecasting effectiveness. Nevertheless, firms
often fail to recognize the importance of this
critical management function. Our objective here
is to take what we've learned about sales fore-
casting from working with hundreds of compa-
nies, and summarize that learning into seven key
focus points (summarized in Figure 1) that will
help any company improve its forecasting perfor-
mance. Although no management function can
be reduced to seven keys, or 70 keys for that
matter, our hope is that the ideas presented here
will inspire senior management to look closely at
their own sales forecasting practices and recog-
nize opportunities for improvement.
Key #1: Understand What Forecasting Is,
and What It Is Not
The first and perhaps most important key to bet-
ter forecasting is a complete understanding of
what it actually is and-of equal importance-
what it is not. Sales forecasting is a management
process, not a computer program. This distinction
is important because it affects so many areas
across an organization. Regardless of whether a
company sells goods or services, it must have a
clear picture of how many of those goods or
services it can sell, in both the short and long
terms. That way, it can plan to have an adequate
supply to meet customer demand.
Forecasting is critical to a company's produc-
tion or operations department. Adequate materi-
als must be obtained at the lowest possible price;
adequate production facilities must be provided
at the lowest possible cost; adequate labor must
be hired and trained at the lowest possible cost;
and adequate logistics services must be used to
avoid bottlenecks in moving products from pro-
ducers to consumers. None of these fundamental
business functions can be performed effectively
without accurate sales forecasts.
Business Horizons /September-October 1998
Many companies consider the most important
decisions about forecasting to revolve around the
(
selection or development of computer software
for preparing the forecasts. They have adopted
the overly simplistic belief that "If we've got good
software, we'll have good forecasting." Our re-
search team, however, has observed numerous
instances of sophisticated computer systems put
into place, costing enormous amounts of time
and money, that have failed to deliver accurate
forecasts. This is because system implementation
has not been accompanied by effective manage-
ment to monitor and control the forecasting pro-
cess.
One company we worked with has an excel-
lent computer system with impressive capabilities
of performing sophisticated statistical modeling of
seasonality and other trends. However, the sales-
people, who are the originators of the forecast,
use none of these tools because they do not un-
Figure 1
The Seven Keys to Better Forecasting
Seven Keys to Better Forecasting
4 5
Keys Issues and Symptoms Ac ions Results
Understand what
forecasting is
and is not.
•
Computer system as focus,
rather than management
processes and controls
•
Blurring of the distinction
between forecasts, plans, and
goals
•
Establish forecasting group
•
Implement management
control systems before
selecting forecasting software
•
Derive plans from forecasts
•
Distinguish between forecasts
and goals
•
An environment in which
forecasting is acknowledged
as a critical business function
•
Accuracy emphasized and
game-playing minimized
Forecast demand,
plan supply.
•
Shipment history as the basis
for forecasting demand
•
"Too accurate" forecasts
•
Identify sources of information
•
Build systems to capture key
demand data
•
Improved capital planning
and customer service
Communicate,
cooperate,
collaborate.
•
Duplication of forecasting
effort
•
Mistrust of the "official"
forecast
•
Little understanding of the
impact throughout the firm
•
Establish cross-functional
approach to forecasting
•
Establish independent forecast
group that sponsors cross-
functional collaboration
•
All relevant information used
to generate forecasts
•
Forecasts trusted by users
•
Islands of analysis eliminated
•
More accurate and relevant
forecasts
Eliminate islands
of analysis.
•
Mistrust and inadequate infor-
mation leading different users
to create their own forecasts
•
Build a single "forecasting
infrastructure"
•
Provide training for both users
and developers of forecasts
•
More accurate, relevant, and
credible forecasts
•
Optimized investments in
information/communication
systems.
Use tools wisely. •
Relying solely on qualitative
or quantitative methods
•
Cost/benefit of additional
information
•
Integrate quantitative and
qualitative methods
•
Identify sources of improved
accuracy and increased error
•
Provide instruction
•
Process improvement in
efficiency and effectiveness
Make it important. •
No accountability for poor
forecasts
•
Developers not understanding
how forecasts are used
•
Training developers to under-
stand implications of poor
forecasts
•
Include forecast performance
in individual performance
plans and reward systems
•
Developers taking forecasts
seriously
•
A striving for accuracy
•
More accuracy and credibility
Measure, measure,
measure.
•
Not knowing if the firm is
getting better
m Accuracy not measured at
relevant levels of aggregation
•
Inability to isolate sources of
forecast error
•
Establish multidimensional
metrics
•
Incorporate multilevel
measures
•
Measure accuracy whenever
and wherever forecasts are
adjusted
•
Forecast performance can be
included in individual perfor-
mance plans
•
Sources of errors can be
isolated and targeted for
improvement
•
Greater confidence in forecast
process
46
REs
The ideas presented in this article are drawn from a program of re
that has spanned more than 15 years. Phase 1 began in 1982 with
survey of 157 companies that explored the techniques they usedd to
cast. Ten years later, under the sponsorship of AT&T Network Systems,
the survey was replicated and expanded in Phase 2 to explore not only
techniques but also the systems and management processes used by
companies. Phase
3,
conducted between 1994 and 1996, was sponso
by a consortium of companies consisting of AT&T Network -Systems,•
Andersen, Consulting, Anheuser-Busch, and Pillsbury. It took the form' of a
benchmarking study that consisted of in-depth analysis of 20 companies:'.
Anheuser-Busch, Becton-Dickinson, Coca-Cola, Colgate-Palmolive,
Fed
-
eral Express, Kimberly Clark, Lykes Pasco, Nabisco, JCPenney,
Pillsbury„
ProSource, Reckitt Colman, Red Lobster, RJR Tobacco, Sandoz, Schering
Plough, Sysco, Tropicana, Warner Lambert, and Westwood Squibb.
Finally,' -
Phase 4 has been conducted since 1996 and consists of a series of fore-
cast audits: The forecasting practices of seven companies-Allied Signal,
Du Pont Agricultural Products Canada, Eastman Chemical, Hershey
Foods
USA, Lucent Technologies, Michelin North America, and Union Pacific ..
Railroad have been studied so far in this phase and compared to those
of the 20 benchmarked companies. The references to this research are
listed at the end of this article.
ormal studies, the learning
nted by more than 20 years of
g with a large number of major
Rg casting systems and processes.
firsthand observation of what
sting in companies, large;
d services and that are "
or retailers.
In additign io
presented here i
experience to c
corporation.
Our ideas
actually
and s
derstand them and have no confidence
in the
numbers generated. As a result, their forecasts are
based solely on qualitative factors and are often
very inaccurate. A similar case is a technology-
based company that has created another highly
sophisticated forecasting tool, yet the salespeople
continue to underforecast significantly because
their forecasts have a direct effect on their sales
quotas. Both of these examples show how some
companies focus on forecasting systems rather
than forecasting management.
On the other hand, some companies have
been more successful in their efforts by
recogniz-
ing the importance of forecasting as a manage-
ment process. Some have organized independent
groups or departments that are responsible for
the entire forecasting process, both short- and
long-term. One large chemical company has
formed a forecasting group not associated with
either marketing or production. It has ownership
and accountability for all aspects of forecasting
management, with responsibility not only for the
systems used to forecast but also for the numbers
themselves. The group accomplishes its mission
in several ways: providing training in the meth-
ods and processes throughout the company; de-
signing compensation systems that reward fore-
cast accuracy; and facilitating communication
among sales, marketing, finance, and production
departments, thereby improving overall forecast-
ing effectiveness. Recognizing the importance of
forecasting, this firm has put an organization in
place to manage the process, not just to choose
and manage a system.
Another way in which companies confuse
what forecasts are and what they are not is by
failing to understand the relationship between
forecasting, planning, and goal setting. A sales
forecast should be viewed as an estimate of what
future sales might be, given certain environmen-
tal conditions. A sales plan should be seen as a
management decision or commitment to what the
company will do during the planning period. A
sales goal should be a target that everyone in the
organization strives to attain and exceed.
Each of these numbers serves a different
purpose. The primary purpose of the sales fore-
cast is to help management formulate its sales
plan and other related business plans-its com-
mitment to future activity. The sales plan's pur-
pose is to drive numerous tactical and strategic
management decisions (raw material purchases,
human resource planning, logistics planning, and
so on), realistically factoring in the constraints of
the firm's resources, procedures, and systems.
The sales goal is primarily designed to provide
motivation for people throughout the organiza-
tion in meeting and exceeding corporate targets.
Whereas the sales forecast and the sales plan
should be closely linked (the former should pre-
cede and influence the latter), the sales goal may
be quite independent. The objective of those
who receive a sales goal should be to beat that
goal. It can be developed based on a sales fore-
cast, plan, and motivation levels. However, be-
cause forecasters should strive for accuracy, it is
not appropriate for a forecast to be confused
with the firm's motivational strategy.
It is particularly problematic when sales fore-
casts and sales goals are intertwined, because this
mixture leads to considerable game playing, es-
pecially involving the sales force. If salespeople
believe that long-term forecasts will affect the
size of the next year's quota, they will be strongly
motivated to underforecast, hoping to influence
those quotas to be low and attainable. Alterna-
tively, as one salesman at a parts supply company
put it, "It would be suicide for me to submit a
forecast that was under my targets." In both
cases, because goals and forecasts are so inter-
t
wined, salespeople are motivated to "play
games" with their forecasts. There is a built-in
disincentive to strive for accuracy.
Some companies have expressed a reluctance
to "manage to different numbers," suggesting that
when the forecast and the goals differ, it creates
confusion and lack of focus. The reaction to such
perceived confusion is to develop inaccurate
forecasts that can affect performance throughout
Business
Horizons / September-October 1998
the company. We believe the sales forecast and
the sales goal must be distinct, because the be-
haviors they are meant to influence can conflict.
Key #2: Forecast Demand, Plan Supply
One mistake many companies make is forecast-
ing their ability to supply goods or services rather
than actual customer demand. At the beginning
of the forecast cycle, it is important to create
predictions that are not constrained by the firm's
capacity to produce. Consider the forecaster for a
certain product who questions the company's
sales force and learns they could sell 1,500 units
per month. At the same time, current manufactur-
ing capacity for that product is 1,000 units per
month. If the forecaster takes that production
capacity into account when creating initial fore-
casts, and predicts 1,000 units, there is no record
of the unmet demand of 500 units per month,
and the information on where to expand manu-
facturing capacity is lost.
This problem often occurs when historical
shipments are used as the basis for generating
forecasts. Forecasting shipments will only predict
a company's previous ability to meet demand.
Suppose demand for a particular product in the
past had been 10,000 units per month, but the
supplier could only ship 7,500. Corporate history
would show shipments at 7,500 units per month,
(
thus causing this amount to be projected and
produced again the following month. The result
is twofold: the impression of an accurate forecast-
ing system, but an actually recurring unfulfilled
monthly demand of 2,500 units. Forecasting
based on shipping history only leads a company
to repeat its former mistakes of not satisfying
customer demand. Predicting actual demand
allows measurement of the disparity between
demand and supply so it can be reduced in fu-
ture periods through plans for capacity expan-
sion.
Often the symptom of this key is the attitude,
" We do a great job of forecasting. We are very
accurate, always selling close to what we fore-
cast." Notice in the previous example that the
forecast accuracy would appear very good be-
cause both the forecast and the actual sales were
7,500 units each month. The key, however, is the
failure to realize the 2,500 units in sales lost each
month because of an inability to meet demand.
In fact, the " true" demand forecasting accuracy
was not 100 percent, but only 75 percent. Fore-
casting by shipments and obtaining accurate re-
sults are often symptomatic of chronic underfore-
casting of demand.
Unfortunately, determining actual customer
demand is more difficult than predicting a com-
pany's ability to supply. Systems and processes
are needed to capture this elusive demand that
Seven Keys to Better Forecasting
was not fulfilled. Mechanisms are needed to allow
salespeople to provide valuable information about
customers who would order more if they could.
In addition, records of orders accepted but not
filled in the period demanded adds to the de-
mand versus supply level of information. Finally,
such electronic data interchange (EDI) informa-
tion as point-of-sale (POS) demand, retail inven-
tory levels, and retailer forecasts are all valuable
sources of information that help a company
move toward demand forecasting.
Although it is more difficult, forecasting true
demand will help a company make sensible,
long-term decisions that can profoundly affect its
market position. By identifying where capacity
does not meet demand forecasts, the company
has valuable information on where to expand
capacity through capital planning. Such a long-
term program of matching capacity planning to
forecasts will reduce the incidence of chronic
underforecasting and result in higher levels of
customer satisfaction.
Key #3: Communicate, Cooperate,
Collaborate
Companies that forecast most effectively consider
it critical to obtain input from people in different
functional areas, each of whom contributes rel-
evant information and insights that can improve
overall accuracy. But employees are often unable
or unwilling to work across functions to achieve
high levels of forecasting performance. To do so
requires a great deal of communication across
department boundaries, and not all communicat-
ing is equal; some companies are simply better at
it than others.
When it comes to cross-functional forecast-
i ng, we distinguish among three levels: communi-
cation, cooperation, and collaboration. Compa-
nies at lower levels of sophistication merely com-
municate. This can take the form of one-way
reports, in which one department responsible for
forecasting informs other functional areas of the
results of its efforts. With coordination, represen-
tatives from different functional groups meet to
discuss the forecast. Often, however, one area-
usually the one that " owns" the forecast-will
dominate the discussions and work to persuade
the other functions to accept the forecast it has
created.
Coordination is superior to one-way commu-
nication, because at least there is opportunity for
some dialogue. But it does not promote as effec-
tive a forecasting process as when different con-
stituencies in a company collaborate. Here, the
views of each functional area receive equal con-
sideration, and no one department dominates.
Such collaboration is most likely to occur when
management of the forecasting process resides in
47
4 8
duling production sche
department was so
distrustful of the
forecasts developed
by marketing that it
completely ignored
them and created a
whole,,. lack market
forecsting system.
an independent department instead of being part
of marketing, finance, logistics, or production.
Each area, with its unique biases and agendas,
can contribute equally to a true consensus fore-
cast.
In several companies we have worked with,
the functional area responsible for generating
forecasts-usually marketing-makes little effort
to obtain input from other affected areas, such as
production planning, operations, or logistics. A
number of negative consequences result. First,
critical information about production lead times
or capacity constraints are not taken into account
when the forecast is finalized. Because this infor-
mation is missing, forecast users have little trust
in projections they did not help develop. This
lack of trust leads to duplicated forecasting ef-
forts. In one company, the production scheduling
department was so distrustful of the forecasts
developed by marketing that it completely ig-
nored them and created a whole " black market"
forecasting system. Had a consensus-based ap-
proach been used, such nonproductive duplica-
tion of efforts could have been avoided.
A further consequence of not working cross-
functionally is a lack of understanding of the
assumptions that go into
forecasts, which leads to
further distrust. In another
company, a production
scheduler would adjust the
forecasts to take into ac-
count the seasonality she
believed was present in
the marketplace. However,
she was not aware that the
marketing department had
already accounted for that
seasonality in the informa-
tion they gave her. Had
production planning been
involved in a consensus-
based forecasting process,
the scheduler's adjust-
ments-which skewed the
have been made.
It is most important in effective forecasting to
establish a mechanism that brings people from
multiple organizational areas together in a spirit
of collaboration. Such a mechanism, often orga-
nized by an independent forecasting group, en-
sures that all relevant information is considered
before forecasts are created. One such mechanism
is in place at a national consumer products firm,
in which the forecasting group organizes and
holds regularly scheduled, half-day meetings that
bring together representatives from National Ac-
counts (sales), product management (marketing),
production planning, logistics, and finance. Each
participant comes to the meeting prepared to
forecasts-would not
discuss upcoming issues that will affect sales and
demand over the forecast period. Formal minutes
are kept to document the reasons for making
adjustments. The end product is a consensus
forecast, with numbers that its users have helped
develop. Duplicate forecasting efforts are elimi-
nated and all the parties can trust the final result:
a more accurate and relevant forecast.
Key #4: Eliminate Islands of Analysis
Islands of analysis are distinct areas within a firm
that perform similar functions. Each area main-
tains a separate process, thereby performing re-
dundant tasks and often having the same respon-
sibilities. Because islands of analysis are often
supported by independent computer systems
(which often are not electronically linked to other
systems within the firm), information contained
within the different islands is not shared between
them.
In our research, we have identified forecast-
ing islands in logistics, production planning, fi-
nance, and marketing. They have usually emerged
because of a lack of interfunctional collaboration
between units, which leads to a lack of credibility
associated with the forecast. Because the " offi-
cial" forecast generated in a particular department
may not be credible to forecast users, the latter
often take steps to implement processes and
systems to create their own forecast.
Islands of analysis are detrimental to corpo-
rate performance. Forecasts developed in this
manner are often inaccurate and inconsistent.
Because each area maintains its own forecasting
process and often its own computer system,
data-if shared at all-are shared only through
manual transfers, which are prone to human
errors. When completely separate systems are
used, the assumptions that underlie the forecasts,
such as pricing levels and marketing programs,
tend to differ from one system to the next. More-
over, each area forecasts with a unique bias,
making separate predictions inconsistent and
unusable by other areas. Redundancies generated
by separate systems cost the firm both money
and valuable personnel time and energy. Em-
ployee frustration builds up, along with an over-
all lack of confidence in the forecasting process.
To solve this problem, management must
devote attention to eliminating the factors that
encourage the development of islands of analysis.
Such a goal can be reached by establishing a
single process supported by a " forecasting infra-
structure." This process should consist of soft-
ware that communicates seamlessly with other
information systems in the firm. Appropriate tools
should include a suite of statistical techniques,
graphical programs, and an ability to capture and
report performance metrics over time. Historical
Business Horizons 11 September-October 1998
sales data can be accessed from a centrally main-
tained " data warehouse" that is electronically
available to all functional areas and provides
real-time data.
Once this forecasting infrastructure is in
place, effective training aimed at a common un-
derstanding of the process and its system should
be implemented for both users and developers.
Employees should be trained to comprehend the
overall process, each individual's role in the pro-
cess, and the importance of accurate forecasting.
They must be able to use the system effectively
and efficiently.
Once islands of analysis are eliminated, the
company can expect improved forecasting per-
formance and significant cost savings. Forecasts
will be more precise, more credible, and better
able to meet the needs of various departments.
When systems are electronically linked, the errors
that result from manual data transfers can be
avoided, and the necessary information can be
accessible to all functional areas. From a cost
perspective, a single forecasting process elimi-
nates redundant efforts within the firm, thus sav-
ing valuable employee time and other resources.
And because accuracy will be improved, all the
well-documented cost savings in areas such as
purchasing, inventory control, and logistics plan-
ning can be tracked and realized.
Key #5: Use Tools Wisely
Many companies tend to rely solely on qualitative
tools-the opinions of experienced managers
and/or salespeople-to derive forecasts, ignoring
such quantitative tools as regression and time-
series analysis. Alternatively, many companies
expect the application of quantitative tools, or the
computer packages that make use of them, to
" solve the forecasting problem." The key is that
both quantitative and qualitative tools are integral
to effective sales forecasting. To be effective,
however, they must be understood and used
wisely within the context of the firm's unique
business environment. Without understanding
where qualitative techniques, time series, and
regression do and do not work effectively, it is
impossible to analyze the costs and achieve the
benefits of implementing new forecasting tools.
One common symptom of a failure to realize
this key is the existence of detailed sales forecast-
ing processes that, when examined, reveal the
subjective judgments of managers or salespeople
as the only input used in the forecast. In other
words, the company has a quantitative sales fore-
casting process that supports only qualitative
forecasts. It relies too much on the ability of ex-
perienced personnel to translate what they know
into a forecast number, without taking into ac-
count the myriad of quantitative techniques and
Seven Keys to Better Forecasting
their ability to analyze patterns in the history of
demand.
The opposite symptom is a sales forecasting
process that performs intensive numerical analy-
sis of demand history and the factors that relate
statistically to changes in demand, but with no
qualitative information on the nature of the mar-
ket and what causes demand to change. The
company depends too much on the ability of
these techniques to determine estimates of future
demand without taking experience into account.
A variation on these symptoms is relying on a
" black box" forecasting system. This occurs when
a company has a sales forecasting computer
package, or " box," into
which historical sales data
are fed and the forecasts
come out, but no one seems
to know how it comes up
with them, or even what
techniques it uses. The com-
pany abrogates its responsi-
bility by turning the impor-
tant job of sales forecasting
over to a computer package
that nobody understands.
Using forecasting tools wisely requires know-
ing where each type of tool works well and where
it does not, then putting together a process that
uses the advantages of each in the unique context
of the firm. Salespeople who do a poor job of
turning their experience into an initial forecast
may be good at taking an initial quantitative fore-
cast and qualitatively adjusting it to improve
overall accuracy. Time series models work well in
companies that experience changing trends and
seasonal patterns, but they are of no use in deter-
mining the 'relationship between demand and
such external factors as price changes, economic
activity, or marketing efforts by the company and
its competitors. On the other hand, regression
analysis is quite effective at assessing these rela-
tionships, but not very useful in forecasting
changes in trend and seasonality.
To apply this key, a process should be imple-
mented that uses time series to forecast trend and
seasonality, regression analysis to forecast demand
relationships with external factors, and qualitative
input from salespeople, marketing, and general
management to adjust these initial quantitative
forecasts. This general recommendation must be
refined for each individual company by finding
the specific techniques that provide the most
i
mproved accuracy. Finally, key personnel in-
volved in either the quantitative or the qualitative
aspects of the forecasting process need training
in using the techniques, determining where they
work and do not work, and incorporating quali-
tative adjustments in the overall forecasting pro-
cess.
5 0
"Salespeople product
managers; and other
forecasters will see the
i mportance of the task
f salient rewards follow
as a result' of forecasting
excellence.
Key #6: Make it important
What gets measured gets rewarded, and what
gets rewarded gets done, say Mentzer and Bien-
stock (1998). This management truism is the
driver behind our final two keys. Sales forecast-
ing is often described by senior management as
an important function. But although this assess-
ment may be shared by individuals throughout
the firm, few organizations institute policies and
practices reinforcing the
notion that forecasting is
important for business
success. There is often a
gap between manage-
ment's words and their
actions. Companies fre-
quently tell those who
develop forecasts that
" forecasting is impor-
tant," but then fail to
reward them for doing
the job well or punish
them for doing it poorly.
Forecast users become
frustrated by a perceived lack of interest and
accountability for accuracy among forecasters.
Such frustration often leads them to manipulate
existing forecasts or, in the extreme case, develop
islands of analysis that duplicate forecasting ef-
forts and ignore valuable ideas.
One way to gauge how important forecasting
is to a firm is to determine how familiar users and
developers are with the entire process. Without
such familiarity, individuals involved in forecast-
ing throughout the firm have little appreciation of
the impact of their inaccuracies and are therefore
unlikely to spend the time and attention needed
to do the job well. As a result, users perceive that
forecasters are not taking the task seriously and
thus discount the value of what they produce.
A number of actions can be taken to address
this gap in forecasting importance. One way is to
give all individuals involved adequate training.
Forecast creators and users must know where
and how forecasts are used throughout the firm.
When forecasters become aware of all the down-
stream ramifications of sloppy work, the task
takes on more relevance to them. Marketing and
salespeople who typically are concerned about
forecasting only at the product or product line
level should understand that this does not pro-
vide the necessary detail for operations to plan
stock-keeping unit (SKU) production or for logis-
tics to make SKUL (by location) shipment plans.
Similarly, forecast users should be more aware of
the needs and capabilities of forecast developers.
Another action management can take is to
incorporate forecasting performance measures
into job performance evaluation criteria. Clearly.
salespeople, product managers, and other fore-
casters will see the importance of the task if sa-
lient rewards follow as a result of forecasting
excellence. Even senior managers become inter-
ested when the metrics of accuracy are worked
into their personal performance evaluations and
bonus plans.
But focusing on senior management is not
enough. One company includes forecast accuracy
as a meaningful part of the performance plans of
its senior executives, but not of those on the
" front line" who work with forecasts on a daily
basis. The job has not been made to seem impor-
tant to those who do it, with the effect that it is
still
not done very well.
This is particularly true of the people who
are typically responsible for initial forecast in-
put-the sales force. At nearly all the benchmark
and audit companies, salespeople are critically
important pieces of the forecasting puzzle. Yet in
almost all cases, the ones who develop forecasts
receive neither feedback on how well they fore-
cast nor any type of reward for doing it well.
Many agree with a salesman for a high-tech
manufacturer, who said, " My job is to sell, not
forecast." Similarly, product managers, who also
provide critical input to the forecasting process at
many companies, often consider forecasting an
extra burden that takes them away from their
" real jobs."
Key #7: Measure, Measure, Measure
Obviously, before forecasters can be rewarded
for excellence, a company must first develop
systems for measuring performance, tools for
providing feedback, and standards and targets for
what constitutes forecasting excellence. Without
the ability to effectively measure and track per-
formance, there is little opportunity to identify
whether changes in the development and appli-
cation of forecasts are contributing to, or hinder-
ing, business success. This key may be intuitive
for most business managers, yet our research has
i dentified surprisingly few companies that syste-
matically measure forecasting management per-
formance. In cases where measures have been
implemented, they are infrequently used for per-
formance assessment or to identify opportunities
for improvement.
A primary symptom indicating a lack of per-
formance measurement can be gleaned from
conversations with individuals involved in the
forecasting process. Simply asking for a measure
of forecasting accuracy typically elicits a response
of 'pretty good," " lousy," or other general de-
scriptors. In some cases, the answer may include
a number considered to be a measure of accu-
racy, such as " 75 percent," or error, such as " 25
percent." Further inquiry may indicate that the
Business Horizons / September-October 1998
source of the measure is based on a general
"feeling," estimate, or a second- or third-hand
source of information, and the respondent is
unsure of how such measures were calculated or
what level of aggregation was used.
In cases where measures are collected and
documented, there may still be insufficient detail
or little realization as to how they can help iden-
tify opportunities for forecasting improvement.
Generally we have found that even when accu-
racy has been measured over time, few individu-
als who contribute to forecast development re-
view the history and can determine whether their
performance has improved, remained constant,
or deteriorated. This reflects a complacency to-
ward performance measures when such mea-
sures are not used to evaluate a person's job
performance, or do not provide support for iden-
tifying sources of forecasting error.
Effective measures evaluate accuracy at dif-
ferent levels of aggregation. Logistics operations
are interested in forecast performance at the
SKUL level; sales managers may be more inter-
ested in a forecast stated in dollars and at the
territory or product line level of aggregation.
Performance metrics should support these vari-
ous units of measure as well as the aggregation
of demand at different levels.
It is also important to track accuracy at each
point at which forecasts may be adjusted. As an
illustration, the forecasting task of the sales force
of one company is to examine "machine-gener-
ated" forecasts for their customers and make
adjustments. Those adjustments are then mea-
sured against actual sales to determine whether
the salesperson's adjustment improved the fore-
cast or not. Similarly, the product manager's job is
to take the machine-generated forecast, which
has been adjusted by the sales force, and make
further adjustments based on a knowledge of
market conditions or upcoming promotional
events. Once again, these adjustments are mea-
sured against actual sales to determine whether
they improved the forecast. In both cases, the
salespeople and the product manager gain feed-
back that helps them improve their efforts.
Finally, companies should assess forecasting
accuracy in terms of its impact on business per-
formance. Accurate forecasts should not be an
end in themselves, but rather a means to achiev-
ing the end, which is business success. Improve-
ments in accuracy require expenditures of re-
sources, both human and financial, and should
be approached in a return-on-investment frame-
work. For example, in a distribution environ-
ment, maintaining or improving customer service
may be a worthy corporate objective. Investment
in more accurate forecasts may be one way to
achieve that objective. However, if the investment
required to improve accuracy significantly is very
Seven Keys to Better Forecasting
high, then alternative approaches to improving
customer service, such as carrying higher inven-
tory levels, should be considered. The resulting
strategy for improving customer service will then
be based on sound business analysis.
Measuring and tracking accuracy will ulti-
mately help build confidence in the forecasting
process. As the users realize mechanisms are in
place to identify and eliminate sources of error,
they will probably use the primary forecast devel-
oped to support all operations in the company.
Islands of analysis will begin to disappear. and
the organization will be able to assess the finan-
cial return from forecasting management im-
provements.
A
s we work with companies, many of
them come to realize what a profound
impact these seven keys can have on
their sales forecasting practices. As they improve
those practices, they experience reductions in
costs and increases in customer and employee
satisfaction. Costs decline in inventory levels, raw
materials, production, logistics, and transporta-
tion. Greater customer satisfaction accrues from
more accurately anticipating demand and. subse-
quently, fulfilling that demand more often. Greater
employee satisfaction comes from a more under-
standable process, easier information access and
transfer, and explicit rewards tied to performance.
But the first step any company must take before
realizing these benefits is to recognize the impor-
tance of sales forecasting as a management func-
tion. With this recognition comes a willingness to
commit the necessary resources to improving this
critical process. O
References
Kenneth B. Kahn and John T. Mentzer, "The Impact of
Team-Based Forecasting," Journal of Business Forecast-
ing, Summer 1994, pp. 18-21.
Kenneth B. Kahn and John T. Mentzer, "Forecasting in
Consumer and Business Markets," Journal of Business
Forecasting, Summer 1995, pp. 21-28.
John T. Mentzer and Carol C. Bienstock, Sales Forecast-
ing Management (Thousand Oaks, CA: Sage Publica-
tions, 1998).
John T. Mentzer and James E. Cox, Jr., "A Model of the
Determinants of Achieved Forecast Accuracy.
-
Journal
of Business Logistics, 5, 2 (1984a): 143-155.
John T. Mentzer and James E. Cox, Jr., "Familiarity,
Application, and Performance of Sales Forecasting
Techniques," Journal of Forecasting, 3 (1984b): 27-36.
John T. Mentzer and Kenneth B. Kahn, "Forecasting
Technique Familiarity, Satisfaction, Usage, and Applica-
tion, "Journal of Forecasting, 14, 5 (1995): 465-476.
5 1
5 2
John T. Mentzer and Kenneth B. Kahn, "The State of
Sales Forecasting Systems in Corporate America," jour-
nal of Business Forecasting, Spring 1997, pp. 6-13.
John T. Mentzer, Kenneth B. Kahn, and Carol C.
Bienstock, "Sales Forecasting Benchmarking Study,"
Research Report No. 3560 - ROI-1445-99-004-96, Uni-
versity of Tennessee, Knoxville. 1996.
John T. Mentzer and Jon Schroeter, "Multiple Forecast-
ing System at Brake Parts, Inc.," journal of Business
Forecasting, Fall 1993, pp. 5-9.
Mark A. Moon is an assistant professor of
marketing at the University of Tennessee,
Knoxville, where John T. Mentzer holds the
Harry J. and Vivienne R. Bruce Chair of Ex-
cellence in Business Policy and Carlo D.
Smith is a research associate and doctoral
candidate. Michael S. Garver is an assistant
professor of marketing at Western Carolina
University, Cullowhee, North Carolina.
Business Horizons / September-October 1998
doc_290234537.pdf
Sales forecasting is a management function that companies often fail to recognize as a key contributor to corporate success. From a top-line perspective, accurate sales forecasts allow a company to provide high levels of customer service.
Seven Keys to Better
Forecasting
Mark A. Moon, John T. Mentzer, Carlo D. Smith, and Michael S. Garver
44
S
ales forecast-
ing is a man-
agement
function that compa-
nies often fail to
recognize as a key
contributor to cor-
porate success. From
a top-line perspec-
tive, accurate sales
forecasts allow a
company to provide
high levels of customer service. When demand
can be predicted accurately, it can be met in a
timely and efficient manner, keeping both chan-
nel partners and final customers satisfied. Accu-
rate forecasts help a company avoid lost sales or
stock-out situations, and prevent customers from
going to competitors.
At the bottom line, the effect of accurate
forecasts can be profound. Raw materials and
component parts can be purchased much more
cost-effectively when last minute, spot market
purchases can be avoided. Such expenses can be
eliminated by accurately forecasting production
needs. Similarly, logistical services can be ob-
tained at a much lower cost through long-term
contracts rather than through spot market ar-
rangements. However, these contracts can only
work when demand can be predicted accurately.
Perhaps most important, accurate forecasting can
have a profound impact on a company's inven-
tory levels. In a sense, inventory exists to provide
a buffer for inaccurate forecasts. Thus, the more
accurate the forecasts, the less inventory that
needs to be carried, with all the well-understood
cost savings that brings.
The ultimate effects of sales forecasting ex-
cellence can be dramatic. Mentzer and Schroeter
(1993) describe how Brake Parts, Inc., a manufac-
turer of automotive aftermarket parts, improved
its bottom line by $6 million per month after
Excellence in sales
forecasting can boost
a firm 's financial health
and gratify customers
and employees alike,
launching a company-wide effort to improve
sales forecasting effectiveness. Nevertheless, firms
often fail to recognize the importance of this
critical management function. Our objective here
is to take what we've learned about sales fore-
casting from working with hundreds of compa-
nies, and summarize that learning into seven key
focus points (summarized in Figure 1) that will
help any company improve its forecasting perfor-
mance. Although no management function can
be reduced to seven keys, or 70 keys for that
matter, our hope is that the ideas presented here
will inspire senior management to look closely at
their own sales forecasting practices and recog-
nize opportunities for improvement.
Key #1: Understand What Forecasting Is,
and What It Is Not
The first and perhaps most important key to bet-
ter forecasting is a complete understanding of
what it actually is and-of equal importance-
what it is not. Sales forecasting is a management
process, not a computer program. This distinction
is important because it affects so many areas
across an organization. Regardless of whether a
company sells goods or services, it must have a
clear picture of how many of those goods or
services it can sell, in both the short and long
terms. That way, it can plan to have an adequate
supply to meet customer demand.
Forecasting is critical to a company's produc-
tion or operations department. Adequate materi-
als must be obtained at the lowest possible price;
adequate production facilities must be provided
at the lowest possible cost; adequate labor must
be hired and trained at the lowest possible cost;
and adequate logistics services must be used to
avoid bottlenecks in moving products from pro-
ducers to consumers. None of these fundamental
business functions can be performed effectively
without accurate sales forecasts.
Business Horizons /September-October 1998
Many companies consider the most important
decisions about forecasting to revolve around the
(
selection or development of computer software
for preparing the forecasts. They have adopted
the overly simplistic belief that "If we've got good
software, we'll have good forecasting." Our re-
search team, however, has observed numerous
instances of sophisticated computer systems put
into place, costing enormous amounts of time
and money, that have failed to deliver accurate
forecasts. This is because system implementation
has not been accompanied by effective manage-
ment to monitor and control the forecasting pro-
cess.
One company we worked with has an excel-
lent computer system with impressive capabilities
of performing sophisticated statistical modeling of
seasonality and other trends. However, the sales-
people, who are the originators of the forecast,
use none of these tools because they do not un-
Figure 1
The Seven Keys to Better Forecasting
Seven Keys to Better Forecasting
4 5
Keys Issues and Symptoms Ac ions Results
Understand what
forecasting is
and is not.
•
Computer system as focus,
rather than management
processes and controls
•
Blurring of the distinction
between forecasts, plans, and
goals
•
Establish forecasting group
•
Implement management
control systems before
selecting forecasting software
•
Derive plans from forecasts
•
Distinguish between forecasts
and goals
•
An environment in which
forecasting is acknowledged
as a critical business function
•
Accuracy emphasized and
game-playing minimized
Forecast demand,
plan supply.
•
Shipment history as the basis
for forecasting demand
•
"Too accurate" forecasts
•
Identify sources of information
•
Build systems to capture key
demand data
•
Improved capital planning
and customer service
Communicate,
cooperate,
collaborate.
•
Duplication of forecasting
effort
•
Mistrust of the "official"
forecast
•
Little understanding of the
impact throughout the firm
•
Establish cross-functional
approach to forecasting
•
Establish independent forecast
group that sponsors cross-
functional collaboration
•
All relevant information used
to generate forecasts
•
Forecasts trusted by users
•
Islands of analysis eliminated
•
More accurate and relevant
forecasts
Eliminate islands
of analysis.
•
Mistrust and inadequate infor-
mation leading different users
to create their own forecasts
•
Build a single "forecasting
infrastructure"
•
Provide training for both users
and developers of forecasts
•
More accurate, relevant, and
credible forecasts
•
Optimized investments in
information/communication
systems.
Use tools wisely. •
Relying solely on qualitative
or quantitative methods
•
Cost/benefit of additional
information
•
Integrate quantitative and
qualitative methods
•
Identify sources of improved
accuracy and increased error
•
Provide instruction
•
Process improvement in
efficiency and effectiveness
Make it important. •
No accountability for poor
forecasts
•
Developers not understanding
how forecasts are used
•
Training developers to under-
stand implications of poor
forecasts
•
Include forecast performance
in individual performance
plans and reward systems
•
Developers taking forecasts
seriously
•
A striving for accuracy
•
More accuracy and credibility
Measure, measure,
measure.
•
Not knowing if the firm is
getting better
m Accuracy not measured at
relevant levels of aggregation
•
Inability to isolate sources of
forecast error
•
Establish multidimensional
metrics
•
Incorporate multilevel
measures
•
Measure accuracy whenever
and wherever forecasts are
adjusted
•
Forecast performance can be
included in individual perfor-
mance plans
•
Sources of errors can be
isolated and targeted for
improvement
•
Greater confidence in forecast
process
46
REs
The ideas presented in this article are drawn from a program of re
that has spanned more than 15 years. Phase 1 began in 1982 with
survey of 157 companies that explored the techniques they usedd to
cast. Ten years later, under the sponsorship of AT&T Network Systems,
the survey was replicated and expanded in Phase 2 to explore not only
techniques but also the systems and management processes used by
companies. Phase
3,
conducted between 1994 and 1996, was sponso
by a consortium of companies consisting of AT&T Network -Systems,•
Andersen, Consulting, Anheuser-Busch, and Pillsbury. It took the form' of a
benchmarking study that consisted of in-depth analysis of 20 companies:'.
Anheuser-Busch, Becton-Dickinson, Coca-Cola, Colgate-Palmolive,
Fed
-
eral Express, Kimberly Clark, Lykes Pasco, Nabisco, JCPenney,
Pillsbury„
ProSource, Reckitt Colman, Red Lobster, RJR Tobacco, Sandoz, Schering
Plough, Sysco, Tropicana, Warner Lambert, and Westwood Squibb.
Finally,' -
Phase 4 has been conducted since 1996 and consists of a series of fore-
cast audits: The forecasting practices of seven companies-Allied Signal,
Du Pont Agricultural Products Canada, Eastman Chemical, Hershey
Foods
USA, Lucent Technologies, Michelin North America, and Union Pacific ..
Railroad have been studied so far in this phase and compared to those
of the 20 benchmarked companies. The references to this research are
listed at the end of this article.
ormal studies, the learning
nted by more than 20 years of
g with a large number of major
Rg casting systems and processes.
firsthand observation of what
sting in companies, large;
d services and that are "
or retailers.
In additign io
presented here i
experience to c
corporation.
Our ideas
actually
and s
derstand them and have no confidence
in the
numbers generated. As a result, their forecasts are
based solely on qualitative factors and are often
very inaccurate. A similar case is a technology-
based company that has created another highly
sophisticated forecasting tool, yet the salespeople
continue to underforecast significantly because
their forecasts have a direct effect on their sales
quotas. Both of these examples show how some
companies focus on forecasting systems rather
than forecasting management.
On the other hand, some companies have
been more successful in their efforts by
recogniz-
ing the importance of forecasting as a manage-
ment process. Some have organized independent
groups or departments that are responsible for
the entire forecasting process, both short- and
long-term. One large chemical company has
formed a forecasting group not associated with
either marketing or production. It has ownership
and accountability for all aspects of forecasting
management, with responsibility not only for the
systems used to forecast but also for the numbers
themselves. The group accomplishes its mission
in several ways: providing training in the meth-
ods and processes throughout the company; de-
signing compensation systems that reward fore-
cast accuracy; and facilitating communication
among sales, marketing, finance, and production
departments, thereby improving overall forecast-
ing effectiveness. Recognizing the importance of
forecasting, this firm has put an organization in
place to manage the process, not just to choose
and manage a system.
Another way in which companies confuse
what forecasts are and what they are not is by
failing to understand the relationship between
forecasting, planning, and goal setting. A sales
forecast should be viewed as an estimate of what
future sales might be, given certain environmen-
tal conditions. A sales plan should be seen as a
management decision or commitment to what the
company will do during the planning period. A
sales goal should be a target that everyone in the
organization strives to attain and exceed.
Each of these numbers serves a different
purpose. The primary purpose of the sales fore-
cast is to help management formulate its sales
plan and other related business plans-its com-
mitment to future activity. The sales plan's pur-
pose is to drive numerous tactical and strategic
management decisions (raw material purchases,
human resource planning, logistics planning, and
so on), realistically factoring in the constraints of
the firm's resources, procedures, and systems.
The sales goal is primarily designed to provide
motivation for people throughout the organiza-
tion in meeting and exceeding corporate targets.
Whereas the sales forecast and the sales plan
should be closely linked (the former should pre-
cede and influence the latter), the sales goal may
be quite independent. The objective of those
who receive a sales goal should be to beat that
goal. It can be developed based on a sales fore-
cast, plan, and motivation levels. However, be-
cause forecasters should strive for accuracy, it is
not appropriate for a forecast to be confused
with the firm's motivational strategy.
It is particularly problematic when sales fore-
casts and sales goals are intertwined, because this
mixture leads to considerable game playing, es-
pecially involving the sales force. If salespeople
believe that long-term forecasts will affect the
size of the next year's quota, they will be strongly
motivated to underforecast, hoping to influence
those quotas to be low and attainable. Alterna-
tively, as one salesman at a parts supply company
put it, "It would be suicide for me to submit a
forecast that was under my targets." In both
cases, because goals and forecasts are so inter-
t
wined, salespeople are motivated to "play
games" with their forecasts. There is a built-in
disincentive to strive for accuracy.
Some companies have expressed a reluctance
to "manage to different numbers," suggesting that
when the forecast and the goals differ, it creates
confusion and lack of focus. The reaction to such
perceived confusion is to develop inaccurate
forecasts that can affect performance throughout
Business
Horizons / September-October 1998
the company. We believe the sales forecast and
the sales goal must be distinct, because the be-
haviors they are meant to influence can conflict.
Key #2: Forecast Demand, Plan Supply
One mistake many companies make is forecast-
ing their ability to supply goods or services rather
than actual customer demand. At the beginning
of the forecast cycle, it is important to create
predictions that are not constrained by the firm's
capacity to produce. Consider the forecaster for a
certain product who questions the company's
sales force and learns they could sell 1,500 units
per month. At the same time, current manufactur-
ing capacity for that product is 1,000 units per
month. If the forecaster takes that production
capacity into account when creating initial fore-
casts, and predicts 1,000 units, there is no record
of the unmet demand of 500 units per month,
and the information on where to expand manu-
facturing capacity is lost.
This problem often occurs when historical
shipments are used as the basis for generating
forecasts. Forecasting shipments will only predict
a company's previous ability to meet demand.
Suppose demand for a particular product in the
past had been 10,000 units per month, but the
supplier could only ship 7,500. Corporate history
would show shipments at 7,500 units per month,
(
thus causing this amount to be projected and
produced again the following month. The result
is twofold: the impression of an accurate forecast-
ing system, but an actually recurring unfulfilled
monthly demand of 2,500 units. Forecasting
based on shipping history only leads a company
to repeat its former mistakes of not satisfying
customer demand. Predicting actual demand
allows measurement of the disparity between
demand and supply so it can be reduced in fu-
ture periods through plans for capacity expan-
sion.
Often the symptom of this key is the attitude,
" We do a great job of forecasting. We are very
accurate, always selling close to what we fore-
cast." Notice in the previous example that the
forecast accuracy would appear very good be-
cause both the forecast and the actual sales were
7,500 units each month. The key, however, is the
failure to realize the 2,500 units in sales lost each
month because of an inability to meet demand.
In fact, the " true" demand forecasting accuracy
was not 100 percent, but only 75 percent. Fore-
casting by shipments and obtaining accurate re-
sults are often symptomatic of chronic underfore-
casting of demand.
Unfortunately, determining actual customer
demand is more difficult than predicting a com-
pany's ability to supply. Systems and processes
are needed to capture this elusive demand that
Seven Keys to Better Forecasting
was not fulfilled. Mechanisms are needed to allow
salespeople to provide valuable information about
customers who would order more if they could.
In addition, records of orders accepted but not
filled in the period demanded adds to the de-
mand versus supply level of information. Finally,
such electronic data interchange (EDI) informa-
tion as point-of-sale (POS) demand, retail inven-
tory levels, and retailer forecasts are all valuable
sources of information that help a company
move toward demand forecasting.
Although it is more difficult, forecasting true
demand will help a company make sensible,
long-term decisions that can profoundly affect its
market position. By identifying where capacity
does not meet demand forecasts, the company
has valuable information on where to expand
capacity through capital planning. Such a long-
term program of matching capacity planning to
forecasts will reduce the incidence of chronic
underforecasting and result in higher levels of
customer satisfaction.
Key #3: Communicate, Cooperate,
Collaborate
Companies that forecast most effectively consider
it critical to obtain input from people in different
functional areas, each of whom contributes rel-
evant information and insights that can improve
overall accuracy. But employees are often unable
or unwilling to work across functions to achieve
high levels of forecasting performance. To do so
requires a great deal of communication across
department boundaries, and not all communicat-
ing is equal; some companies are simply better at
it than others.
When it comes to cross-functional forecast-
i ng, we distinguish among three levels: communi-
cation, cooperation, and collaboration. Compa-
nies at lower levels of sophistication merely com-
municate. This can take the form of one-way
reports, in which one department responsible for
forecasting informs other functional areas of the
results of its efforts. With coordination, represen-
tatives from different functional groups meet to
discuss the forecast. Often, however, one area-
usually the one that " owns" the forecast-will
dominate the discussions and work to persuade
the other functions to accept the forecast it has
created.
Coordination is superior to one-way commu-
nication, because at least there is opportunity for
some dialogue. But it does not promote as effec-
tive a forecasting process as when different con-
stituencies in a company collaborate. Here, the
views of each functional area receive equal con-
sideration, and no one department dominates.
Such collaboration is most likely to occur when
management of the forecasting process resides in
47
4 8
duling production sche
department was so
distrustful of the
forecasts developed
by marketing that it
completely ignored
them and created a
whole,,. lack market
forecsting system.
an independent department instead of being part
of marketing, finance, logistics, or production.
Each area, with its unique biases and agendas,
can contribute equally to a true consensus fore-
cast.
In several companies we have worked with,
the functional area responsible for generating
forecasts-usually marketing-makes little effort
to obtain input from other affected areas, such as
production planning, operations, or logistics. A
number of negative consequences result. First,
critical information about production lead times
or capacity constraints are not taken into account
when the forecast is finalized. Because this infor-
mation is missing, forecast users have little trust
in projections they did not help develop. This
lack of trust leads to duplicated forecasting ef-
forts. In one company, the production scheduling
department was so distrustful of the forecasts
developed by marketing that it completely ig-
nored them and created a whole " black market"
forecasting system. Had a consensus-based ap-
proach been used, such nonproductive duplica-
tion of efforts could have been avoided.
A further consequence of not working cross-
functionally is a lack of understanding of the
assumptions that go into
forecasts, which leads to
further distrust. In another
company, a production
scheduler would adjust the
forecasts to take into ac-
count the seasonality she
believed was present in
the marketplace. However,
she was not aware that the
marketing department had
already accounted for that
seasonality in the informa-
tion they gave her. Had
production planning been
involved in a consensus-
based forecasting process,
the scheduler's adjust-
ments-which skewed the
have been made.
It is most important in effective forecasting to
establish a mechanism that brings people from
multiple organizational areas together in a spirit
of collaboration. Such a mechanism, often orga-
nized by an independent forecasting group, en-
sures that all relevant information is considered
before forecasts are created. One such mechanism
is in place at a national consumer products firm,
in which the forecasting group organizes and
holds regularly scheduled, half-day meetings that
bring together representatives from National Ac-
counts (sales), product management (marketing),
production planning, logistics, and finance. Each
participant comes to the meeting prepared to
forecasts-would not
discuss upcoming issues that will affect sales and
demand over the forecast period. Formal minutes
are kept to document the reasons for making
adjustments. The end product is a consensus
forecast, with numbers that its users have helped
develop. Duplicate forecasting efforts are elimi-
nated and all the parties can trust the final result:
a more accurate and relevant forecast.
Key #4: Eliminate Islands of Analysis
Islands of analysis are distinct areas within a firm
that perform similar functions. Each area main-
tains a separate process, thereby performing re-
dundant tasks and often having the same respon-
sibilities. Because islands of analysis are often
supported by independent computer systems
(which often are not electronically linked to other
systems within the firm), information contained
within the different islands is not shared between
them.
In our research, we have identified forecast-
ing islands in logistics, production planning, fi-
nance, and marketing. They have usually emerged
because of a lack of interfunctional collaboration
between units, which leads to a lack of credibility
associated with the forecast. Because the " offi-
cial" forecast generated in a particular department
may not be credible to forecast users, the latter
often take steps to implement processes and
systems to create their own forecast.
Islands of analysis are detrimental to corpo-
rate performance. Forecasts developed in this
manner are often inaccurate and inconsistent.
Because each area maintains its own forecasting
process and often its own computer system,
data-if shared at all-are shared only through
manual transfers, which are prone to human
errors. When completely separate systems are
used, the assumptions that underlie the forecasts,
such as pricing levels and marketing programs,
tend to differ from one system to the next. More-
over, each area forecasts with a unique bias,
making separate predictions inconsistent and
unusable by other areas. Redundancies generated
by separate systems cost the firm both money
and valuable personnel time and energy. Em-
ployee frustration builds up, along with an over-
all lack of confidence in the forecasting process.
To solve this problem, management must
devote attention to eliminating the factors that
encourage the development of islands of analysis.
Such a goal can be reached by establishing a
single process supported by a " forecasting infra-
structure." This process should consist of soft-
ware that communicates seamlessly with other
information systems in the firm. Appropriate tools
should include a suite of statistical techniques,
graphical programs, and an ability to capture and
report performance metrics over time. Historical
Business Horizons 11 September-October 1998
sales data can be accessed from a centrally main-
tained " data warehouse" that is electronically
available to all functional areas and provides
real-time data.
Once this forecasting infrastructure is in
place, effective training aimed at a common un-
derstanding of the process and its system should
be implemented for both users and developers.
Employees should be trained to comprehend the
overall process, each individual's role in the pro-
cess, and the importance of accurate forecasting.
They must be able to use the system effectively
and efficiently.
Once islands of analysis are eliminated, the
company can expect improved forecasting per-
formance and significant cost savings. Forecasts
will be more precise, more credible, and better
able to meet the needs of various departments.
When systems are electronically linked, the errors
that result from manual data transfers can be
avoided, and the necessary information can be
accessible to all functional areas. From a cost
perspective, a single forecasting process elimi-
nates redundant efforts within the firm, thus sav-
ing valuable employee time and other resources.
And because accuracy will be improved, all the
well-documented cost savings in areas such as
purchasing, inventory control, and logistics plan-
ning can be tracked and realized.
Key #5: Use Tools Wisely
Many companies tend to rely solely on qualitative
tools-the opinions of experienced managers
and/or salespeople-to derive forecasts, ignoring
such quantitative tools as regression and time-
series analysis. Alternatively, many companies
expect the application of quantitative tools, or the
computer packages that make use of them, to
" solve the forecasting problem." The key is that
both quantitative and qualitative tools are integral
to effective sales forecasting. To be effective,
however, they must be understood and used
wisely within the context of the firm's unique
business environment. Without understanding
where qualitative techniques, time series, and
regression do and do not work effectively, it is
impossible to analyze the costs and achieve the
benefits of implementing new forecasting tools.
One common symptom of a failure to realize
this key is the existence of detailed sales forecast-
ing processes that, when examined, reveal the
subjective judgments of managers or salespeople
as the only input used in the forecast. In other
words, the company has a quantitative sales fore-
casting process that supports only qualitative
forecasts. It relies too much on the ability of ex-
perienced personnel to translate what they know
into a forecast number, without taking into ac-
count the myriad of quantitative techniques and
Seven Keys to Better Forecasting
their ability to analyze patterns in the history of
demand.
The opposite symptom is a sales forecasting
process that performs intensive numerical analy-
sis of demand history and the factors that relate
statistically to changes in demand, but with no
qualitative information on the nature of the mar-
ket and what causes demand to change. The
company depends too much on the ability of
these techniques to determine estimates of future
demand without taking experience into account.
A variation on these symptoms is relying on a
" black box" forecasting system. This occurs when
a company has a sales forecasting computer
package, or " box," into
which historical sales data
are fed and the forecasts
come out, but no one seems
to know how it comes up
with them, or even what
techniques it uses. The com-
pany abrogates its responsi-
bility by turning the impor-
tant job of sales forecasting
over to a computer package
that nobody understands.
Using forecasting tools wisely requires know-
ing where each type of tool works well and where
it does not, then putting together a process that
uses the advantages of each in the unique context
of the firm. Salespeople who do a poor job of
turning their experience into an initial forecast
may be good at taking an initial quantitative fore-
cast and qualitatively adjusting it to improve
overall accuracy. Time series models work well in
companies that experience changing trends and
seasonal patterns, but they are of no use in deter-
mining the 'relationship between demand and
such external factors as price changes, economic
activity, or marketing efforts by the company and
its competitors. On the other hand, regression
analysis is quite effective at assessing these rela-
tionships, but not very useful in forecasting
changes in trend and seasonality.
To apply this key, a process should be imple-
mented that uses time series to forecast trend and
seasonality, regression analysis to forecast demand
relationships with external factors, and qualitative
input from salespeople, marketing, and general
management to adjust these initial quantitative
forecasts. This general recommendation must be
refined for each individual company by finding
the specific techniques that provide the most
i
mproved accuracy. Finally, key personnel in-
volved in either the quantitative or the qualitative
aspects of the forecasting process need training
in using the techniques, determining where they
work and do not work, and incorporating quali-
tative adjustments in the overall forecasting pro-
cess.
5 0
"Salespeople product
managers; and other
forecasters will see the
i mportance of the task
f salient rewards follow
as a result' of forecasting
excellence.
Key #6: Make it important
What gets measured gets rewarded, and what
gets rewarded gets done, say Mentzer and Bien-
stock (1998). This management truism is the
driver behind our final two keys. Sales forecast-
ing is often described by senior management as
an important function. But although this assess-
ment may be shared by individuals throughout
the firm, few organizations institute policies and
practices reinforcing the
notion that forecasting is
important for business
success. There is often a
gap between manage-
ment's words and their
actions. Companies fre-
quently tell those who
develop forecasts that
" forecasting is impor-
tant," but then fail to
reward them for doing
the job well or punish
them for doing it poorly.
Forecast users become
frustrated by a perceived lack of interest and
accountability for accuracy among forecasters.
Such frustration often leads them to manipulate
existing forecasts or, in the extreme case, develop
islands of analysis that duplicate forecasting ef-
forts and ignore valuable ideas.
One way to gauge how important forecasting
is to a firm is to determine how familiar users and
developers are with the entire process. Without
such familiarity, individuals involved in forecast-
ing throughout the firm have little appreciation of
the impact of their inaccuracies and are therefore
unlikely to spend the time and attention needed
to do the job well. As a result, users perceive that
forecasters are not taking the task seriously and
thus discount the value of what they produce.
A number of actions can be taken to address
this gap in forecasting importance. One way is to
give all individuals involved adequate training.
Forecast creators and users must know where
and how forecasts are used throughout the firm.
When forecasters become aware of all the down-
stream ramifications of sloppy work, the task
takes on more relevance to them. Marketing and
salespeople who typically are concerned about
forecasting only at the product or product line
level should understand that this does not pro-
vide the necessary detail for operations to plan
stock-keeping unit (SKU) production or for logis-
tics to make SKUL (by location) shipment plans.
Similarly, forecast users should be more aware of
the needs and capabilities of forecast developers.
Another action management can take is to
incorporate forecasting performance measures
into job performance evaluation criteria. Clearly.
salespeople, product managers, and other fore-
casters will see the importance of the task if sa-
lient rewards follow as a result of forecasting
excellence. Even senior managers become inter-
ested when the metrics of accuracy are worked
into their personal performance evaluations and
bonus plans.
But focusing on senior management is not
enough. One company includes forecast accuracy
as a meaningful part of the performance plans of
its senior executives, but not of those on the
" front line" who work with forecasts on a daily
basis. The job has not been made to seem impor-
tant to those who do it, with the effect that it is
still
not done very well.
This is particularly true of the people who
are typically responsible for initial forecast in-
put-the sales force. At nearly all the benchmark
and audit companies, salespeople are critically
important pieces of the forecasting puzzle. Yet in
almost all cases, the ones who develop forecasts
receive neither feedback on how well they fore-
cast nor any type of reward for doing it well.
Many agree with a salesman for a high-tech
manufacturer, who said, " My job is to sell, not
forecast." Similarly, product managers, who also
provide critical input to the forecasting process at
many companies, often consider forecasting an
extra burden that takes them away from their
" real jobs."
Key #7: Measure, Measure, Measure
Obviously, before forecasters can be rewarded
for excellence, a company must first develop
systems for measuring performance, tools for
providing feedback, and standards and targets for
what constitutes forecasting excellence. Without
the ability to effectively measure and track per-
formance, there is little opportunity to identify
whether changes in the development and appli-
cation of forecasts are contributing to, or hinder-
ing, business success. This key may be intuitive
for most business managers, yet our research has
i dentified surprisingly few companies that syste-
matically measure forecasting management per-
formance. In cases where measures have been
implemented, they are infrequently used for per-
formance assessment or to identify opportunities
for improvement.
A primary symptom indicating a lack of per-
formance measurement can be gleaned from
conversations with individuals involved in the
forecasting process. Simply asking for a measure
of forecasting accuracy typically elicits a response
of 'pretty good," " lousy," or other general de-
scriptors. In some cases, the answer may include
a number considered to be a measure of accu-
racy, such as " 75 percent," or error, such as " 25
percent." Further inquiry may indicate that the
Business Horizons / September-October 1998
source of the measure is based on a general
"feeling," estimate, or a second- or third-hand
source of information, and the respondent is
unsure of how such measures were calculated or
what level of aggregation was used.
In cases where measures are collected and
documented, there may still be insufficient detail
or little realization as to how they can help iden-
tify opportunities for forecasting improvement.
Generally we have found that even when accu-
racy has been measured over time, few individu-
als who contribute to forecast development re-
view the history and can determine whether their
performance has improved, remained constant,
or deteriorated. This reflects a complacency to-
ward performance measures when such mea-
sures are not used to evaluate a person's job
performance, or do not provide support for iden-
tifying sources of forecasting error.
Effective measures evaluate accuracy at dif-
ferent levels of aggregation. Logistics operations
are interested in forecast performance at the
SKUL level; sales managers may be more inter-
ested in a forecast stated in dollars and at the
territory or product line level of aggregation.
Performance metrics should support these vari-
ous units of measure as well as the aggregation
of demand at different levels.
It is also important to track accuracy at each
point at which forecasts may be adjusted. As an
illustration, the forecasting task of the sales force
of one company is to examine "machine-gener-
ated" forecasts for their customers and make
adjustments. Those adjustments are then mea-
sured against actual sales to determine whether
the salesperson's adjustment improved the fore-
cast or not. Similarly, the product manager's job is
to take the machine-generated forecast, which
has been adjusted by the sales force, and make
further adjustments based on a knowledge of
market conditions or upcoming promotional
events. Once again, these adjustments are mea-
sured against actual sales to determine whether
they improved the forecast. In both cases, the
salespeople and the product manager gain feed-
back that helps them improve their efforts.
Finally, companies should assess forecasting
accuracy in terms of its impact on business per-
formance. Accurate forecasts should not be an
end in themselves, but rather a means to achiev-
ing the end, which is business success. Improve-
ments in accuracy require expenditures of re-
sources, both human and financial, and should
be approached in a return-on-investment frame-
work. For example, in a distribution environ-
ment, maintaining or improving customer service
may be a worthy corporate objective. Investment
in more accurate forecasts may be one way to
achieve that objective. However, if the investment
required to improve accuracy significantly is very
Seven Keys to Better Forecasting
high, then alternative approaches to improving
customer service, such as carrying higher inven-
tory levels, should be considered. The resulting
strategy for improving customer service will then
be based on sound business analysis.
Measuring and tracking accuracy will ulti-
mately help build confidence in the forecasting
process. As the users realize mechanisms are in
place to identify and eliminate sources of error,
they will probably use the primary forecast devel-
oped to support all operations in the company.
Islands of analysis will begin to disappear. and
the organization will be able to assess the finan-
cial return from forecasting management im-
provements.
A
s we work with companies, many of
them come to realize what a profound
impact these seven keys can have on
their sales forecasting practices. As they improve
those practices, they experience reductions in
costs and increases in customer and employee
satisfaction. Costs decline in inventory levels, raw
materials, production, logistics, and transporta-
tion. Greater customer satisfaction accrues from
more accurately anticipating demand and. subse-
quently, fulfilling that demand more often. Greater
employee satisfaction comes from a more under-
standable process, easier information access and
transfer, and explicit rewards tied to performance.
But the first step any company must take before
realizing these benefits is to recognize the impor-
tance of sales forecasting as a management func-
tion. With this recognition comes a willingness to
commit the necessary resources to improving this
critical process. O
References
Kenneth B. Kahn and John T. Mentzer, "The Impact of
Team-Based Forecasting," Journal of Business Forecast-
ing, Summer 1994, pp. 18-21.
Kenneth B. Kahn and John T. Mentzer, "Forecasting in
Consumer and Business Markets," Journal of Business
Forecasting, Summer 1995, pp. 21-28.
John T. Mentzer and Carol C. Bienstock, Sales Forecast-
ing Management (Thousand Oaks, CA: Sage Publica-
tions, 1998).
John T. Mentzer and James E. Cox, Jr., "A Model of the
Determinants of Achieved Forecast Accuracy.
-
Journal
of Business Logistics, 5, 2 (1984a): 143-155.
John T. Mentzer and James E. Cox, Jr., "Familiarity,
Application, and Performance of Sales Forecasting
Techniques," Journal of Forecasting, 3 (1984b): 27-36.
John T. Mentzer and Kenneth B. Kahn, "Forecasting
Technique Familiarity, Satisfaction, Usage, and Applica-
tion, "Journal of Forecasting, 14, 5 (1995): 465-476.
5 1
5 2
John T. Mentzer and Kenneth B. Kahn, "The State of
Sales Forecasting Systems in Corporate America," jour-
nal of Business Forecasting, Spring 1997, pp. 6-13.
John T. Mentzer, Kenneth B. Kahn, and Carol C.
Bienstock, "Sales Forecasting Benchmarking Study,"
Research Report No. 3560 - ROI-1445-99-004-96, Uni-
versity of Tennessee, Knoxville. 1996.
John T. Mentzer and Jon Schroeter, "Multiple Forecast-
ing System at Brake Parts, Inc.," journal of Business
Forecasting, Fall 1993, pp. 5-9.
Mark A. Moon is an assistant professor of
marketing at the University of Tennessee,
Knoxville, where John T. Mentzer holds the
Harry J. and Vivienne R. Bruce Chair of Ex-
cellence in Business Policy and Carlo D.
Smith is a research associate and doctoral
candidate. Michael S. Garver is an assistant
professor of marketing at Western Carolina
University, Cullowhee, North Carolina.
Business Horizons / September-October 1998
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