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Operations Research: A Global Language for Business Strategy

Author(s): Harvey M. WagnerSource: Operations Research, Vol. 36, No. 5 (Sep. - Oct., 1988), pp. 797-803Published by: INFORMSStable URL: http://www.jstor.org/stable/171325 .

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OPERATIONS

RESEARCH:

A

GLOBALLANGUAGE

FOR

BUSINESS STRATEGY

HARVEYM.

WAGNER

University f NorthCarolina,ChapelHill, NorthCarolina

(ReceivedJune 1988;acceptedAugust 1988)

Based on the author'sobservations nd experience, his paper argues hat the concepts and vocabulary f operations

researchhave become a pervasivepart of the thinking of modem American ndustrialmanagers, nd that the related

models are playing important roles

in

informing the decisions that they make. Since any model is only a partial

representation f reality,

his

fact-and the criticisms

hat t has

evoked-present

the

operations esearch nd management

communities

with

continuingchallenges or improving

heir

work.

Operations

research s a relativelyyoung disci-

pline. Unlike many seminal developments in

other sciences, promising new ideas in operations

research are usually translated into successful appli-

cations within

10 years,

and often within 5. As a

result, there is little stockpiling

of untested

ideas,

and

a critical look

at the current state of the art

tells

a

good

deal

about how well

operations

research

is likely to serve the needs

of

enterprises

in

the next

decade.

My central thesis is that during the past 40 years

operations research has gained significance by being

an international language of business strategy-or,

rather,an international languagefor business strategy.

The utility and practicality of this language have been

conclusively demonstrated by many thousands

of

real

applications

in

a wide variety of enterprises, both

private and public, throughout the industrially devel-

oped world.

To see how this has come about and where it is

likely to lead, it will be useful to consider three ques-

tions:

* What have been the pivotal achievements in opera-

tions research over the past 40 years?

*

How does operations research produce value added

for

an industrialenterprise?

*

What furtherprogress n operations research s likely

to

be made

in

the next 10 years?

Past Achievement

From

a long historical

perspective,

the conceptual

foundations of operations

research

are

tracedto

very

early

developments

in the fields

of

economics,

prob-

ability theory, statistical

inference,

mathematics,

computation

and physics.

Many deep

theoretical

in-

vestigations

of these

underpinnings

took

place before

the

middle

of the 20th century.

But it is

really only

since midcentury

that these scientific

developments

have had a

significant

and demonstrable

impact on

actual decision making. Although progressin opera-

tions research

has

been evolutionary,

its pace has

been

so

rapid

that

strategic

decision

making processes

in

major corporations

today

are

radically

different

from

what they

were

in 1950.

During these

40 years,

operationsresearch

has

pro-

duced practical

implementable

tools for

analyzing

decision

making problems

in

large-scale,

complex

real-life organizational

environments.

These methods

have provided

valuable

new

insights and yielded

ac-

tionable

results.

The economic

benefits are

so substan-

tial

that the costs

of

performing

the analyses

are

usually

recovered

three

to four times faster

than is

the case with most capital investment projects.

The technical

achievements

can be classified

into

a

few,

slightly overlapping,

categories:

*

The optimal

allocation of

scarce resources

subject

to a

large

number

of constraints

(these

are mostly

applications

of

linear programming).

Subject classification: Professional: addresses and OR/MS implementation.

Operations Research

0030-364X/88/3605-0797

$01.25

Vol. 36, No. 5, September-October

1988 797

?

1988 Operations Research Society of America

This content downloaded from 186.133.224.114 on Fri, 6 Dec 2013 17:10:53 PMAll use subject to JSTOR Terms and Conditions

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WAGNER

*

The search

for efficient solutions

among

a vast

mul-

titude of

alternative

choices

(these

are

applications

involving discrete

search and

optimization,

such

as

mixed

integer

programming

models).

* The diagnosis of dynamic systems characterizedby

fluctuating inputs

and

outputs (these

applications

involve Markovian

decision

processes,

most often

applied

to

inventory

and maintenance

decisions).

*

Inferential processes

to

derive

insights

from

multi-

variate statistical observations

(these

examples

in-

volve the

analysis

of

complex systems

rife

with

probabilistic

phenomena).

*

Computer

simulation of

intricate economic

and

physical

systems (these

applications

require

the

use

of

fourth

generation

computing

software).

Let us

look briefly at each

of these.

Mathematicalprogrammingmodelsare integralto the

planning

processes of

thousands of

manufacturing

companies,

principally

in

the petroleum,

chemicals,

fertilizer, metals,

forest-products

and food

industries.

Usually

the models are

aimed

at

finding

a

least-cost

plan for

production that

meets stated

marketing ob-

jectives. The

planning horizon

can be

anywhere from

a

single

month

to a

full year

or

occasionally longer.

Sometimes the models are

more

comprehensive

and

yield a

maximum

profit plan, thereby

analyzing the

merit of

the

marketing

objectives

themselves.

By way of

illustration,

recently

I

was

asked by a

packagingfirm

to assist

in

building a

linear program-

ming model to plan monthly production of 130 prod-

ucts,

on

45

pieces

of

equipment,

located

in

8

plants.

The firm

has never

used

linear

programming

before.

The

resulting

model contains

over 1000

constraints

and

4000

variables. It took

only a

week to

construct

and

debug

the

model,

which

now is

quickly

solved on

a

microcomputer

in

15 minutes.

Discrete Optimization.

Many

important

examples of

discrete

search occur

in

logistic-system

design and

capital-investment-selection

ituations. A

fabricatoror

distributor

may need to

operate

several

warehouses

that position inventories close to customer sources.

Running too many

warehouses

needlessly

drains

profits;

having

too few

degrades

service

response

and

may increase

transportation

costs.

Accordingly,

the

optimization

process

searches for a

combina-

tion of

warehouses

that offers the

best

balance of

overhead

expense,

transportation costs

and service

requirements.

Several

months

ago,

I

constructed

such

a

model

for

a

Japanese

hard-goods

manufacturer. This

company

wanted to

establish an

effective

distribution system

for

serving its overseas

markets.The

model

examines

the

selection of

entry

ports

in

several

countries,

the

location

and size of

majordistribution

warehouses

in

these and adjacent countries, the modes of transpor-

tation

(such as

truck,

rail, and

barge) from ports

to

distribution

centers, and the

assignmentof

geographic

markets to

the distribution

facilities.

This

model

involves nearly

100 discrete (0,

1) variables

and hun-

dreds of

continuous

variables; there are

more than

500

complicating constraints.

For this

model, solution

time

on a

microcomputeris

less than

one-half hour.

(The linear

programming

software

package that I

use,

which is

exceptionally good, is

the

XA

Professional

Programming

System, developed

by Sunset

Software,

1613

ChelseaRoad,

Suite 153, San

Marino,

California

91108.)

Dynamic

Models.

Service

organizations are the prin-

cipal

users of

dynamic

systems models. For

example,

office equipment

and

computer

manufacturers

posi-

tion

repairstaff

and

spare parts

in

field service loca-

tions to

respond rapidly

to

customer

calls. Dynamic

models are

effective tools for

sizing the service

orga-

nization and

providing work

assignment

rules to

maximize

the productive time of

the repair

staff.

My personal

experience

with

these

models is

pri-

marily

in

the

application of

stockage

models. Here

is

one

illustration.

A

major

electronics

components

sup-

plierwished to install

a

replenishment

system to pro-

vide a

competitive level of

service at low

cost.

The

most

difficult

operations

research

challenge

in

this

situation was to

integrate

the

effective

statistical de-

mand-estimation

procedures

with

scientific

inventory-

replenishment rules.

Applications

of new

multivariate statistical

models

have grown

significantly

in

the

past

decade

with

the

advent of

large-scale

data

bases,

enormous fast-access

computer

storage

capacities,

and

quick

turnaround

times

for

analyzing

these vast

quantities

of

informa-

tion.

Here is a real

example

in

the automobile

insur-

ance industry. Today, thanks to these models, it is

possible,

in

only

a few

minutes,

to

categorize

the loss

ratio

experience

of

50,000

insurance

policies

auto-

matically

into

several dozen

profitable

and

unprofit-

able,

statistically

valid,

market

segments.

With

these

results,

the

insurance carrier s better

able

to

assess its

premium

rate

structure and

diagnose

in

detail

the

profitability

of

alternative

business-getting

strategies.

The

technical

breakthrough

s the

ability

to

do

such

a

comprehensive

analysis

in so short

a time.

Previously,



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799

months

of analysis

would

not

have

sufficed

to

produce

the

same degree

of

market segment

resolution.

In

the

past few years, these applications

have become more

advanced and

now embrace

concepts

of artificial in-

telligence.

Finally, computer simulation models

have enabled

companies

to

test strategiesbefore implementing

them

and

thereby substantially

reduce the

risk

of

adopting

an

unworkable approach.

The

ambitious nature

of

these applications is impressive.

A

recent article

ap-

pearing

n

PC

Week (CraigZarley,

Air

Traffic Model

Aims to Get You There on Time,

May 10, 1988, p.

57) describesa microcomputersimulation model built

by

American

Airlines in

cooperation

with the Federal

Aviation Administration, to simulate

air

traffic con-

trol

at any airport

in

the

world.

The

model,

which is

written in SIMSCRIPT, will be made available as

public

domain software to

other

airlines and

individ-

uals. It is

used

to

diagnose options

that have an

impact

on

airport traffic congestion. Of

all

the techniques

mentioned, computer simulation is the most resource

intensive. Nevertheless, the number of applications of

this

approach probably exceeds that of mathematical

programmingby a factor

of

10

to

1.

Until 5 years ago, all

of

these advances

depended

heavily

on

large-scale high-speed

computing.

Since

then,

the

computing situation

has

been revolutionized.

As

I

already

have

illustratedby examples, many useful

operations

research

applications

are now

being

made

with microcomputers. This computing option, when

feasible,

cuts

application development

time

consider-

ably,

and allows

managers

with MBA

educations

to

develop

and

apply operations

research

models

that

heretofore

required systems specialists.

Each of

the

five

application areas

now

has

a rich

body

of scientific

literature,

contributed

by

researchers

working

in

many

different

countries. Several of their

discoveries have been

recognized

in

the Nobel Eco-

nomics

Prize awards of

recent years.

Today

there are

many

well

established and stable

graduate

education

programs

in

operations

research.

At least one

new

comprehensive

text on

operations

research/management science is written every year.

Without

doubt, operations

research

enjoys

a well

func-

tioning professionalinfrastructure.

Universal Benefit

The social

and intellectual significance of all this

research,however, goes beyond

a

summation of tech-

nical

achievements. Obviously, most

managers who

benefit from

using operations researchare not familiar

with the scientific foundations

of the field. Fortu-

nately, the fundamental

managerial significance of

the subject can be articulated

through nontechnical

illustrations. Once again, I will

draw on a personal

experience.

A

manufacturingcompany of a household chemical

product faced the strategicoption of whetherto build

an

expensive new plant,

now or

later,

or add to the

capacities of

existing plants. Operations research has

demonstrated that the

analytic approach appropriate

to

determining

the

right strategic

choice for

this

com-

pany is the same as

it

is for a

company

that

makes

paperboard,industrial solvents, aluminum bars and

sheets, soups

or

breakfast cereals. It

is

the same

whether the enterprise s located

in

Canada,

Belgium,

Malaysia or Czechoslovakia, and

whether

it is

owned

by

individual, private

citizens or

by

the state.

Indeed,

two companies in different industriesand in different

countries

may-assuming

the

firms

are

structurally

comparable-be using

the same

computer software,

structuring

heir

input

data

in

the same

way, studying

the same

printed

formats

of

output,

and

expending

the same amount

of

effort

to

obtain

their

analyses.

Thus,

the

lessons learned

in

one

particular

situation

are

directly

transferableto a host

of

others.

All

this

comparable

activity

can

occur

despite

essential differ-

ences

in

the

companies' profit

economics, organi-

zational

structures,

market

shares, degree

of

plant

utilization,

and so on.

Moreover,

an

experienced op-

erations

research

analyst

from

one of two

companies

can usually complete a similar analysis in the other

within a matter of a

few

weeks.

A

Common

Language

What

has

happened, simply,

is

that over the past 40

years operations research has

pioneered and tested a

form of

language-mathematical

decision making

models-that

effectively

transcends

traditional indus-

trial and national

boundaries.

In

sayingthis

I

am not

simplyasserting

he

universalityof

operations research

concepts.Rather,

I

am

pointing

to a

recently emergent

empirical

fact, namely, that,

at a

given moment, a

managerin a Texas oil company and a manager in a

British cement company may well

be deciding next

month's

manufacturingquantities

by looking

at iden-

tically

formatted

computer

printouts,

obtained

by an

identically formal

optimization or simulation logic,

the

only

differences

in

the

printouts being

the labels

and the units.

By way

of

personal

testimony,

I

have constructed over

the

past

3

years structurally

comparable

models

using

microcomputer

software

that

analyzed production planning

in

a Canadian



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800

/

WAGNER

electricity

generation company, a United

States pho-

tographic film manufacturer,

a French cooking-oil

producer,

a Southeast Asian petroleum

company,

a

British battery manufacturer,

and a United States

company

that makes hotdogs.

Even a

nonexpert can recognize and appreciatewhat

has happened by listening

to how the managers

of

such enterprisesdescribetheir tasks.

For

example,

they

speak of optimizing

their objectivefunction,

remov-

ing the

binding constraints, heeding the

shadow

costs, updating

the productioncoefticients,

reliev-

ing the bounds -technical

phrases

originally coined

by operations

researchers

when the field was

devel-

oping

40 years ago. Today's managers,

actively using

these models, have gotten

the vocabulary right

even

though

they may

never

have

studied the

underlying

mathematics.

That mathematical models actually have unified

many

management decision making processes

around

the world

is no small achievement.

This

unification,

however,

is not the only significant

contribution of

operations

research.

Traditionally,business organizations

typically have

been structured

n

a

way

that takes

limited account of

interdependencies

among decisions

made in different

parts of the organization.

However compelling

the

traditionalstructural ogic may have been

in

the

past,

a

large

company today cannot easily compete

effec-

tively

unless it

recognizes

the

complex

interactions

among functions-marketing,

manufacturing, pur-

chasing, research and development, personnel and

finance-and among divisions,

product categories,

n-

ternational

marketsand

complementary

technologies.

Operations

research models

have contributed

to

breaking

down the historical

organizational

distinc-

tions.

They

have

permitted-or

forced-managers

to

treat their

enterprises

as interconnected

systems.

Widespread

Influence

Often

an

operations

research model

can

strongly

in-

fluence human

perception

and discourse about com-

plex industrial

enterprises.Another

real example

will

illustrate he point. The eventual profitabilityof petro-

leum exploration

activities depends critically

on ac-

curate estimation of oil

basin size. This, however,

is

as

much

a

matter

of

applying experienced judgment

asof

analyzinggathered

seismic information.

Notably,

qualified

experts looking at

the same data

can

differ

enormously

in

their estimates. Before

deciding explo-

rationexpenditures,

a

company must therefore some-

how reconcile

the

experts'

divergent opinions

or

else

choose

whom to believe.

Often

experts reason

by analogy

with other basins

where

seismic data seem

to be similar

and relevant.

Each expert

mentallyprocesses

all the data from

what

he

or she sees as

analogous situations.

The argument

starts

when the experts share

their overall

assessments.

To improve on this approach to syndicating risk, it

has been possible

to construct

a mathematical

model

to calculate

an

estimate

of basin size by

appropriately

combining

a set of elementary

and

independent as-

sumptions which,

when taken

together, encompass

the critical factors.

In one situation, the

experts easily

agreedto the

model's structure,

and from that

point

onward their debate

focused on

different judgments

about detailed assumptions.

The

model combined

the

individual

estimatesexplicitly,

enabling

the expertsto

better understand

the sources of

their disagreements

and

eventually

arrive

at

a

consensus.

An analogous example

occurs

in constructing new

electric

power

plants.

Hiere

xpert

judgment

is used to

assess

the extent

of safety and environmental

protec-

tion

measures that

will

be

required.

The

pivotal

choice

of fuel type (such

as

nuclear,

oil, gas, coal)

frequently

hinges

on

these particular

assessments.

Wholehearted

Adoption

What these

illustrations

exemplify

is that formal

models

have

succeeded

in

organizing

decision

makers'

thoughts, judgments,

beliefs and expectations

in

highly complex business

situations

and

in

encoding

managers'

accumulated

experience.

The formal

pro-

cess have evolved to the point that, in comparison to

40

years

ago,

a successful

application

of

operations

research

is

in

no way

an

extraordinary

event.

Every

year

now,

dozens

of

highly

successful

applications

are

being reported

in

the operations

research

literature

and

at

professional

conferences. Of course

there are

many

more

successes that

do not

get publicized.

Operations

research

models

are,

however,

at

best

approximations

to reality,

and

the

concepts

they

em-

body

are

often

fictions,

figures

of

speech,

and unreal

entities.

A

reliable indication

of

a

successful

applica-

tion is the altered vocabulary of the managers.

Has

the

concept

or

fiction

become real-that is, meaning-

ful-in the decision makers'smind?Today, scenarios,

game plans,

road

maps,

decision trees,

critical paths,

and

contingency

strategies

have taken

their

place

alongside

breakeven

points,

payout periods,

and

the

bottom

line.

What is

especially

noteworthy

about

this

shift

in

vocabulary

is

that

the new concepts

are

far

more

sophisticated

than

the old:

in

effect, operations

research models have helped managers

keep pace

in-

tellectually

with the

growing

size and

complexity

of

the

enterprises

hey

run.



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Of course, some corporations

have been

applying

operations research models

for decades. Where these

models are used for strategicplanning

and

are applied

repeatedly, they eventually

become a form of living

history of the company. New options

are evaluated by

comparison with past performance as

measured and

calibratedby

these models.

The

approach

s a

conven-

ient and effective way to make assumptions explicit.

Value Added

So far,

I

have not

said

anything

about the economic

benefits stemming from technical

advancements in

operations

research.

A

glance

at recent

business his-

tory suggests

that it

is

essentially by leveraging

man-

agement talent that operations research

produces

value added for

industrial enterprises.

For some time now, the business environment has

been growing steadily more competitive. Worldwide

competition

is

the rule, not the exception.

Many do-

mestic

markets have stopped growing.

Shifts in

gov-

ernment control

and

social regulation

have

had a

significant impact. Technological

innovation has

rap-

idly obsoleted products

and industries. At the same

time,

it

has

positioned

some

companies

as

tough

new

competitors

in

a

hitherto stable environment.

Whereas

it

once

may have been proper to compete

politely,

the

emphasis

now

is

on

winning by aggres-

sively exploiting

the

competition's

weaknesses.

This heightened

aggressiveness

n

a world economy

means that corporate managements need

to formulate

strategiesmore thoughtfully than ever before. They

need to consider a broad set of options, analyze them

carefully,

examine their downside

risks and

protect

against these. Companies

must be

prepared

and

able

to react

swiftly

to shifts

in

the environment.

In

this situation, assessing strategicoptions with the

help of

an

operations

research model is a significant

advance over relying on

hunch and

guesswork.

Cor-

porations

have

become

so

large

that an

undisciplined

approach to strategy development

is

just

too risky.

Yet management

talent is scarce and expensive.

An

effective, practical

way

of

leveraging

this talent

is

to

provide executives

with operations researchbackup.

In this way, it becomes feasible for a corporation to

examine

alternatives,

be

prepared

for

contingencies,

and

reassess strategy

when earlier

assumptions

have

to be

revised.

In

the 1950s and 1960s, managers

who encouraged

and

sponsored

the

application

of

operations

research

models were taking personal

risks

in

doing so. Their

expectations

of results

frequently

exceeded

the state

of the

art at

that

time,

and

many

attempted applica-

tions were failures.

Today

the

picture

has

changed.

Practitioners

of operations research are more

skilled,

the

models are more adaptable to the unique

charac-

teristics of

a

particular

business

environment,

and

computing

capability and availability

have increased

manyfold.

Facing Criticism

Despite

the demonstrated

value of all these operations

researchapplications, some

serious criticisms

of op-

erations research

model building applications-some

technical, others

philosophical-remain

to be an-

swered.

I

shall pass over

the technical problems

be-

cause they are

likely to be removed,

sooner or later,

by

further research.The philosophical

and behavioral

objections are

much harder to resolve.

Loss

of Ambiguity. Scholars

of competitive behavior

have recognizedfor a long time that ambiguitycan be

of

positive

value. When

an

executive

says:

I'm

not

sure myself

what

I

am

going

to do, then

competitors

cannot

be

certain either. Keep

the opposition guess-

ing is

another version of

the point.

The

formal process

of model building,

however,

abhors ambiguity.

It

demands

that executives

make

their assumptions explicit,

and

it

presses

decision

makersto articulate their

choice criteria. Even

though

a

model

building effort may start

off

in an

experimen-

tal

framework,

its

assumptions

and

criteria-stated

only tentatively

at

the

outset-will

eventually

be taken

seriously.

It

is

virtually

inevitable that

a

model

that

has been implemented should become a reality in

itself

and

therebycompete

with the real phenomenon

it modeled.

A

mundane

but actual example clarifies the

point.

I

have worked

with

a

data processing

manager

who

keeps

an

up-to-datetally

of inventory availability.

He

does

this

by

decrementing quantities shipped,

which

he obtains

from customer invoices,

and incrementing

quantities

replenished,

which he obtains from

ven-

dors'

bills of

lading.

He never

sees the actual

inventory.

At the

end

of the year,

he insists that

the recorded

amounts of

inventory

on

hand are more accuratethan

the values

that are obtained

in

the

company's

once

a

year physical counting process.

Does

all

this mean

that

companies

that

rely

on

formal

analytic

processes

will

become

predictable,

and

so

possibly

more vulnerable to

competition?

We

shall

have to watch

and see.

Loss of Humanity.

A

second criticism

is that formal

model building

is

antagonistic

to social values,

moral-

ity,

and

recognition

of the individual

as a unique

being.

At

best,

say

the

critics,

these considerations



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/ WAGNER

enter

models

as

constraints nd,therefore,

reviewed

as hindrances; t worst hey are simply gnored.

Undeniably,

an

easy case

can

be made that

as

of

today operations esearch ines up with the techno-

cratic orcesn oursociety.ButIfind thard o believe

that the ultimatesuccessor failureof operations e-

searchwill significantly lter our social ethics or un-

dermine

everence

or

humanvalues.

Loss of Control.The third and final philosophical

criticismof operations esearch pplicationss that a

model,

like Frankenstein'smonster,can get out of

controland overpowerts own creator.This romantic

idea actuallymay have some validity.We note that

modelshavebecome argeandthere s alwayspressure

to make themlarger; hey consume ncreasing uan-

titiesof

data,

and

require

moreandmore

sophisticated

computations.Modelsare not reticentaboutextrap-

olatinghistory

ar into the

future.And

as the

logical

path

from

assumption

nd

data

to

recommendation

becomes

moretortuous,

he

volume of

potential

out-

put

s

exploding.

The

ssue, hen,

s

whether

xecutives

can

increase

heirhuman

powers

of

discernment

uf-

ficiently o remain

n

control

over he

analytic ystems

they

have

nitiated.

I

confess that

of all

three criticisms,

his last

one

worriesme most. Since humankind

has

always

suf-

fered

fromthe

ills of

imperfect ystems, he

issue

is

not whether

erfection

s

attainable:

e

know t

is not.

Rather,

he

worry

sthatthe errors hatemanate

rom

future ystemsmaybecome ncreasingly ataclysmic,

and

hat t

may

not be

possible

o reducehe ikelihood

of their occurrence

ufficiently

o assure hat we

will

be

better

off

in net terms.

Here,

once

more,

we

will

simply

have

to wait

and see.

Future Progress

Philosophical

ssues

apart,

what

progress

an we ex-

pect

o

see

in

operations

esearch

n

the decade

ahead?

In

my view,

three themes

will

be

important:

aster

response, reater roductivity nd increased se.

Faster Response.First, new, valuable echnicalad-

vances

will

be vitally linked, as

in

the past, to the

further evelopment

f

computing oftware nd hard-

ware.

n

contrast

o

medical

research,where cientific

investigationand experimentation roceeds oward

the

eventual

breakthrough

iscovery,

most

of

the

practicalproblemsaddressed y operations esearch

already

have

a

workable

olution,since they are ob-

served n a

real environmentwhere the individuals

involved

havealreadyearned o cope n unscientific

ways.In

operations esearch, he

challenge s to find

truly

ignificant,

osteffective

mprovements vercur-

rentpractices.

There is

no letup in sight for advancements n

computing capability.This will occur in parallel

modes.For

mainframe

pplications,

urther

progress

in

building

argemodels s inevitable.

These models

will

havemoredetail,

extendovermore ime

periods,

and

integratemore functionsand

decisionareas.

At

the same

time, desktopapplications

will

rapidly

x-

pand. This already s

happening

with

optimization

models,

and,

to some

extent,

with simulationmodels

andartificial

ntelligence.

At the

microcomputer

evel,

we are about to see the

adoptionof new

operating

systems

and the

introduction f

software

hat

will

use

faster

coprocessors.

With

such software

advance-

ments,a 386

microcomputer

will

be able

to

do an

impressive mountof workbetween, ay, 6 p.m. one

nightand 8 a.m. the

followingday.

And as

the cost of

mainframe nd

microcomputings

reduced,

new so-

lution methodswill

become economically easible

o

implement.

Greater

Productivity.

The second theme of

progress

relates to increased

productivity. Applications of op-

erations

esearch ave both

directand indirect ffects

on

productivity.

A

direct

way operationsresearch

modelshave

ncreased rofits

s

by

showingmanage-

ment how to

get better utilizationout

of invested

capital.

Sometimes his

happens

rom

exploitingpro-

ductivity differentials,sometimes from reducing

capital

nvestment

and

releasing

unds

for

other

pro-

ductiveopportunities.Withnew

applications

f

larger

models, his thrust

will

continue.

An

indirect

way

in

which

operations research

models ncrease

productivitytems

from

ts

beingan

international

anguage.

As new

productive

echnolo-

gies emerge,their

economic impact is

likely

to

be

studied

with

he

help

of

operations

esearch

valuative

planningmodels.As

advantageous roductive ptions

are

discovered,

heirworldwide

ransfers

likely

o

be

more

rapid.

IncreasedUse.

The

final

hemerelates o

the

concept

of

progresstself,

and its true

nature.

An

example

will

serve

to

pinpoint

the issue.

For

about

a

decade,

an

argument aged

n

the United States

among

a few

intellectual

iants

including

Nobel

Prize

winner)

as

to whenan

electronic

omputer

wouldbeable o

play

master's

evel

chess. The

frameof

referencewas the

technical

capabilities

of state

of

the art

large-scale

computers

is-'a-vis

he

skills

of world

championship



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chess players. While volleys

of invective were flying

among the distinguished

debaters,

to the amusement

of onlookers, some clever

computer designers per-

fected small-scale,

special purpose devices.

As

a

result,

anyone today can buy for about $100 a desktop com-

puter

that

plays

very proficient

chess-not at grand-

master level to be sure, but quite

well enough to beat

the

majority

of amateurs. For a multitude of

average

level chess players, this represents

a very noteworthy

advance-and at that level there is plenty of

action.

Analogously,

for many years a few operations re-

searchers have been foretelling

the day when senior

level corporate

executives will interact directly via

computer monitors to make

major decisions. Over

the past couple of years,

I

have

observed

that many

senior executives

are

making frequent

use of micro-

computers.

Photographsof CEOs with

microcompu-

ters on their desks appear often now in business

magazines.

Doing what

if

analysis using

spread-

sheet models and gathering

factual information

from

stored

data bases have become

routine for

many

high level

managers.

But the most impressive progress

hat is being made

relates to the

emergence of operations research as a

pervasive

and international

approach

o

business

strat-

egy.

When model building was

in

its

infancy,

its

proponents could only predict,

not demonstrate, suc-

cess-the

hard evidence

was

not yet in, and

only a

few

technicians had any knowledge of the

approaches.

Managers

who

had

even a nodding acquaintance with

quantitative methods were

labeled as specialists.

Today

all of this has changed. Thanks to the

dem-

onstrated effectiveness of operations research, many

executives

throughout the

typical large business orga-

nization

understand he underlyingconcepts of formal

model

analysis and make constant use of this lan-

guage.

The term

specialist,

if

it

still has

any

meaning,

pertains to

functional and institutional

knowledge,

not modes of analysis and

problem solving. The prog-

ress to watch forin the next

10 years will not be visible

in the form of electronic gear sitting on the chief

executive's desk.

Rather, it

will be

evident

in

the

growing and

continuing successes of general

managers

who are making consistent

and effective strategic use

of formal

operations researchmodels.

Acknowledgment

This paper is the text of the Harold Lamder

Memorial

Lecture

presented

on

May 25,

1988,

to the 1988

meeting

of

the

Canadian

Operational

Research

Soci-

ety

in

Montreal, Quebec,

Canada.

I

am indebted to

the

Program

Committee

for this

meeting

for the in-

vitation to

present this lecture, and to the Harold

Lamder Memorial Trust for underwriting the

ex-

penses

associatedwith it.

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