HD28
Oewey
.M414
no.
•iST.
IS^b-
SEP
•85-
24
m.
1985
^^
\
Center for Information Systems Research
Massachusetts
Institute of
Technology
Sloan School of Management
Massachusetts Avenue
n
Cambridge, Massachusetts, 02139
'
SUPPORTING SENIOR EXECUTIVES' MODELS
FOR PLANNING AND CONTROL
Michael
E.
/
Treacy
June 1985
CISR WP #125
Sloan WP #1675-85
(c)
Michael
E.
Treacy
1985
Center for Information Systems Research
Sloan School of Management
Massachusetts Institute of Technology
SEP 2 4
L
1985
RECEsVED
SUPPORTING SENIOR EXECUTIVES' MODELS
FOR PLANNING AND CONTROL
by
Michael E. Treacy
1.
Introduction
By now the
use of models
successful
documented and easily observable.
in corporate decision making
Mclnnes and
Wooller [1977], Naylor and Schauland [1976],
all
Carleton
[1982],
and Traenkle,
et
al_
observed the accelerating use of decision support system
support
functional
corporate planning.
or used by
needs
in
finance,
Very few explicit
senior executives
marketing,
-
or formal
operations
-
[Rockart and Treacy,
Grinyer and
[1975] have
(DSS)
Most of these models are built and used by mid-level
packages.
is well
modeling
analysts to
management,
and
models appear to be built
1982].
If
these
are
the
people responsible for the formulation of broad strategies and the design of
organizational control mechanisms, then it follows that explicit modeling has
not had as much impact upon these areas as some proponents would desire.
Yet,
models
reality,
simplify
these senior executives use models for planning and control.
are
intuitive
abstractions
the
decision
and
of
implicit.
complex
process,
They
decision
to
are
mental
contexts,
identify
that
important
Their
representations
executives
variables,
of
use
to
and
to
generate and evaluate alternatives.
built
into
a
computer
analyzed
program,
models, but they are models.
They are not usually put down on paper,
quantitatively,
even
or
viewed
as
They are often simple and inelegant and may bear
little relationship to "good theory," but they are arguably the most important
models for planning and control, because they are powerful
and they get used.
As one executive put it:
•
I
bring a lot of knowledge to the party.
Just
scanning the current status of our operations
enables me to see some things that those with less
time in the company would not see as important.
[Rockart and Treacy, 1982]
This
executive
These
operation.
clearly
models
well -developed
many
has
allow
him
to
quickly
implicit
assess
in
How well
trouble because
is this company doing?
sales
are declining.
conrionly held implicit model
But what
if
the time frame
model
is required
these
numbers may
actually
model
of expected
sales was much
simply
This conclusion
represented is only
pattern may
These
that
illustrates
1
One reaction is that the firm is
is
that sales are always rising in
sales
only
Figure
his
Looking at the sales performance reflected in the trend line, one
this point.
might ask:
of
information
new
someone less experienced would have to formally analyze.
models
indicate
seasonality,
to interpret the data.
see
a
very
lower.
and
a
derived
more
a
healthy company.
twelve months?
a
from
Then
complex
the
informal
An experienced executive looking at
healthy
This
situation because
example
his
illustrates
or
her
that data
becomes information when it is interpreted through some implicit model.
models
are
nothing
experience and knowledge.
other
than
the
accumulation
of
an
individual's
3-
Sales
Time
Figure
1
Analyzing Sales Data With Implicit Models
-4-
Does the executive really need an explicit or formal
his
analyzed using
modeling
range of techniques.
a
understood
who
Explicit models
operations?
her
or
Others
operations.
those
of
operations
firm's
the
are
easily
might
argue
that
they
can
a
manager
benefit
by
formal
what
the
executive
but support for his or her implicit modeling,
needs is not formal models,
be
that even
could
well
and
shared
Some would argue
very
to understand
model
so
that the weaknesses of this abstract mental modeling can be reduced.
This
paper contributes
toward
the
debate
over whether
senior executives
should be supported with explicit models or by other measures.
The answer depends upon the type of decision contexts faced
is a simple one.
by
We analyze the nature of explicit and implicit models and
the executive.
alternatives for supporting these different modeling processes.
this,
the
rest
Our position
of
section discusses
senior executives'
paper
the
divided
is
into
needs
most important problems are complex
that complete and formal
The
next
and concludes
that
sections.
several
nature of executive analytic
the
To accomplish
unstructured
and
and
analyses of these problems are not always possible.
The section concludes with
a
simple categorization of analyses that is later
used to discuss alternate types of support systems.
Section
alternate
3
forms
executives.
of
develops
analysis
of
a
set of criteria for evaluating the appropriateness of
support
This
is
may
incur,
done
by
for
for
different
analytic
examining the potential
a
alleviate or avoid such problems.
of decision support systems.
the
major
objective
The final
of
needs
of
senior
problems that each form
support
systems
is
to
section discusses alternate forms
These different forms derive from differences in
•5-
DSS
tools,
time
some
so
devoted
is
to
a
discussion
of
these
One
tools.
conclusion drawn in this section is that new» emerging forms of modeling, such
as
may provide support
fuzzy modeling,
for many
of
most
senior executives'
important analytic problems.
The Nature of Senior Executive Analysis
2.
There
is
a
organizations,
explanations
of
lot
little
but
suggest
of
use
formal
of
use
themselves.
models
them
may
It
planning
for
senior
by
control
executives.
senior
that
be
and
in
Several
executives
do
not
building and analysis or for using
possess the necessary skills for model
a
But Rockart and Treacy have found that even among senior executives
computer.
who use computers for analytic support there is little use made of explicit
It may be that executives
Models [1981, 1982].
don't need formal models; or
that their information requirements are simple enough that ad hoc analysis is
sufficient.
are
too
Or, it may be quite the opposite,
complex
for
explicit modeling
to
that the problems they confront
be
of
much
benefit.
This
third
of
senior
possiblility we believe is often closest to the truth.
Much
has
executives.
with
been
written
about
the
roles
information
and
needs
Mintzberg [1973] observed that senior managers are often dealing
overwhelming
computer-generated
complexity
under
information
was
great
of
time
little
pressure.
use
to
these
He
found
people.
that
They
preferred current and timely information that could be obtained only through
informal
channels.
Gorry and Scott Morton
[1971] build upon Anthony's
[1965]
-6-
framework
and
characterized
role
the
of
senior executives
They require support for defining
largely unstructured.
strategic
as
and
generating
problems,
alternative solutions, and evaluating them against strategic criteria.
issues that senior executives
Most of the important planning and control
must face, such as the creation of plans, the control of subordinates, or the
analysis of competitive advantage, are highly dependent upon the exact context
of
the
problem.
They
operating
standard
deal
with
Sufficient
procedures.
under great uncertainty.
There
and
is
no
for
complete
Therefore,
problems is very difficult to obtain.
work
problems
unique
which
are
there
knowledge
no
these
of
senior managers must often
"normative"
approach
planning
to
and control; there is no clear set of decisions which arise in the course of
this process.
as
future
Rather, data concerning both past and current results as well
plans,
are
scanned
and
analyzed,
variable decision opportunities occur.
and
As Charles
a
set
of
ad
hoc
,
highly
Hitch [1957] observed some
years ago:
The sort of simple explicit model which operations
researchers are so proficient in using can certainly
reflect most of the significant factors influencing
traffic control on the George Washington Bridge, but
the proportion of the relevant reality which we can
represent by any such model or models in studying, say,
a major foreign policy decision, appears to be almost
trivial, [p. 718]
Adding to the difficulties of
what Bellman
[1957]
calls
aspire to plan and control
a
lack of structure and great uncertainty is
"the curse
of multidimensional ity.
"
Managers may
their firms for the single oDjective of long
term
-7-
as present period profit plans and gaining X% of market share,
along
way.
the
Thus,
there
as milestones
their
independent and
objective of long run profits is often not well
relationship to the overall
understood.
not
objectives are
operational
These
such
objectives,
profitability, but in practice they require many operational
further
is
introduced
uncertainty
multiple
by
and
fuzzy evaluation criteria.
We may conclude that the most important problems of planning
and control
These two
faced by senior executives are complex and relatively unstructured.
»
characteristics, complexity and structure, define
situations that is helpful
executives.
In
Figure
2
a
categorization of analytic
in discussing alternate ways of supporting
have
we
illustrated
senior
For
categorization.
this
expository purposes, quadrants have been used to define four types of analytic
The qualifier
situations.
"relatively"
indicate that there
has been used to
are not clear demarcations between these categories.
important problems
The
are
analytic
of
planning
illustrated
situations
in
and
the
control
upper
faced by
right
senior managers
are
they
quadrant:
complex, relatively unstructured problems.
In
the
lower left quadrant are
situations
analytic
where
extent
the
of
knowledge about the problem is relatively complete and the problem is not very
such
In
operating
procedures
quadrant
complex.
are
problem
situations,
complex.
analytic
can
often
situations
For these problems,
be
solving
routinely
that
are
is
fairly
applied.
relatively
easy
In
well
the
and
standard
upper
structured,
left
but
standard operating procedures or rules of thumb
-8-
PROGRAM COMPLEXITY
relatively
complex
relatively
simple
relatively
relatively
structured
unstructured
PROBLEM STRUCTURE
Figure 2
Four Types of Analytic Situations
-9-
typically in the form of an explicit
analysis,
Formal
are often inadequate.
model, is required to integrate many pieces of information in a structured way.
difference
The
between
problems can be illustrated with
can
problem
be
viewed
as
order quantity can
economic
simple
a
one,
applied,
be
models are needed to pull
formal
problem of
the
to
or
inventory
which
as
well -structured
relatively
of
types
two
these
rules
such
thumb
of
Material
relevant information together.
all
It can
requirements planning is an example of the latter view of the problem.
be
implemented
with
only
the
of
aid
large,
Which
models.
formal
as
which
for
complex problem,
a
This
reordering.
view
is
appropriate for structured problems depends primarily upon the level of effort
one wishes
to
expend
to
generate
a
satisfactory
For unstructured
solution.
problems, it also depends upon what is and is not known about the problem.
It is evident from the preceding examples that some problems can be viewed
An inventory problem can
as fitting in any of the four quadrants of Figure 2.
be simple or complex,
depending upon how it is viewed,
and
the
greater one's
experience and understanding of such problems, the more structure they appear
to
The more
have.
provide
a
unstructured the
complete represention;
important it is to provide
a
problem
area,
the more complex
valid formal
analysis.
the
given
executives,
a
choice.
by
the
nature
of
the
problems
able
are
the problem area,
we
to
the more
Thus, given a choice, one
would opt for the most complete analysis and perhaps
senior
less
a
they
formal
face,
analysis,
are
not
but
often
-10-
For each
of
the
analytic
illustrated
situations
analytic approaches can be applied.
particular,
In
Figure
in
different
2,
choose to
one can
explicit or implicit models to any of these types of problems.
apply
Which form of
modeling is most appropriate for each type of analytic situation is examined
in the next section by developing a list of strengths and weaknesses
analytic
These
approach.
lists are
also
used
in
subsequent
the
determine which form of support system is most appropriate
for each
section
for each
to
type of
analytic situation.
3.
Potential Problems with Explicit and Implicit Models
Little
models
are
has
developed
a
list of criteria
robust,
simple,
easy
control,
to
with, and as complete as possible.
executives
have
been
cases, and critical assumptions can all
Good
[1970].
to
communicate
implicit mental models
to examine alternate
scenarios,
current
observation negates part of
account for what was observed.
Subtle
from
observing
relationships,
models
complex
exceptional
These
And one can use them in many ways -
as part of a
information.
a
derived
The mental
be part of a good mental model.
implicit models are also easy to control.
with
easy
They can be robust.
relationships and events over many years.
conjunction
adaptive,
By these criteria,
be carried around in a person's head.
experienced
models
good
They are simple, or at least simple enough that they can
can be good models.
of
for
larger analytic
Mental
models
are
process,
adaptive.
or
in
If
manager's model, it will probably be modified to
They are as complete as possible,
as a manager has been able to comprehend.
as complete
II.
can
however,
good models,
for
criteria
Little's
They can be viewed as a list of the potential
way.
Formal
criteria.
Little's
meet
easily
so
problems introduced when
This would account for why mental
implicit mental models are made explicit.
models
another
interpreted
be
models
tend
be
to
complicated; they can sometimes give absurd results when certain complexities
are
only
can
They
missing.
controlled
be
They usually are difficult to
facilities are built into the modeling system.
adapt
situations
other
to
and
information.
new
to
whatever
through
exercised
and
explicit
And
models
certainly are not easily communicated with by a typical manager unaccustomed
to their use.
Not all
pick out the good ones.
ills
than
That
research
El
am,
for
et
al_
But,
why
is
those
and
formal
to
Will
[1975]
fomal models suffer more of these
least
at
Or,
their
shortcomings
are
important
and
new
models.
Konsynski
and
Dolk
is
each
present
conceptual
more
of
area
an
management
model
committed
[1980],
in general,
models.
informal
do
obvious.
Little provided a list that helps to
formal models are this bad.
[1982],
for
schemes
modeling systems that would help automatically to reduce some of the problems
with explicit models.
If these
ideas could be implemented,
then we might see
more managers approaching problems using formal modeling methods because they
would be perceived as more valuable relative to implicit mental models.
Perhaps the most important limitation of mental models is that they do not
handle
complexity
relationships
remarkably
well.
between
broad
range
It
is
variables.
of
difficult
Our
knowledge,
integrators of that knowledge.
but
to
mental
people
mentally
integrate
capacities
are
not
can
complex
handle
particularly
a
good
12-
A
problem
second
manipulate.
Once
a
with
implicit models
these
adequately describes
model
a
that
is
are
they
hard
to
phenomenon, one might like to
use it to predict or to optimize some aspect of the phenomenon.
This requires
manipulation of the model, trying alternative scenarios, assessing the impact
of uncertainty, and all
the other techniques that have become familiar through
line-oriented
systems.
modeling
This
cannot
done
be
as
easily
with
as
a
formal mathematical model.
Another problem with mental
because
experience.
planning
that
is
they
are
difficult to
share
And it is even harder to convey the supporting evidence for the
with others.
model
models
it
This
and
often
is
is
and
gathered
years
through
of
important problem because the process of management
an
control
intangible
is
a
large
and
widespread
undertaking.
requires
It
specialists in particular areas, such as a strategic planning, accounting, and
requires the insight,
finance.
In
addition,
it
can
be
provided
by
only
senior
line
analysis,
management.
Thus,
and guidance which
it
is
a
shared
activity, involving many different actors with only partly shared perspectives.
A
fourth
problem with
implicit models
Mental models are difficult to validate.
stems
test
against
impartial
information processors.
mental models.
evidence.
This may
result
problems
and
one
two.
If they cannot easily be manipulated
and if they cannot be easily shared with others,
to
from
We
in
know
then they are also difficult
that
imperfect,
people
perhaps
are
imperfect
badly
Without proper validation, these flaws may not surface.
flawed
-13-
Finally,
implicit models
are
vastly
Managers
underutilized.
often know
much more about situations than they bring to the decision-making processes of
planning and control.
There is a major, uncatalogued resource out there that
is not being adequately utilized.
In
Figure 3 we have summarized the preceding discussion as a list of the
primary potential
of
a
support
and mental
problems with formal
system
executive modeling
for
is
models.
to
A major objective
alleviate
some
of
these
problems.
Formal Models:
*
*
*
*
*
complicated and hard to understand
difficult to make robust
difficult to control
difficult to adapt
difficult to communicate with
Mental Models
*
do not handle complexity well
difficult to manipulate
difficult to share
difficult to validate
uncatalogued and underutilized
*
Figure 3
Primary Potential Problems with
Formal and Informal Models
4.
Alternate Forms of Analytic Support
Among
all
the
buzzwords,
jargon,
and acronyms of the information
field, decision support systems (DSS) has proven to be
philosophical
substance.
As
a
practical
matter,
a
systems
term of practical
and
DSS focuses on the analysis
of data and explicit modeling of the business and its environment.
A manager
14-
viewed
is
not
as
passive
a
infonnation,
of
consumer
but
active
an
as
participant in the development and use of his or her personalized, supporting
process
development
Adaptive design
with
manager
and
wants
desires.
are
necessary
user control
to
maintain,
tenet
the
the
the
in
to
a
process
of
of
the
direction
the DSS
Adaptive
approach.
parts
those
evolve
involvement in,
system
a
that
supporting
and
manager's
job.
support
begin
job
that
that
the
or
she
he
The user is assumed to be knowledgeable about his or her role and,
if provided with the appropriate data,
evolve
will
of
fit
to
require
and
not just
over,
fundamental
a
perspective,
user's
the
is
design
evolutionary
user control
Thus,
computer system.
equivalent
better
toward
Lindblom's
to
software tools, training and guidance,
management.
It
"muddling
[1959]
is
information
the
through."
This
systems
non-invasive,
adaptive philosophy has been key to DSS's extraordinary practical success.
During
past
the
ten
years,
decision
of
host
a
support
systems
(DSS)
generators and software packages have been developed that allow analysts and
managers
to
differences
forms
of
develop
among
analytic
the
and
major
support
directly
decision
use
categories
presently
support
packages
of
available.
define
Figure
systems.
the
4,
The
alternative
adapted
from
Montgomery and Urban [1969], shows the capabilities that have been provided by
different DSS generators.
A
decision
support
system
provides
information on his or her internal
an
a
manager
and external
interaction and display facility
that may
with
another
source
business environment.
include
a
of
Through
command language and
query, report writing, and color graphics facilities, the manager can access
a
15-
MANAGER
\
DECISION
INTERACTION
MODELS
AND DISPLAY
STATISTICS
AND
DATA BASE
MANIPULATION
1
ENVIRONMENT
Figure 4
Comprehensive DSS Capabilities
-16-
base
of
and
functions,
arithmetic,
statistical,
perform
data,
explicit
create
models
his
of
other
and
her
or
data
firm,
manipulation
competitors,
industry, and the economy.
Figure 4
represents
an
generator capabilities
DSS
set of
ideal
With
.
these capabilities it is now possible to build many different types of support
systems.
best for each of
form of support is
face is which
The question we
the different analytic situations discussed
in
Section 2?
In
Figure 5,
four
different forms of support are indicated for the four analytic situations.
describe each of these forms of support and
the rest of this section we will
explain
how
they
relieve
problems
potential
the
In
explicit
of
and
implicit
models discussed in the last section.
Model -Oriented DSS for Complex, Well -Structured Problems
Complex problems that are
with
formal
explicit
models.
enough knowledge to construct
context.
is
the
complex,
Hence,
analytic
relatively well
formal
a
a
well -structured
area,
there
is
fairly complete representation of the problem
of
choice,
problems
system
A
should
modeling environment in which modeling
reduced.
problem
better analyzed
modeling is possible and for significant problems it
approach
well -structured
In
structured are
be
is
to
aimed
support
toward
the
analysis
providing
easier and potential
a
of
formal
problems are
-17-
PROBLEM COMPLEXITY
relatively
complex
relatively
simple
MODEL-ORIENTED
DSS
18-
focused
support
decision
of
the
on
just
Many
such
form
a
generators
system
capabilities
The
support.
of
line-oriented modeling packages are diagrammed in Figure
modeling
line-oriented
A
gives
spreadsheet,
interrelated
several
time
manipulate
the
that the model
model,
ability
and
analyses.
These
are
known
results
assumptions
defined line by line, with each
of
and
line
easily
to
and
scenarios,
systems
over
model
the
of
model
user
the
line-oriented
electronic
an
as
explicit
an
allow
packages
the
alternate
test
the
these
of
6.
define
to
calculate
Typically,
to
commonly
more
are
the
in
representing
to
sense
another
The packages usually allow a manager to define, solve, and analyze
variable.
model
is
the
variables
periods.
perform sensitivity
a
manager
a
several
system,
available
presently
and generate reports and graphs.
They do not,
in
general,
manage
a
data base or offer ad hoc analytic capabilities.
These
line-oriented modeling
systems have won
many
supporters,
but
they
are still not user-friendly enough to have gained a significant base of senior
executive users, except in financial
the creation of formal
to
create
also
a
address
designed
providing
to
a
many
make
of
the
modeling
powerful
They are,
however,
ideal
for
models if the analyst has enough knowledge to be able
complete
fairly
functions.
but
representation
potential
easier,
problems
less
uncomplicated
building and model analysis commands.
of
the
with
problem.
formal
complicated,
syntax
that
and
These
models.
more
incorporates
packages
They
friendly
both
are
by
model
19-
-20-
The command languages used in this type of software also make it easier to
control
Changing
models.
data
values,
performing
different
analyses,
and
changing the format of printouts are all easier with these packages than with
a
standard
programming
language.
Because the
models
are
syntactically
less
complicated and easier to understand, they are also easier to adapt.
The one potential problem that these packages do not help to alleviate is
the
difficulty
in
making
might be used to create
a
only a language and has
formal
models
For example,
robust.
this
software
sales forecasting model, but because the package is
no built-in
intelligence about sales forecasting,
it
can in no way assure that the model is correct.
These
modeling
line-oriented
accounting-based models
in
fairly
systems
are
well-understood
used
areas,
such
evaluation of projects, budgeting, and consolidation analysis.
understand enough accounting to be able to evolve useful
models through an interactive process of model
one
way
that
the
semantic
potential
problems
as
financial
Most managers
and usable financial
building and analysis.
of
for
extensively
formal
models
have
Thus,
been
overcome is by basing the models upon well -understood accounting principles.
Data-Oriented DSS for Simple, Well -Structured Problems
Simple
and
well -structured
monitoring and tracking sales,
data
that
they
can
use
to
problems
are
for example,
trigger
rules
fairly
easy
what managers
of
thumb
or
to
need
support.
is
standard
access
In
to
operating
-21-
This is the type of support offered by data-oriented DSS.
procedures.
These
systems are built to provide managers with ready access to a pre-defined base
They are often used as supplements for paper-based reporting systems
of data.
because they offer more flexible reporting formats as well as a limited set of
These systems are used to support monitoring in areas where
analytic tools.
an
executive
has
fairly well -formed mental
a
But because the data covers only
limited area and analytic tools are also
a
appropriate
only
are
DSS
data-oriented
limited,
performance.
of expected
model
recurring,
for
reasonably-structured problem areas such as monitoring performance vs. budget.
The software that data-oriented decision support systems are based upon is
quite
different
usually
called
management
in
RAMIS,
software
the
"friendly"
systems
illustrated
FOCUS,
from
(DBMS).
Figure
NOMAD,
(that
The
Some
7.
for model -oriented
used
is,
easy
to
capabilities
well-known
and DBASEII.
These
of
and
learn
this
products
packages
systems.
use)
class
in
provide
a
data
base
software
are
marketplace
are
of
the
is
It
manager with
a
facility for managing and accessing a large base of data, creating reports and
graphs,
and
performing
very
limited
analysis
upon
the
data.
They
give
managers the ability to choose the data that they wish to see and to format it
in reports or graphs as they wish to
the line-oriented modeling
software,
see it.
Unfortunately, as with much of
the user interfaces for these data base
packages are not yet friendly enough to encourage widespread use by executives.
•22-
•23-
Infonnation Support for Simple, Unstructured Problems
difficult
more
the more unstructured they are,
for relatively simple problems,
Even
becomes
it
used to address problems
relative
Concurrently,
for support.
appropriate
to
pre-define
to
such
subject
become
must be based upon at least three design elements:
a
that are
firm is
doing
validity,
questionable
to
are
Support for simple but unstructured problems
manipul ability, and sharability.
(2)
models
as succession planning or how a
competition
its
implicit mental
the
that
reports
and
data
the
the
(1)
large base of data,
a
set of ad ho£ analytic tools, and (3) sharing of the system by managers
These design
with common information needs.
support
system,
which
the
is
most
elements define
form
implemented
widely
information
an
of
support
for
senior executives.
an
In
increasing
number
of
companies,
of
level
a
and
data
software
commonality have been established; their information support systems resembles
Figure
8.
naturally
planners
Bases of data have been established for groups of individuals who
draw
and
the
upon
same
controllers
type
created
of
one
data.
In
base
of
managers and market researchers created another.
Chicago,
corporate
staff
[Rockart and Treacy,
1982]
and
In
executives
all
both firms,
data,
At
draw
it was
company,
one
while
one
strategic
the
marketing
Industries
Northwest
from
the
base
the managers
of
in
data.
and analysts
who established what data would be in the data bases and what data definitions
would be used.
And,
despite the common data bases,
also maintain their own personal data files.
in every
case the
users
-24-
USER INTERFACE
Specific
25-
With present information requirements analysis
These data bases evolve.
techniques and with the existing state of data management in most firms, only
about half the needed data can be put into
data base on the first pass.
a
In
addition, the nature of the executive's job is one of constant change and new
data are often needed to support new problems and opportunities.
Each of these three design elements helps
the
manager
against the data.
models.
are
build,
to
In
explore
and
test
his
or
her
models
implicit
this way, data analysis serves to help validate mental
One of the potential
difficult to
reduce problems of validity
A large base of data combined with analytic tools
and sharing mental models.
allows
to
share,
problems with these implicit models is that they
difficult to
transfer to another person.
A
shared
information support system provides a language and a medium for communicating
If managers support their implicit models through a system,
implicit models.
then the models become more transferable with the sharing of data and analysis.
The
data
software
management
that underlies
and
ad
hoc
information
analysis
support
capabilities.
systems
The
comprehensive and contain all the elements shown in Figure
must
have
packages
both
must
be
4.
Fuzzy Modeling and Expert Support for Complex and Unstructured Problems
Most implicit mental models are fuzzy, but computer-based modeling systems
are precise.
computer-based
Until
we can develop fuzzy modeling systems, most of the useful,
support
for
implicit
models
will
be
provided
through
data
-26-
and
analysis,
retrieval,
the
Fuzzy modeling systems will
internal
inconsistent
and
transfonning
allow
a
different
into
formats.
manager to externalize crude, incomplete,
without
models
data
of
having
complete and internally-consistent models like those
to
translate
found
in
into
them
model -oriented
DSS.
Fuzzy models are used by senior managers all
pricing decision,
(2)
(3)
of
manager might bring into play the following rules:
statements
these
is
in
form
a
precise
enough
fuzzy modeling system, however, the above statements form
decision that could be solved.
satisfactory,
but it is
a
to
be
used
Each statement begs for refinement.
standard formal modeling system.
be
in a
Our price should be about two times direct
costs.
Our price should be just below our dominant
competitor's price.
If our competitor's prices go too high, we
should price for increased market share.
(1)
None
a
For example,
the time.
model
a
in
a
With a
of a pricing
The results of that solution clearly would not
useful
starting point that prompts the manager
for a more refined representation of his or her mental model.
Fuzzy
models
problems,
with.
complexity.
potential
type
the
These
are
of
models
But,
flaws
of
ideally
suited
problems
have
the
fonnal
precision and consistency.
that
power
conceptually,
models
complex
to
senior
of
fuzzy
such
as
the
relatively
executives
formal
models
and
must
modeling
compensate
requirements
unstructured
commonly
systems
for
to
many
deal
handle
of
the
for completeness,
-27-
Fuzzy modeling systems can create and analyze models that are incompletely
these
for
compensate
systems
ambiguous
in
their
the models
by
intelligently
or
inconsistent,
internally
specified,
flaws
in
assumptions
added
automatically
are
model
the
to
choosing
Along with the results,
assumptions that will allow the model to be analyzed.
these
The
causality.
shown
and
the
to
manager who can modify them for further analysis.
The development of fuzzy modeling systems might sound as if is many years
away.
The technology for fuzzy modeling has been developed in the
It is not.
intelligence
artificial
product already
has
an
community
approximate
and
widely
becoming
is
reasoning
provides
that
capability
One
available.
some
fuzzy modeling features.
intelligence community,
Another important development from the artificial
relevant
the
to
support
of
complex
relatively
situations, is human expert systems.
assistants
computer.
a
reorient the applications toward
to
that
can
support
rather
than
A
recent development
providing computer-based expert
replace
a
of
the
data,
taking
For
maker.
decision
example, one company has developed "smart" statistical tools.
is given sales data,
decision
unstructured
To date, such systems have been oriented
toward replicating expert capabilities on
has been
and
When
a
computer
for instance, the system will analyze the characteristics
into
account such
things as
seasonality,
and
will
then
choose the most appropriate sales forecasting tool.
The conceptual
under
the
rubric
underpinnings of expert support systems have been developed
of
"model
management
systems."
According
to
El
am,
et
al_
-28-
[1980], a model management system,
can be viewed as a system that dynamically constructs a
This is
decision aid in response to a particular problem.
accomplished by drawing on a knowledge base of models that
captures the technical expertise of a management scientist
plus an understanding of the basic activities involved in
a given decision-making environment...
These fuzzy modeling and expert support tools are not yet available.
is
little
wonder,
modeling
needs
problems.
Of
are
the
meaningful
of
therefore,
popular
faced
executive
for
difficulty
had
complex
with
support
a
problems,
the
unstructured
and
support systems
because
systems
models.
support mechanism for complex problems.
senior managers might feel
supporting
information
support,
support for a manager's implicit mental
support fails as
"real"
executives
senior
have
we
three other forms of
the
most
that
It
offer
they
But,
information
That is why many
that executive support cannot help them with their
their complex
For them,
problems.
must wait
we
little
a
longer for the emergence of some "real" executive support tools.
While
we
are
waiting,
models or modeling systems.
for
planning
explicit,
and
control,
it
In
one
benefits
not
might
assume
of
challenge us
to
decision making.
uses of them.
rethink
think
too
narrowly
-
all
in
important
about
a
models
spreadsheet
implicit models
-
a
model
and
its
role
in
are
format.
and how to
support technology evolves,
As
our concept of
that
problems
This paper has explored another type of model
best support managers'
to
reading some of the literature on models used
representations
mathematical
us
it will
managerial
-29-
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Will,
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8
25
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