University of Phoenix Wk5 Forecasting Methods Discussion This discussion is based on the article, “Manager’s Guide to Forecasting” by David Georgoff and Ro

University of Phoenix Wk5 Forecasting Methods Discussion This discussion is based on the article, “Manager’s Guide to Forecasting” by David Georgoff and Robert Murdick from Harvard Business Review that is part of the article pack you need to buy from Harvard Business Press. See the Syllabus for details on purchasing this pack.

Georgoff, D. M., & Murdick, R. G. (1986). Manager’s guide to forecasting. Harvard Business Review, 64(1), 110–120.

In this course, you will touch on a few forecasting methods, although there are many more approaches available to those managers who wish to do more. This week’s article provides an overview of many forecasting methodologies and provides a framework through which you can explore their differences.

Your objective in this discussion is to learn to analyze a specific forecasting situation and identify the best suited methodology. You will complete three steps.

Step 1: Describe a specific forecasting need in your organization.

Step 2: Use the provided table below to analyze the requirements of the forecasting problem.

Step 3: Identify the best matching forecasting methodology to your situation and describe how it would be executed.

In Step 2, the analysis will be based on the table shown on pages 4 and 5 of the article. This table lists several questions about the nature of the forecasting situation, such as the urgency, detail required, and costs factors, and provides an overview of how well various forecasting methodologies will fit those requirements. For example, some forecasting methods cost more than others and depending on your financial resources, some of them may not be suitable. The same holds true with the math skills available or the need for high accuracy. So, understand what each category is referring to, fill in the information, and follow the table to see which methodology is recommended for your specific case. You are learning how to analyze your situation so as to pick the best approach. Please note that these 2 pages in the article (pages 4–5) go side by side. You may wish to print them out and place them next to each other to read across the rows comfortably. The table shown below is based on the table in the article. Also note that many of the squares are shaded light or dark grey to show strength or weakness in each category.

Full instructions on how to use the table are in the second column of page 6 in the article.

Please use the template below in your answers so everyone can easily follow your answers to all the questions (copy and paste to your post).

Use this format for your Unit 5 Discussion.

Forecast need

Describe what question this forecast aims to answer, and why it is important for your organization to have this information.

Forecast situation analysis

Identify a forecast method by filling in the table below. The full table is on pages 4–5 of the article. You should fill in the table by answering the questions in the “Questions” column. Your answers will lead you to the methods that are most suitable for your forecasting need. The ideal fit will give you a strong match to your answers in the “output” section of the table, while still meeting the conditions in the “time”, “input” and “resource requirements” sections.

Category

Dimension

Questions

Answer to the Questions

Time

Span

Is the forecast period a present, short/medium, or long-term projection?

Urgency

Is the forecast needed immediately?

Frequency

Are frequent forecast updates needed?

Resource

Math skills

Are quantitative skills limited?

Computer

Are computer capabilities limited?

Financial

Are only limited financial resources available?

Input

Antecedent

Are only limited past data available?

Variability

Does the primary series fluctuate substantially?

Internal consistency

Are significant changes in management decisions expected?

External consistency

Are significant environmental changes expected?

External stability

Are significant shifts expected among variable relationships?

Output

Detail

Are component forecasts required?

Accuracy

Is a high level of accuracy critical?

Capability for reflecting direction changes

Should turning points be reflected properly?

Capability for detecting direction changes

Should turning points be identified early?

Form

Is an internal or probabilistic forecast critical?

Recommendations

Answer the following:

Which forecasting methodology listed in the article is the best match to your situation?

In which categories is the methodology showing a good fit (in other words, why did you select this methodology)? In which categories does this methodology show a weak fit?

Describe how this forecast will be executed: Who will do it, where will the data come from, how frequently will it be repeated, and how will the results be used? Manager’s Guide
to Forecasting
by David M. Georgoff and Robert G. Murdick
Harvard Business Review
Reprint 86104
For the exclusive use of A. James, 2020.
HBR
J A N U A RY– F E B R U A RY 1 9 8 6
Manager’s Guide to Forecasting
David M. Georgoff and Robert G. Murdick
E
arly in 1984, the Houston-based COMPAQ
Computer Corporation, manufacturer of IBMcompatible microcomputers, faced a decision
that would profoundly affect its future. Recognizing
that IBM would soon introduce its version of the
portable computer and threaten COMPAQ’s dominance in this profitable market, the company had two
options. It could elect to specialize in this product
line and continue to market its highly regarded portables aggressively, or it could expand market offerings
to include desktop microcomputers. The latter move
would force the year-old company to confront IBM on
its home ground. Moreover, COMPAQ would have to
make a substantial investment in product development and working capital and expand its organization
and manufacturing capacity.
COMPAQ’s management faced several important
unknowns, including the potential market’s size,
structure, and competitive intensity. Management
recognized that the company’s vitality might seriously erode if it did not expand its product line. If the
expansion were successful, COMPAQ might enjoy
economies of scale that could help ensure its survival
in a dynamic and very competitive industry. If COMPAQ’s market assumptions were incorrect, however,
its future might be bleak.
Many of today’s managers face similar new market
realities and uncertainties. Continually confronted
with issues critical to their companies’ competitive
future, they must deal with novel and rapidly changing environments. In short, they must judge a broad
range of dissimilar influences.
For more than a decade, new forecasting techniques
have theoretically helped managers evaluate these
varied factors. Much of the promise of these techniques has been unrealized, however, even as a quickening succession of related advances have been overwhelming decision makers with new alternatives.
As the number of techniques proliferates, management also realizes that some of its crucial assumptions and projections about the economy have become quite tenuous. Equipped only with a little
history, meager and questionable data, and frail and
changing theoretical tools, the forecaster must nevertheless make critical decisions about altered futures.
As an example, COMPAQ Computer’s quandary
was further complicated because new technologies,
competitors, and products were already transforming
Mr. Georgoff is professor of marketing at Florida Atlantic
University and chairman of the Department of Management, Marketing, and International Business. He has published articles and worked as a consultant to large corporations in the areas of new product marketing, marketing
planning, market research, and forecasting.
Mr. Murdick is professor of management at Florida Atlantic University. Previously he worked at the General
Electric Company for 14 years. Well known in the field of
management information systems, he is the author or
coauthor of 18 books on management and marketing, the
most recent of which is MIS: Concepts and Design (Prentice-Hall, second edition, 1986).
Authors’ note: We thank Steven C. Wheelwright for his
valuable assistance in the preparation of this article.
Copyright © 1986 by the President and Fellows of Harvard College. All rights reserved.
This document is authorized for use only by Aaron James in GB513: Business Analytics_Regular_8_23_2020 taught by CHRIS OSADCZUK, Purdue University Global from Feb 2020 to Aug
2020.
Scenario
methods: smoothly
unfolding narratives
that describe an
assumed future
expressed through a
sequence of time
frames or snapshots.
Jury of executive
opinion: the
consensus of a
group of “experts,”
often from a variety
of functional areas
within a company.
Sales-force
composite: a
compilation of
estimates by
salespeople (or
dealers) of expected
sales in their
territories, adjusted
for presumed biases
and expected
changes.
Naive
extrapolation:
the application of a
simple assumption
about the economic
outcome of the next
time period, or a
simple, if subjective,
extension of the
results of current
events.
Judgment methods
Historical
analogy:
predictions based
on elements of past
events that are
analogous to the
present situation.
Delphi technique:
a successive series
of estimates
independently
developed by a
group of “experts”
each member of
which, at each step
in the process, uses
a summary of the
group’s previous
results to formulate
new estimates.
Brief descriptions of methods
Manager’s Guide to Forecasting
Industrial
market survey:
data similar to
consumer surveys
but fewer, more
knowledgeable
subjects sampled,
resulting in more
informed evaluations.
Consumer
market survey:
attitudinal and
purchase intentions
data gathered from
representative
buyers.
Moving
averages:
recent values of the
forecast variables
averaged to preduct
future outcomes.
Market testing:
representative
buyers’ responses to
new offerings, tested
and extrapolated to
estimate the
products’ future
prospects.
Time series
extrapolation:
a prediction of
outcomes derived
from the future
extension of a least
squares function
fitted to a data
series that uses time
as an independent
variable.
Adaptive
filtering:
a derivation of a
weighted
combination of
actual and estimated
outcomes,
systematically
altered to reflect
data pattern
changes.
Exponential
smoothing:
an estimate for the
coming period
based on a
constantly weighted
combination of the
forecast estimate for
the previous period
and the most recent
outcome.
Time series methods
Counting methods
Box-Jenkins:
a complex,
computer-based
iterative procedure
that produces an
autoregressive,
integrated moving
average model,
adjusts for seasonal
and trend factors,
estimates
appropriate
weighting
parameters, tests the
model, and repeats
the cycle as
appropriate.
Time series
decomposition:
a prediction of
expected outcomes
from trend,
seasonal, cyclical,
and random
components, which
are isolated from a
data series.
Econometric
models:
outcomes forecast
from an integrated
system of
simultaneous
equations that
represent
relationships among
elements of the
national economy
derived from
combining history
and economic
theory.
Leading
indicators:
forecasts generated
from one or more
preceding variable
that is systematically
related to the
variable to be
predicted.
Regression
models:
estimates produced
from a predictive
equation derived by
minimizing the
residual variance of
one or more
predictor
(independent)
variable.
Correlation
methods:
predictions of values
based on historic
patterns of
covariation between
variables.
Input-output
models:
a matrix model that
indicates how
demand changes in
one industry can
directly and
cumulatively affect
other industries.
Association or causal methods
Indicates
weakness
Indicates
strength
Ex = Execution
time
*Dev =
Development
time
a market that had been only recently established.
COMPAQ’s forecast of the size, direction, and price
trends of the 1984 microcomputer market was confounded by uncertainties about the market’s response
to several vital factors:.
M The entry of IBM’s new portable computer.
M IBM’s 23% price cut in June 1984 and its
potential erosion of margins.
M The entry of lap portables introduced by
Hewlett-Packard and Data General.
MThe launch of IBM’s new PC AT, complicated
by unexpected delivery delays and compatibility
problems.
M The introduction of desktop computers by
Sperry, NCR, ITT, and AT&T.
Eventually, COMPAQ entered the desktop segment of the market, even though 1984 was unforgiving and rampageous. Several large competitors restricted their programs; many smaller companies
went into—or to the edge of—receivership. Financially and competitively, COMPAQ succeeded. During 1984, sales rose from $111 million to $329 million
and earnings increased from $4.7 million to $12.8
million.
The market’s dynamics, however, make such results increasingly difficult to achieve; positive and
negative events—both expected and unforeseen—have a decisive effect. Even when managers
anticipate outcomes, grave uncertainties about timing, form, and impact persist.
Despite the difficulty, the vice president of marketing and the CEO—the two executives most directly
involved with the decision—demonstrated what can
be done. They used an extended series of consumer
and dealer surveys coupled with periodic evaluations
of the technology to assess the future market and to
guide the development of products and programs to
accommodate the industry’s fluid and rapidly evolving needs. Managers can use forecasting techniques
to help them reach important decisions. A large and
fast-growing body of research deals with the development, refinement, and evaluation of forecast techniques. Managers also have greater access to both
internal and external data and can benefit from a
multitude of computer software programs on the
market, as well as easier access to computer capabilities for analyzing these data.
FORECASTER’S CHART
While each technique has strengths and weaknesses,
every forecasting situation is limited by constraints
like time, funds, competencies, or data. Balancing the
advantages and disadvantages of techniques with re6
gard to a situation’s limitations and requirements is
a formidable but important management task.
We have developed a chart to help executives decide which technique will be appropriate to a particular situation; the chart groups and profiles a diverse
list of 20 common forecasting approaches and arrays
them against 16 important evaluative dimensions.
We list techniques in columns and dimensions of
evaluation in rows. Individual row-column intersections (cells) reflect our view of a technique’s characteristics as they apply to each dimension. Brief descriptions of the forecasting methods are given on the
chart.
We have used different shades of gray to show
which dimensions represent a strength for a particular technique and which represent its weaknesses.
The strengths are highlighted in light gray; weaknesses are indicated by a dark gray cell. Naive extrapolation, for example, is strong in internal consistency in that it easily reflects changes in management
decisions. It is weak, however, in forecast form. It is
important to keep these distinctions in mind when
you are using the chart.
The chart is useful in two ways. The first is in
deciding which technique will suit your particular
needs as a forecaster. The second is in deciding how
to combine techniques to further improve the result.
In this section, we discuss the simpler approach; we
talk more about combining methods later.
To use the chart, look at the 16 questions listed in
the first column after the dimensions. They are the
most common questions a manager will ask when
deciding to use a certain forecast. The first question
sets out the various time spans a forecast would have
to cover. Everyone who uses the chart will have to
answer question 1. But each of the following questions can be answered with a yes or no. If you answer
no to a question, you don’t have to look across that
row.
In responding to question 1, make note of those
techniques whose time span matches your needs. We
have found it easiest for forecasters to write down the
technique’s column letter. The row number of each
dimension and the column letter of each technique
are written along the horizontal and vertical axes.
With regard to question 1, for example, if your forecast horizon is short-term, you can write down the
cell letters for naive extrapolation (A), sales-force
composite (B), jury of executive opinion (C), and so
forth. But you would ignore the letters for scenario
methods (D), Delphi technique (E), historical analogy
(F), and so on.
The columns you have now listed represent techniques that are qualified for further consideration.
Next read down the column of each of these techniques and note any gray cells. If these gray cells are
HARVARD BUSINESS REVIEW
January–February 1986
associated with questions to which you have answered yes, then the dimension either precludes use
of the technique or the technique can be used but it
has difficulty accommodating that dimension. Such
precautions will help you determine whether you
must—or wish to—eliminate certain techniques
from further consideration. An arrow in a cell indicates that its evaluation is the same as the cell to its
left.
After you have answered all the questions and have
a list of surviving techniques, note the cells that are
highlighted in light gray. Those cells represent specific strengths of a technique and can guide you in
making a final selection.
In the course of the exercise, you may have eliminated a technique that you like, have heard about, or
routinely use. You can go back to that one and compare its strengths and weaknesses with those of the
methods that the chart has indicated would be best
for you. You can then decide whether you would
rather proceed with the technique that the chart
indicates corresponds most closely to your specific
requirements or whether you can accommodate the
eliminating factors in order to use the technique that
you initially favored.
Important Considerations
When considering each question, you should remember some “tricks of the trade” concerning:
Time horizon. Most managers will want the forecast results to extend as far into the future as possible.
Too long a period, however, may make the technique
selection process even more confusing because of the
varying abilities of the techniques to accommodate
different time spans. In choosing an extended time
horizon, the forecaster increases the complexity,
cost, and time required to develop the final product.
You can break down the time needed to produce a
forecast into development (Dev) and execution (Ex)
time. Development time includes the gathering and
entry of data, the modification of programs to the
company’s specific requirements, and the start-up of
the system. Execution time is the time it takes to
produce a forecast with a particular technique. Initially, of course, development time is a significant
concern for the forecaster; once the forecast technique is firmly established, however, execution time
is a more appropriate concern.
Technical sophistication. Experience shows that
computer and mathematical sophistication is integral to many techniques. Although many executives
have improved their skills in this area, not all have
sharpened their quantitative skills enough to be comfortable with some of the forecast results a computer
will spill out.
Cost. The cost of any technique is generally more
HARVARD BUSINESS REVIEW
January–February 1986
important at the beginning when it is being developed
and installed; after that, any technique’s potential
value to a decision maker usually exceeds the expense of generating an updated forecast.
Data availability. Before choosing a technique, the
forecaster must consider the extensiveness, currency,
accuracy, and representativeness of the available
data. More data tend to improve accuracy, and detailed data are more valuable than those presented in
the aggregate. Because a technique’s ability to handle
fluctuations is important to a forecast’s success, the
manager must match the sensitivity and stability of
a technique to the random and systematic variability
components of a data series.
Variability and consistency of data. Beyond
changes that might occur in the company’s structure
or its environment, the manager must look at the
kind of stable relationships assumed among a model’s
independent variables (represented by the “external
stability” dimension). For example, while most historically oriented quantitative forecasts might use
expected levels of automobile production as a basis
for determining demand for steel, the forecast model
may not reflect changes over time in the average
amount of steel used in automobiles. These relationships sometimes do change, but any variation is
usually so gradual that it will not affect a short-term
forecast. When the forecasts are long-term, however,
or when the company expects a substantial change in
a vital relationship, the forecaster should either apply
judgment in a quantitative technique or use a qualitative method.
Amount of detail necessary. While aggregate forecasts are easy to prepare, the manager will need
specific information (including individual product
classes, time periods, geographic areas, or productmarket groupings, for example) to determine quotas
or allocate resources. Since forecasts vary widely in
their ability to handle such detail, the manager may
want a technique that can accurately predict individual components and then combine the results into an
overall picture. Otherwise, the forecaster can use one
technique to provide an overall picture and then use
past patterns or market factors to determine the
component forecasts.1
Accuracy. While accuracy is a forecaster’s holy
grail, the maximum accuracy one can expect from a
technique must fall within a range bounded by the
average percentage error of the random component of
a data series. Also, because of self-defeating and selffulfilling prophecies, accuracy must be judged in light
of the control the company has over the predicted
outcome and within the time and resource constraints imposed on the forecaster.
Remember also that accuracy alone is not the most
important criterion. The forecaster may wish to forgo
7
Forecasting Strategies
[There are] three basic strategies of forecasting. . . .
The deterministic strategy assumes that the present has
a close causal relation to the future. This is the strategy
that would be used by a cardsharp, who had stacked
the deck of cards, to predict the deal. In economic forecasting, the strategy would be used to predict construction expenditures by a knowledge of construction contract awards already made.
The symptomatic strategy assumes that present signs
show how the future is developing; such signs do not
“determine” the future but reveal the process of
change that is already taking place. Thus, a falling barometer may reveal a coming storm, or a rising body
thermometer an incipient illness. In economic forecasting, this strategy calls for the spotting of “leading indicators”—time series whose movements foreshadow
rises or declines in general business activity.
The systematic strategy assumes that, though
changes in the real world may seem accidental or chaotic, careful analysis can reveal certain underlying
regularities (sometimes called principles, theories, or
laws). The way to find these regularities is to black out
much of reality and hold only to the abstractions that
make up a system, such as a solar system, or a nuclear system, or an economic system.
Though the theories that result from this process of
abstraction are “unreal,” they may nevertheless possess the power to affect the real world—provided, of
course, that the theories are sound. The test of the
soundness of a theory is how it measures up when applied to reality: An atomic explosion confirms Einstein’s E = mc2. Similarly, a price cut that leds to increased sales confirms the hypothetical demand curve
that no man has ever seen outside an economics textbook.
To be sure, economic “laws” do not have the consistency of those in the physical sciences. Nevertheless,
economic relations or theories, derived from a study of
the past, may be useful tools for prediction, within
some acceptable range of probable error.
some accuracy in favor of, for example, a technique
that signals turning points or provides good supplemental information.
Turning points. Because turning points represent
periods of exceptional opportunity or caution, the
manager will want to a…
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