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Probability & their application in Business

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ALLAMA IQBAL OPEN UNIVERSITY(Department Of Business Administration)

SEMESTER: AUTUMN 2010

BUSINESS MAHEMATICS & STATISTICS

Code no 523

SIDRA MUDASSERAH 525501

ASSIGNMENT # 2



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ACKNOWLEDGEMENT

Isincerely feel that the credit of this project work could not be narrowed to

only one individual as the whole work is outcome of integrated efforts of my

instructors and their hand outs and their cooperation which results into

completion of this project.


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ABSTRACT

Integration of MS office has been designed to simplify the use ofthe various

Microsoft Officetools while still offering users the optionto continue working with

what is familiarto them. Previously set uptemplates and forms can continueto be

used by organizations enablingthemto seamlessly integratetheirexistingprocesses

into new ones with Integration of MS office. This factalone willpromote user

adoption substantially and demonstrate Integration of MS officeease of use.


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TABLE OF CONTENTS

Probability..5Types of Probability...5Microsoft Excel...10-11Microsoft PowerPoint ..12Microsoft Access .13MS office Integration ..14Benefits of Integration .15Share Point Integration ...15-18MS Access & MS excel Integration ...19-23Case Study of SPO..23-33Conclusion ...35


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1. PROBABILITY

Probability theory is an importantpart of statisticaltheory that bridges descriptiveand

inferential statistics. It is the science of uncertainty or chance, or likelihood. A

probability valueranges between 0 and 1 inclusiveand represents thelikelihoodthata

particularevent will happen. A probability value of 0 means there is no chance that

and will happenand a value of 1 means there is 100 percent chancethattheevent will

happen.

Understandingprobability is helpful for decision-making. Conductinganexperiment

or sampletestprovides an outcomethat can be used to computethe chance ofevents

occurring inthe future.

An experiment is the observation of some activity or the act of taking some

measurement. Whereas, an outcome is a particular result of an experiment. The

collection of one ormore outcomes ofanexperiment is knownas anevent.

Forexample,amarkettesting ofa sample ofnew breakfast cereal,new beer,new

wine,new magazine,etc.gives theDirectorof Production orDirectorof Marketinga

company apreliminary idea(outcome) whetherconsumers would liketheproduct if it

is produced and distributed in bulk.

2. TYPES OF PROBABILITIES

There are three definitions of probability. The first one is known as classical

probability. The classical definitionapplies whentherearenequally likely outcomes

to anexperiment. It is obtained by dividingthenumberof favorable outcomes by the

totalnumberofpossible outcomes.

The second one is Empirical probability that is based onpast experience. This is

determined dividing the number of times an event happens by the total number of

observations. Forexample:


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Theprobability that your income tax return will beaudited if thereare two million

mailed to yourdistrict officeand 2,400 areto beaudited is 2,400/2,000,000 = 0.0012

or0.12%.

The third is asubjective probability. Subjectiveprobability is aprobability assigned

to anevent based on whateverevidence is available. It is aneducated guess.Unlike

empiricalprobability, it is not based onpastexperience.

Subjective probability is obtained by evaluating the available options and by

assigning the probability. Examples of events that require computing subjective

probability:

(1)Estimatingtheprobability thataperson wins a jackpotlottery.(2)Estimatingtheprobability thatthe GM willlose its firstranking in carsales.

3.BASIC RULES OF PROBABILITY

No

.

EVENTS FEATURES KEY

CONNECTING

WORDS

APPLICABL

E RULE

FORMUL

A

1 Mutually

exclusive

No overlapping

events - if one

event happens the

otherone cant

occuratthe same

time

Theprobability

ofA occurringor

theprobability of

B occurring

SpecialRule of

Addition

P(AorB) =

P(A)+P(B)

2 Not

mutually

exclusive

(Joint/

Compound)

Overlapping/

Concurrentevents

two ormore

events happenat

the sametime

Theprobability

thateitherA

may occurorB

may occur

followed by the

possibility that

bothAandB

GeneralRule

of Addition

P(AorB) =

P(A)+P(B)

P(Aand

B)


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Probabilit

& t ir appli ation inB

iness

7

may occur.

Independent The occurrence of

eventA has no

effect on the

occurrence of

another eventB

The probability

thatAandB will

occur

SpecialRule of

Multiplication

P(A and B)

= P(A)P(B)

Conditional Dependent events

the probability of

a particular event

occurring given

that another event

has occurred

P(B|A)

probability that

eventB will

occurgiv n that

eventA has

already occurred

GeneralRule

of

Multiplication

P(A and B)

=

P(A)P(B|A)

5 Complemen

t

All events in the

sample space that

are not part ofthe

specified event

determined by

subtracting the

probability of an

event not

happening from

the probability of

happening

Event Not

occurring

or

Neither/nor will

happen

Complement

Rule

P(A) = 1

P (~ A)

3.DISCRETE PROB BILIT DISTRIB TIONS

Probabilities values for experiments whose outcomes are numerical are known as

randomvar

iable

s. Random variables can be discrete (have a finite number of samplespace) or continuous (have an infinite number of sample space). An example of

discrete probability distributions is binomial distribution.

Properties ofBinomial distribution:


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y Each trial(X)may be selected from infinitepopulation withoutreplacement orfroma finitepopulation(N) with replacement.

y Each trial(X) is mutually exclusiveand collectively exhaustivey Each trial(X) has two possible outcomes, success orfailure.y Each trial has a fixedprobability of success orfailure().

5. THE PRINCIPLES OF COUNTING

Ifthenumberofpossible outcomes inanexperiment is small, it is relatively easy to

count them. If, however, therearea largenumber ofpossible outcomes, it would be

tedious to countall thepossibilities. To facilitate counting, three counting formulas

will be examined: the multiplication formula, the permutation formula, and the

combination formula.

A) The Multiplication Formula is:

Totalnumber of arrangements = (m)(n).

Example: If a salesperson has seven shirts and 5 ties to display, 35 outfits are

possible. This means he or she has 35 different ways displayingthe outfits.This can

beextended to more than two events. For threeeventsm, n, and o: Totalnumber of

arrangements _(m)(n)(o).

B)The Permutation Formula

The permutation formula is applied to find thepossible number of arrangements

whenthere is only one group of objects. Orderis very important inpermutation. Note

thearrangements a b c and b a c are differentpermutations. The formulato countthe

totalnumberof differentpermutations is:


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where:

n is thetotalnumberof objects (pool).

ris thenumberof objects selected atatime.

Before we solve the two problems illustrated, note that permutations and

combinations useanotation called n factorial.It is writtenn! and means theproduct

ofn(n-1),(n-2),(n-3),(n-4),(n-5),etc. For instance, 5! Means 5 x4x3x 2 x 1 =

120.

By definition, zero factorial, written 0!, is 1. That is, 0! = 1.

Example: A flag with three stripes of3 colors can useany of six colors. How many

flags arepossible? This is apermutationproblemand the orderofarrangingthe flags

is important.

nPr= 6P3 = 6! = 120

(6-3)!

C)The Combination Formula

Ifthe orderofthe selected objects is not important,any selection is called a

combination.The formula

to countthenumberofrobject combinations froma set ofn objects is:

Example: You have6 colors to choose fromand you wish to choose3 fora flag. How

many choices arepossible? This is a combinationproblemthe orderofarranging

colors innot important.

nCr = 6C3 = 6! = 20

3!(6-3)!


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6. APPLICATION IN THE BUSINESSIn the quest to achieve a sustainablemaximum level ofprofit and profitability in

business,analyticaltools are oftenput into play thataddress thequantitative

Methods of business.

The use of these tools helps to determine theeffectiveness of givenprograms of

operation and can help demonstrate which areas of business need to be subject to

refinement oradaptationto oncemoreminimizeloss and maximizeprofit.

Through addressing the quantitativemethods of business, those inmanagementand

cost- reassessmentprograms can betterunderstand the forces thatareat work intheir

respective departments and within the business environmentas a whole, essentially

accomplishingagreaterdegree of controloverallareas of business.

This paper three investigate many of the commonly- used analytical tools and

concepts thatare used to helpeffectuatedecisions ina business environment.

This paper shall break down the investigation of these analytical tools into three

different

areas of study to better limit the discussion: The first area is that of the localized

department, wheretheanalyticalmethods can beapplied to a specific area of business

such as shipping & receiving ororderprocessing.

The second area is that ofthe business as anentity, wherethe business is examined as

a separateand wholeentity, orthe sum of its localized departments. Finally,thethird

area shall bethe overall business environment in which a specific business is located,

where for example a law firm would be examined according to its position and

business strategies withintheentirelaw industry.

Thetypes ofanalyticalmodels that shall be investigated inthispaperareprobability

theory and statistics, utility theory and gametheory. Through investigatingthesetools


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in this three- tired framework, the readerwill haveagreatercomprehension of how

analytical tools can be used to accomplish various reforms withina business or the

business sectoras a whole.

7. PROBABILITY THEORY AND STATISTICS

This section shall explore the use ofthemodel forprobability theory and how this

theory functions withina business environment. Probability theory is almostalways

used inrespectto statistical figures as this theory is useless without statistics through

which to supportthe initialassessment.

Introduction to Probability Theory

Therole ofprobability theory inanalyticalmethodology has been called thecommon

sense approach to investigating specific areas of study, for probability theory

suggests that the best way of investigating a problem is to combine a previous

knowledge ofevents with thelikelihood ofevents manifestingagain.

The overallassumptions that can be drawn fromprobability theory havea significant

margin oferror (inmost circumstances) but the single solution that can be drawn is

what is mostlikely the situationthat will occurgivenapredetermined set of variables.

Essentially, thenature ofprobability theory has been traced to standard Aristotelian

logic, which reduces a situation to be applicable to deductive according to the

assumptionthata situation can be defined aseithertrue or false.

When a given situation is presented, a researcher can then address the scenarioaccording to true or false qualities (as shall be explored ata laterpoint in this

section). This method 2011 119 therefore allows for various generalizations to be

made based upontheavailable data frompre- existing situations,and is a usefultool

forcreatingthemostlikely outcomes foragiven scenario.


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Take the following citation as a means of getting a better overview of the

methodology and functionality ofprobability theory in respect to related events, for

this is thearea in which probability theory is most frequently utilized in business.

Inthe followingexamples,throwing dice is used to illustratethenature ofprobability

theory in respect to statistics, where theprobability of rolling a specific number is

manifestaccordingto thenumberof dice used and thetimes takento rollthe dice.

Independent or related e ents?

One of the important steps you need to make when considering the probability of two

or more events occurring. Is to decide whether they are independent or related events.

Examples:

Independent or Mutually exclusive events

Theprobability ofthrowinga doublethreewith two dice is theresult ofthrowing

three with the first dieand three with the second die. Thetotalpossibilities are, one

from six outcomes forthe firsteventand one from six outcomes forthe second,

therefore(1/6) *

(1/6) = 1/36th or 2.77%.

Thetwo events are independent, sincewhateverhappens to the first die cannotaffect

thethrow ofthe second; theprobabilities arethereforemultiplied,and remain 1/36th.

Related or Mutually inclusive events

1. What happens if we wantto throw 1 and 6 inany order? This now means that we

do notmind ifthe first die is either1 or6,as weare still in with a chance. But with

the first die, if 1 falls uppermost, clearly It rules out the possibility of 6 being

uppermost, so thetwo Outcomes, 1 and 6,aremutually inclusive, Oneresult directly

affects the other.


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Inthis case,theprobability ofthrowing 1 or6 with the first die is the sum ofthetwo

probabilities, 1/6 + 1/6 = 1/3.

2. Theprobability of the second die being favorable is still 1/6as the second die can

only be one specific number,a6 ifthe first die is 1,and vice versa.

3. Therefore theprobability ofthrowing 1 and 6 inany orderwith two dice is 1/3x

1/6 = 1/18. From theabove citation, it can be seen that givenapredefined set of

variables inthe context of what willmostlikely occur inagiven scenario (whereall

potentialexternal variables remain constant),a specific answerto the situation can be

assessed.

Becauseoftheselimitations,probability theory inbusiness is doneaccordingto past

variablesand existing business situations and is notfrequently used to predictearth-

shatteringbusiness decisions but rather is more limited in its scope, for it is not a

method of businessassessmentthat is infallible.

It must be stressed that the existence of problems corresponding to the

overgeneralization of a specific scenario tends to create complications in certain

situations,where onearticlenotes that:

Probability theory and its offspring, inferentialstatistics, constitute perhaps the most

frustrating branch of human knowledge.The abstractness of formalmethods scares

many people, andleads to frequent errors in the choice of procedures.(Simon &

Bruce: 1993)

Indeed,the overwhelminguse ofprobability theory is a usefultoolonly in scenarios

wheregeneralizations arepossible:

No one would build a space shuttlebased upon theresults ofprobability theory,but

one could theoretically predict ordering a preset number ofproducts for a given

monthbased uponthis method.


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of smallpox, where the mathematician DAlembert attempted to determine the risk

assessment found within inoculationto smallpox.

This source notes: One urgent social issue which prompted much argument about

probability [] was inoculationagainst smallpox. Theprocedure was notrisk free. It

involved impregnatingthe skin with live smallpoxpustules and could lead to death.

At the same time, however, over 10 percent of thepopulations of Londonand Paris

were beingkilled by the disease. ForDAlembert the choice was approximately as

follows:a 1 in 7 longer term chance of dying of smallpox versus a 1 in 200 short-

term chance of dying fromthe inoculation.

Complicatingthe issue was thegenerally smallaveragelifeexpectancy in Paris. In

addition, conservative opinion inFrance, includingthe Faculty of Medicineand the

Faculty of Theology atthe University of Paris, had come out strongly against

inoculation.

Daniel Bernoulli [another mathematician] applied his probability formulae for

lotteries to theproblem of smallpox. He calculated thenumberofpersons likely to be

killed by smallpox in a given time. Then, he calculated the gain in life expectancy

from inoculation forany givenage. His formulas were necessarily rather crudeand

the statistics ofmortality inadequate, but his results definitely favored inoculation.

This risk- versus- gain stillexists inthemodern business environment, where

companies that do business that contains acertainelement ofriskneed to beaware of

thelikelihood of injury to a customer,eitherindependently (insurance) oras aresult

ofproduct use(carmanufacturing).

The end result of this assessment helps to create theparameters around which the

business can be conducted with the maximum amount ofprofit according to the

minimum amount of inherent risk. To enumerate this concept further through a

quantitativemethod,theresearcheruses theexample of stocks ofa company Genco.


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Supposea broker buy shares of GencoCorporationataprice of $100 per shareand

intend to keep it on hold forone yearfora dividend yield of3%.

Totalrate ofreturn ofthe share can beestimated by the formula: Supposetheprice of

the stock changes by 7% while its yield remains at 3% in one week, then the total

return is R= 3 + 7 % = 10%. Gencos stockprice may yield 10% or it may yield

differently butthe yield will bearound 10% inthe shortrun.

The change in Gencos stocks price is basically based onthe volatility theory. Fora

short term theprobability of the returns is easy to gaugebut in the long run, the

volatility rate is quite different.

It is based onarange of deviation orprobability distributionrange.Forexample ifthe

possibility ofnotgainingreturn is zero thentheprobability ratio of 10% return can be

assigned as one. To determine the long term return, the broker will estimate

probabilities of Gencos stockina weak,a strongand normaleconomy andobtains

the following figures:

State ofGenco Probability

Strong30% 0.20

Normal 10% 0.60

Weak-10% 0.20

Usingtheaboveprobability returns thebrokercan furthercalculatetheexpected

returns:

Expected Returns = Sum of(Probability ofReturn)x(PossibleReturn)

Fromthetable ER= 0.20 X30% + 0.6x

10% +0.2 x -10% = 10%

Thus,themostlikely returnthata brokerwillgain is 10% forGencos stock. The

utility ofprobability theory canextended furtherifthe brokerdecides to comparethe

price of Gencos stockand that of its competitors. The outcome willgive himan idea

as to which stockis a betterinvestmentand which oneposes a higherriskforreturns.


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Utility Theory

Far easier to understand than probability theory and arguably the method of

quantitative assessment that is most frequently used in business is that of utility

theory. This section shall define utility theory and how advances in technologyhave

created an environment in which this method of assessment is most effective in

predicting consumerresponses.

Introduction to Utility Theory

The functionality of utility theory is best found inmeasuring thepreferences ofan

individual or a target group of individuals and measuring the impact of these

preferences on behaviors and beliefs. This practice should sound familiar to any

student in business, for the study of utility theory is the study of consumer buying

behaviors.

In utility theory, the individual that is targeted for assessment is believed to be a

rational individual who determines his orherbehaviors accordingto obvious needsor

desires. The scale against which these behaviors are measured is called a utility

scale, and the ranking placed upon this scale helps in the overall assessment of

behaviors.

The study of utility theory is by no means anew area, but it has becomea science

overthepast five decades. Theearliestmethods of utility theory ineconomic practice

can be traced to a mathematician who suggested that consumer behavior was

essentially predefined according to setpatterns of behavior,and that these behaviors

could be slightly altered orexploited:

In his book The Theory of Value, author Debreu (1959) suggested that consumer

behaviors could be understood in the sense that these behaviors created an

equilibrium forconsumers,where if someoneneeded aproducttheywould seekout

thatproduct.


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Those seekingto encourage consumers to changetheirbehaviors would notgetthem

to altertheirneed but could instead altertheirdesires.Forexample, if someoneneeds

drain cleaner,this need cannot be changed buttheirdecision of which drain cleanerto

buy can bealtered accordingtoknowledge ofthe utility theory.

Utility Theory in the LocalizedEnvironment

The use of utility theory in specific sections of a specific business is not overly

functional: Utility theory helps predict behavior ona larger level, which in turn can

impact the localized environment. However, there is very little direct interaction

between utility theory and thelocalized environment.

Utility Theory and the BusinessEnvironment

Utility theory inthe business environment is one ofthemost widely- used assessment

tools forquantitativeanalysis. Thepurchasing behavior of consumers is what drives

businesses,and those businesses that fully understand theirtarget consumeraudience

and how to bestappealto themare farmorelikely to succeed than companies that do

notrecognizethis basic area of commerce.

Companies that wish to succeed haveevengone so faras to utilizenewmethods of

technology to keep track oftheirconsumerspurchasing behaviors in order to better

tailor their businesses and therefore draw ingreater consumer interest. Perhaps the

bestexample ofthis use ofnew technology is inthe supermarket:

It is a common feature in supermarkets to find atype ofmembership discount, where

customers that are part of the supermarkets community are offered discounted

savings whenthey runtheirmembership card.

However, inadditionto offering savings,themembership card also creates atally of

the customers purchasing behaviors, includingthetype ofproductpurchased andits

size, cost,and thetime ofmonth it waspurchased.


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The supermarket can therefore use these membership cards to either offer the

customer increased savings or provide incentive for the customer to return, or to

simply chartthetypes of buying behaviors displayed by theircustomers.

Utility Theory and the RespectiveIndustries

Those familiarwith business willrecognizethatentire industries have been compiled

around utility theory Advertising is a multi- billion dollar industry that all

companies tend to rely uponto some degree,and the use ofadvertising suggests that

all companies areaware oftheneed to assess consumerbehaviors.

According to the use of utility theory and other methods of statistical assessment,

companies determine all manner of business strategies including releasing new

productlines, changingexistingproducts,and adaptingexisting strategies to better fit

the demands of consumers.

Yet despitethetraditionalreliance on utility theory in industries such as advertising

as wellas the businesses that benefit fromthe use of utility theory thereare inherent

flaws withinthis type ofmethod that cannotbe overlooked.

One article from the renown industry digest Quirks Marketing Research Review

(Lawson & Glowa: 2000) suggests that therearefour limitations in utility theory,

wherethis method is lacking inthe following:

a. Forecasting demand fornew, orcategory- revolutionizingproducts.

b. Forecasting demand forproducts in categories in which new and differentattributes

which werenotpreviously a consideration in choiceare introduced.

c. Forecasting demand for products in categories in which attribute ranges are

stretched well beyond those inpresentmarkets.

d. Forecasting demand inmarkets in which serious multicollinearity is presentamong

thepredictorvariables.


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Also,this source suggests thata wholesalereliance onadvertisingas a solutionto all

problems in business is a flawed perspective, because utility theory is based uponthe

belief that customers can be manipulated according to their choices made when

buyingaproduct. Theauthors notethat:

Real customers who make choices inrealmarkets frequently decidenotto choose.

That is, forwhateverreason, some customers chooseto delaypurchases orchooseno

products offered inaparticularcategory.

Examples of the former customers include millions ofpotential customers for PCs

who know they eventually wantto haveoneat home, butare waitingeitherforprices

to drop further, or functionality to increase, orboth. Examples ofthelattercustomers

include those who are allergic to chocolate, and hence, will not buy chocolate

products regardless ofthe category in which they are offered.

Reliance on advertising inan industry can therefore be concluded as a science, but

perhaps not the most exact science necessary to predict all aspects of consumer

behaviors.

This is apparently why markets constantly emergethat wereeitherunderestimated or

unpredicted,and markets that were seenaspotential booms collapsewithout warning.

Game Theory

Unliketheassessmentmodels ofprobability theory and utility theory,gametheory is

best utilized according to human created conditions within a specific working

environment. Gametheory is extremely diverse in its scope yet can begeneralizedashow human beings or larger entities such as organizations interact within certain

conditions.


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AnIntroduction to Game Theory

The study of game theory is the study of human behavior within a specific

environment. When applied to business, the use ofgame theory helps to determine

how an individual ora business willreact withinthatenvironment.

In terms ofqualitativeassessment,gametheory is unique because italso brings into

play elements of humanpsychology that arenot generally found within standard

assessmenttechniques, such as philosophy andaltruism.

For example, one of the best- known elements of game theory is the prisoners

dilemma: Simply put, the matter of game theory in the prisoners dilemma

suggested that in a prison setting where two individuals were being questioned

separately and the one who talked first was (theoretically) offered freedom, either

both parties could benefit slightly, one party could benefit significantly, or both

parties could sufferseverelosses.

Theresponse ofthe otheris dependent uponnot only theirown desires,butalso upon

thequestionable benefits thatthey mightreceive ifthey remain silent. This aspect of

gametheory is summarized fromanarticlepublished intheJournal ofthe History of

Economic Thought. AuthorRoth (1993) writes. InJanuary of 1950, MelvinDresher

and Merrill Flood conducted attheRand Corporationanexperiment which has had an

enormous if indirect influence, since it introduced the game that has subsequently

cometo beknownas the Prisoners Dilemma.

The game they studied was the hundred- fold repetition of the matrix game given

below, betweena fixed pairof subjects who communicated only theirchoices ofrow

(1 or2) orcolumn(1 or2).(-1,2) (1/2, 1)(0, 1/2) (1, -1) Payoffs were inpennies, with

each playerreceivingthe sum, overtheone hundred plays ofthegame, of hispayoffs

ineach play.

The unique Nash equilibriumprediction is that theplayers should choose (2,1) - the

second row and the first column - ateach ofthe hundred repetitions. Thus the


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predicted earnings of theplayers are 0 for the row player (henceforth Row) and

$0.50 forthe columnplayer(henceforth Column).

Of coursethis is inefficient, since iftheplayers instead played (1,2)ateveryperiod,

forexample,theirearnings would be $0.50 forRow and $1.00 forColumn i.e.they

would both earnmore.

But this is not equilibrium behavior. That equilibrium play is substantially less

profitable than cooperativeplay madeDresherand Flood anticipate correctly that

this game would presenta demandingtest oftheequilibriumpredictions.

The observed payoffs, forapairofplayers [] were $0.40 forRow and $0.65 for

Column. This outcome is far from the equilibrium outcome, although it also falls

considerably short ofperfect cooperation.

(This observation has since been replicated many times.) Dresher and Flood

interpreted this as evidenceagainstthegeneral hypothesis thatplayers tend to choose

Nash equilibrium strategies,and in favor of the hypothesis thata cooperative split

the difference principle would bemorepowerful in organizingthe data fromgames

ofthis kind.

Yet in a business setting, the welfare of a single company is weighed against the

potential benefits or losses that might be incurred from interaction with another

company. Gametheory is one way in which to bestpredictthe outcome ofencounters

between individuals and between organizations,as shallnow be clarified:

Game Theory in the LocalizedEnvironment

Game theory in a localized environment is most often utilized as part of the team

buildingprocess, wheremembers ofa specific departmentarerequired toparticipate

inteachingtools that utilizegametheory as ameans of betterunderstandingtheirrole

in business. Forexample, intheaforementioned Randolph Glassworks thereseems to


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bea competition betweentwo men inthe design department fortheattentions oftheir

manager.

Themanagerrecognizes this competitionas unhealthy only when one ofthemen

seeks to sabotagethe others labor.Evenafterseveralmeetings designed to solvethe

problem,themanageris ableto tellthattheproblempersists and is afraid that it will

againerupt ina way that will harmthe workof one ofthemen.

Themanagerdetermines thata day ofteam building is the best way to rectify this

situationand he determines thatgametheory is the bestmethod ofteambuildingto be

used: Not only is themanagerableto bring his department closerthrough interaction

but he canalso taketheresults ofthe survey assessments provided to thedepartment

and see if he can usetheresults to alleviatethepressurethatexists betweenthetwo

men.

Game Theory in a BusinessEnvironment

Therole ofgametheory ina business environment is remarkably likethat found

withinthelocalized environment, wheretheuse ofgametheory can be doneto

determinethe interactions of various departments and individuals,and theresults of

thetesting canthen beevaluated to see how team- building can beaccomplished.

Inaddition,gametheory is occasionally used on customers fora specific business to

evaluate how the customersperceivethemselves withintheirrelationshipto the

business: Surveys andphone interviews are often used to facilitatethis type of

quantitativeassessment.

Game Theory in the RespectiveIndustries

Gametheory is so widely varied interms of its use, its features,and its impactthat it

is rarethatanencounterbetweentwo businesses cannot beexplored viaa function of

gametheory.


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For example, imagine a situation where two businesses were planning to merge:

These businesses thenneed to assess their relationship to each otherand the use of

gametheory can help inpredicting how one business willreactto themerger. Game

theory cantherefore serveas ameans of understandingthepositionofthe otherentity

and willthereafterhelpencouragea facilitated relationship betweenthetwo.

7. CASE STUDYAbout enterprise Attock Petroleum limited, Ltd.

AttockPetroleum Limited (APL) is anassociate company ofthe AttockOil Group of

Companies, which is the only fully vertically integrated Group inthe Oil & Gas sector

of Pakistan involved in Exploration & Production, Refining & Marketing. APL's

corporate head office is registered in Islamabad.

Groups of goods Attock Petroleum limited, Ltd.

Automobile oils, Automotive gear oils, Oils for automatic gearboxes, Oils for

automatic transmissions (automatic), Oils forautomotive torque converters, Oils for

passengerautomobiles, Oils fortrucks and heavy machinery.

Attock Petroleum limited, Ltd. Address: 6, Faisal Avenue, F-7/1, Islamabad,

Pakistan http://1959.pk.all-biz.info

Applying Probability to Impro e Business Performance Helping

Company to Look Forward

Today,enterprise softwareprofessionals continue to mull overwhat capabilities they

canadd to theirproducts both to createmore value fortheircustomers and to identify

unique differentiators that allow them to compete more effectively versus their

competition.

IfThe Graduate was recreated inthe year2009and the workingprofessional had deep

experience inenterprise softwareapplications to help improve business performance,

it is likely thatthe one word that would haveagreat future would beprobability.


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26

Theapplication ofprobabilistic models to helpaddress business problems is nothing

new, but the breadth of understanding of these models, and the ability fornon-

mathematicians to apply them to help tune both financial and operationalperformance, is coming ofage.

ProfessorJames Smith ofDuke Universitys Fuqua School of Business says, From

anacademic perspective,predictivemodels haveadvanced both interms ofaccuracy

and flexibility, and these technologies can now be applied to a wide variety of

business forecastingproblems. It is clearthatthesetechnologies havethepotentialto

haveatremendous impact on day-to-day financialand operational decisionmaking.

Severalperformancemanagement vendors have been touting theapplication of this

type of mathematics in their applications with varying degrees of success. In its

simplest form,predictiveanalytics provideaplatform fora business userto help him

feed historical informationand key business drivers into a financial model and use

thatmodelto help definearange of futureperformance with aprobabilistic degree of

certainty.

Although notthe crystal ballthatmany execs would dreamto have,this approach has

significantadvantages over the traditionalreview of historic financials and applying

x% growth when considering future business forecasts and budgets.

On an operational level, companies are applying this type of analytics in both the

sales and marketing department, as well as with human resources. In the cellular

communications and credit industries, customer churn has always beena topic that

executives recognizeas the driverforsuccess.

The companies that could better identify potential customers who were consideringdefecting to a competitor could have the opportunity to take steps to ward off that

defection. Several banks and cellphone carriers apply sophisticated models to help


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27

identify early signals of waning customer satisfaction or cost sensitivity and take

proactive steps to retainthose clients.

Forward-thinking human resource departments are also using these predictive

capabilities to identify valuableemployees who may beatrisk. Assumingthere is aninterest inretainingan atrisk employee,managers cantakeproactive steps interms

ofenhanced communications, compensationand/orpromotionto helpassurethatthe

employee stays focused onthe jobat hand.

In summary,predictive capabilities continue to evolve, and there are technologies

now available that do not require an advanced degree in mathematics to apply to

pressing business issues. For companies that are ready to proactively embrace this

next stage ofperformance management, they will likely find themselves with an

advantage over their competitors who rely on the more traditional approach to

financialand operationalplanning.

y The HR Manager of the company is responsible for market trend analysis,services developmentand technology vendorrelationships atthe company,the

leading independent authority on business performance management (BPM)

solutions.

y Prior to BPM Partners, John was Senior Director, Product Marketing atHyperion Software, responsible for directing Hyperion's OLAP Business

Analysis financial softwareproducts.

y Earlier in his career, John was an end user of performance managementsolutions while aproduct manager atRaychem Corporation, a Fortune 500

company that has since beenacquired by TycoHRManagerhas contributed to

many publications including the New York Times, BPM Magazine,

Information Week, Business Financeand eWeek,and he is aregularpresenter

atperformancemanagementrelated conferences and web seminars.


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Probabilit

& t eir appli ation inB siness

RE ERENCES

y Aumann, R. J. & S. Hart. (1994) Handbook of Game Theory with EconomicApplications. Amsterdam: North- Holland.

y Axelrod, R. (1984) The Evolution ofCooperation. New York:Basic Books.Baird, D. G., GertnerR. H. & Picker, R. C. (1994) Game Theory and the Law.

Cambridge Mass.: Harvard University Press.

y Bandura, A. (1977) Social Learning Theory. Englewood Cliffs: Prentice-Hall.Carroll, J. S. & Johnson, E. J. (1990) Decision Research: A Field Guide.

(Applied SocialResearch Methods Series: Volume 22).

y Joyce, J. M (1999) The Foundations ofCausal Decision Theory. CambridgeStudies in Probability, Induction, and Decision Theory. Cambridge:

Cambridge University Press.

y Lawson, S. & Glowa, T. (2000) Discrete Choice Experiments and TraditionalConjoint Analysis. Quirks Marketing

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