chapter 8

7/18/2019 Chapter 8

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Chapter 8

Decision Support Systems

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Managers and DecisionMaking

Why Managers Need the Support of InformationTechnology. It is very difficult to make good

decisions ithout valid! timely and relevantinformation.

Num"er of alternatives to "e considered is increasing

Many decisions are made under time pressure.

Due to uncertainty in the decision environment! it is fre#uentlynecessary to conduct a sophisticated analysis.

It is often necessary to rapidly access remote information.

$ decision refers to a choice made "eteen alternatives.

Can we make better decisions?Can we make better decisions?

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Decision Process

Decision makers go through a fairly systematic process.

Act on it

Act on it

Review It

Review It

Definethe

“Process or Problem”

Definethe

“Process or Problem”

DevelopAlternative

Courses of Action

DevelopAlternative

Courses of Action

SelectThe “Best”

One

SelectThe “Best”

One

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The nature of decisions

Information systems can supportdecision%making levels. These includethe three levels of managementactivity.

Strategic management! tacticalmanagement! and operational

management.

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Strategic management

$ "oard of directors and an e&ecutivecommittee of the C'( develop long%range planning.

Decisions made at the strategic leveltend to "e unstructured.

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Tactical management

Mid%level mangers deal ith middlelevel management activities such asshort%term planning! medium rangeplans and control.

Decisions made at the tacticalmanagement level tend to "e semi-

structured.

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(perational management

(perating managers deal ith day%to%day operations of an organi)ation!such as assigning employees to tasks!or placing or purchase an order.

Decisions made at the operationalmanagement level tend to "e more

structured.

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Structured decisions

Structured decisions are repetitiveand routine pro"lems for hichstandard solutions e&ist.

'&* finding an appropriate inventorylevel! finding an optimal investmentstrategy.

MIS primarily analy)es structuredpro"lems.

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Semi%structured decisions

Semi%structured pro"lems fall "eteenstructured and unstructured pro"lems.

(nly some of the phases are structured in

semi%structured pro"lems. It re#uires a com"ination of standard

procedures and individual +udgment.

'&* annual evaluation of employees! trading"onds! setting marketing "udgets forconsumer products.

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,nstructured decisions

,nstructured pro"lems are novel and non%routine! comple&.

-or unstructured pro"lems e cannot

specify some procedures to make adecision. '&* e&panding the "usiness! moving

operations to foreign countries. IS must provide a ide range of

information products to support these typesof decisions at all levels of the organi)ation.

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Decision Complexity

Decision making ranges from simple to very comple& decisions thatfall along a continuum that ranges from structured to unstructured.Structured processes refer to routine repetitive pro"lems ithstandard solutions. While ,nstructured are /fu))y!/ comple& pro"lemsith no clear%cut solutions.

Important

Information

Unstr uctured

Semistructured

Str uctured Its been

done beforeOperational

Tactical

Str ategic Complex

Repetitive

OLAP

Da to DaReport

!ultiDimensional

OperationProblemOb"ective

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Management Support Systems

Management Information Systems

Decision Support Systems

'&ecutive Information Systems

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Management InformationSystems 0MIS1

MIS primarily provides information onthe firm2s performance to helpmanagers in monitoring andcontrolling the "usiness.

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MIS

Information provided "y MIS is used "ydecision makers at the operational andtactical levels of organi)ation.

They face ith more structured and semi%structured types of decision situations.

$ typical MIS report might sho asummary of monthly sales for each of the

ma+or sales territories of a company.

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MIS MIS typically produces fi&ed! regularly

scheduled reports "ased on data e&tracted andsummari)ed from the organi)ation3s underlyingtransaction processing systems.

Sometimes MIS produces exception reports!highlighting only e&ceptional conditions! suchas hen the sales #uotas for a specificterritory fall "elo an anticipated level oremployees ho have e&ceeded their spending

limit in a dental care plan. Traditional MIS produced primarily hard copyreports.

Today! these reports might "e availa"le onlinethrough an intranet! and more MIS reports can

"e generated on demand.

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Decision Support Systems0DSS1 DSS is a part of special category of information systems

that are designed to enhance managerial decision%making.

Decision support system 0DSS1 is a computer%"asedinformation system that com"ines models and data inan attempt to solve semi%structured and unstructuredpro"lems ith user involvement.

They help managers make more effective decisions "yansering comple& #uestions such as4

Should a neer! more poerful machine replace toolder pieces of e#uipment5

Should your company sell directly to the retail market!continue to sell through distri"utors! or "oth5

Should your company order parts more fre#uently and insmaller lots5

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DSS

DSSs help managers make decisionsthat are uni#ue! rapidly changing andnot easily specified in advance.

$lthough DSS uses internalinformation from T6S and MIS! it alsouses e&ternal sources! such as

current stock prices or product pricesof competitors.

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DSS

DSSs com"ine data and sophisticatedanalytical models to support semi%structured and unstructured decision

making. DSSs help managers "etter use their

knoledge and help create neknoledge.

They are essential components ofknoledge management systems.

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DSS Components

DSS relies on model "ases and data"ases. $ model 0in decision making1 is a simplified

representation of reality. Simplified "ecause reality istoo comple& to copy e&actly and much of the

processes comple&ity is irrelevant to a specificpro"lem.

$ DSS model "ase is a softare component thatcontains all the models used to develop applicationsto run the system.

DSS uses models to manipulate data. '&* If you have some historic sales data! you can use

many different types of models to create a forecast offuture sales.

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DSS Components

DSS softare is a collection of softaretools that are used for data analysis or acollection of mathematical and analytical

models. There can "e 7 different types of

modeling softare for DSSs*

statistical models! optimi)ation models!

forecasting models.

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Statistical modeling

Statistical modeling softare can "eused to help esta"lish relationshipssuch as relating product sales todifferences in age! income or otherfactors "eteen communities.

'&* S6SS.

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(ptimi)ation models

(ptimi)ation models often usinginear 6rogramming 061 determinethe proper mi& of products ithin agiven market to ma&imi)e profit.

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-orecasting models

The user of this type of model mightsupply a range of historical data topro+ect future conditions and salesthat might result from thoseconditions.

Companies often use this softare to

predict the action of competitors.

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Capa"ilities of DSS

,sing a DSS involves 9 "asic types ofanalytical modeling activities*

What%if analysis Sensitivity analysis

:oal%seeking analysis

(ptimi)ation analysis

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What%if analysis

$n end user makes predictions and assumptions regardingthe input data! many of hich are "ased on the assessmentof uncertain futures.

When the model is solved! the results depend on theseassumptions.

What%if analysis attempts to check the impact of a changein the assumptions on the proposed solution.

'&* What ill happen to the total inventory cost if theoriginally assumed cost of carrying inventories is not ;<percent "ut ;= percent5 (r! what ill "e the market shareif

the initially assumed advertising "udget is overspent "y >percent5 In a ell designed DSS! managers themselves can

interactively ask the computer these types of #uestions asmany times as needed.

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Sensitivity $nalysis

Investigation of the effect thatchanges in one or more parts of amodel have on other parts of the

model. ,sually e check the impact that

changes in input varia"les on outputvaria"les.

It is a special case of hat%ifanalysis.

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:oal%seeking analysis

$ttempts to find the value of the inputsnecessary to achieve a desired level ofoutputs.

'&* let us say that a DSS solution yielded aprofit of ? = million. Management ants tokno that hat sales volume and additionaladvertising ould "e necessary to generate

a profit of ?=.@ million. This is a goal%seeking pro"lem.

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(ptimi)ation analysis

(ften uses inear 6rogramming.

Determines optimal resource allocationto ma& or minimi)e specified varia"lesuch as cost! profit! revenue! or risk.

$ classic use of optimi)ation analysis isto determine the proper mi& products

ithin a given market to ma&imi)eprofits.

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The "enefits of DSSs

Improved decision making through "etterunderstanding of the "usinesses

$n increased num"er of decision

alternatives e&amined The a"ility to implement ad hoc analysis -aster response to e&pended situations Improved communication

More effective teamork Aetter control Time and costs savings

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DSS%MIS

DSS MIS

Uses data from PS! MIS! and

external data

Uses data from PS"

Interacti#e $ot interacti#e

%mp&asis on models"

'ssumptions! display grap&ics"

$ot fixed format reports"

Pre-specified! fixed format

reports

Supports semi-structured and

unstructured problems

'ddresses structured and

semi-structured problems

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Data Bisuali)ation

Data visuali)ation refers to presentation ofdata "y technologies such as digital images!geographical information systems! graphical

user interfaces! multidimensional ta"les andgraphs! virtual reality! three%dimensionalpresentations! videos and animation.

Ay presenting data in graphical form helps

users see patterns. Bisuali)ation softare packages offer userscapa"ilities for self%guided e&ploration andvisual analysis of large amounts of data.

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:eographical Information System0:IS1

$ geographical information system 0:IS1 is a computer%"ased system for capturing! storing! checking! integrating!manipulating! and displaying data using digiti)ed maps.'very record or digital o"+ect has an identified geographicallocation. It employs spatially oriented data"ases.

:IS softare uses geographic information tying data topoints! lines and areas on a map.

:IS softare simplifies the analysis and visuali)ation ofinformation a"out entities hose physical location isimportant.

:IS can "e used to support decisions that re#uireknoledge a"out the geographic distri"ution of people orother resources in scientific research and resourcemanagement.

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:IS 0Cont2d1

:IS might "e used to help state andlocal governments calculateemergency response times to natural

disasters or to help "anks identify the"est locations for installing ne"ranches or $TM terminals.

:IS tools have "ecome afforda"leeven for small "usinesses and somecan "e used on the We".

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:IS $pplications

Company (&at t&e application does

PepsiCo! Inc" elps select ne locations for Taco Aell and 6i))a

ut restaurants "y com"ining demographic data

and traffic patterns.

Sears Supports planning of truck routes

Cellular)ne Corp" Maps company2s entire cellular netork to identifyclusters of call disconnects and to dispatchtechnicians accordingly.

oyota! *M Direct drivers to destinations

Sun Microsystems Manages leased property in do)ens of places

orldide

(ilkening + Co"

,consulting

ser#ices

Designs optimal sales territories and routes forclients! reducing travel costs "y ;> percent

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'&ecutive Information Systems0'IS1

$n e&ecutive information system 0'IS1! alsoknon as an e&ecutive support system 0'SS1! isa technology designed in response to the specific

needs of top%level managers and e&ecutives. 'IS help managers ith unstructured pro"lems!

focusing on the information needs of Seniormanagement.

'IS helps senior e&ecutives monitororgani)ational performance! track activities ofcompetitors! spot pro"lems! identifyopportunities! and forecast trends.

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'IS 0cont2d1

'IS* very user friendly

supported "y graphics

provides the capa"ilities of exceptionreporting 0reporting only the results thatdeviate from a set standard1

provides drill down 0investigatinginformation in increasing detail1.

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'IS 0cont2d1

Contemporary 'IS can "ring together datafrom all parts of the organi)ation and allomanagers to select! access them as neededusing easy%to%use desktop analytical toolsand online data displays.

It also helps managers to determine thecritical success factors hich are critical toaccomplishing an organi)ation3s o"+ective.

Today2s systems try to avoid the pro"lem ofdata overload "ecause data can "e filteredand vieed in graphic format.

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'IS 0cont2d1

'IS has the a"ility to drill don!

moving from a piece of summary datato loer and loer levels of detail.

Drill don capa"ility provides details"ehind any given information.

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'IS 0cont2d1

'&ternal data including data from We" areno easily availa"le in many 'ISs.

'&ecutives need a variety of e&ternal datafrom current stock market nes tocompetitor information! industry trends.

Through their 'IS! many managers have

access to nes services! financial marketdata"ases and economic information.

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'IS 0cont2d1

'IS includes tools for modeling and analysis. With only a min. e&perience! most managers

can use these tools to create graphic

comparisons of data "y time! region!product! price! and so on. 'IS provides for ad hoc analysis capa"ilities!

ith hich e&ecutives can make specificre#uests for data analysis. Instead of merely

having access to e&isting reports! thee&ecutives can do creative analysis on theiron.

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A'N'-ITS*

-'EIAIITF

$AIITF T( $N$FG'! C(M6$H'!I:I:T TH'NDS

:H$6ICS '6 'E6(H' SIT,$TI(N

M(NIT(H 6'H-(HM$NC' TIM'IN'SS! $B$I$AIITF (- D$T$

$(WS 6H(M6T $CTI(N

'IS 0cont2d1

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Data mining for Decision Support

Data mining is a tool for analy)inglarge amounts of data.

It derives its name from the

similarities "eteen searching forvalua"le "usiness information in alarge data"ase! and mining a

mountain for a vein of valua"le ore.

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Data mining

Data mining helps organi)e information "yanaly)ing huge #uantities of data andlooking for patterns! trends! associations!

e&ceptions! and changes in data that aretoo complicated for normal humandetection.

Data mining uses a variety of tools! such as

artificial intelligence and statistical andvisuali)ation tools to analy)e the data in adata"ase.

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$rtificial Intelligence 0$I1% 0hen1Will computers "e smarter than you5

Several capa"ilities are considered to"e the signs of intelligence* learningand understanding from e&perience!responding #uickly and successfully toa ne situation! dealing ith comple&

situations! etc.

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$rtificial Intelligence

$rtificial intelligence 0$I1 is concernedith studying the thought processes ofhumans and representing those

processes via machines 0computers!ro"ots! and so on1.

It2s ultimate goal is to "uild machinesthat ill mimic human intelligence.

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$I

Why "usinesses are interested in $I4 To create a mechanism that is not su"+ect

to human feelings! such as fatigue andorry.

To eliminate routine and unsatisfying +o"sheld "y people.

To enhance the organi)ation3s knoledge"ase "y generating solutions to specific

pro"lems that are too massive and comple&to "e analy)ed "y human "eing in a shortperiod of time.

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Expert System

Robotics

Cognitive/Learning

Science

Neural Networks

Natural

Language

Processing Visual & u!itory

Processing

AI

Ma.or areas of 'I

'rtificial neural networks ,'$$s simulate massive parallel processesthat involve processing elements

interconnected in a netork.

/u00y logic deals ith uncertainties"y simulating the process of humanreasoning! alloing the computer to"ehave less precisely and logicallythan conventional computers do.

%xpert System" It is an attempt tomimic human e&perts. It is decision%making softare that can reach a levelof performance compara"le to a humane&pert in some speciali)ed and usuallynarro pro"lem area.

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Ma+or areas of $I

1" Cogniti#e science* It is "ased onresearch in "iology! neurology!mathematic and many disciplines. It

focuses on researching ho thehuman "rain orks and ho humanthink and learn.

Its ma+or applications are intelligentagents! neural netorks! and fu))ylogic.

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Intelligent agents

Intelligent agents represent a netechnology ith the potential to "ecomeone of the most important tools ofinformation technology.

It is a softare surrogate for end user orprocess that fulfills a stated need oractivity.

(i0ards is one of its e&amples. Wi)ard is a

"uilt%in package capa"ility that atchesusers and offers suggestions as theyattempt to perform tasks "y themselves.

'&* '&cel2s or Word2s i)ards.

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$rtificial Neural Netorks 0$NN1

It is an architecture that mimics certain dataprocessing capa"ilities of human "rain.

It is a computer model hich can handle fast retrievalof large amounts of information and has the a"ility torecogni)e patterns "ased on e&periences.

It consists of interconnected processing elements!called neurons.

It emulates a "iological neural netork. The neurons in an $NN receive information from other

neurons or from e&ternal sources! transform the

information! and pass it on to other neurons or ase&ternal outputs.

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$NN

The value of neural netork technologyincludes its usefulness for patternrecognition! learning! and the interpretationof incomplete inputs.

$NNs have the potential to provide some ofthe human characteristics of pro"lemsolving that are difficult to simulate usingthe logical! analytical techni#ues of DSSs.

$NN can analy)e large #uantities of data todiscover patterns and characteristics insituations here the logic or rules are notknon.

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$NN

'&* oan applications ould "e a good e&ample. Ay revieing many historical cases of applicants2

responses to #uestionnaires and the grantingdecisions 0yes or no1! the $NN can create patternsJ

or profilesJ of applications that should "e approved!or those that should "e denied. et us say! a ne application is matched against the

pattern. If it comes close enough! the computerclassifies it as a yesJ or noJ.

(therise it goes to a human to decide. $pplications can thus "e processed more rapidly.

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$NN

Specific "usiness areas that are ell%suited to the use of$NNs are4

Ta& fraud* identifying! enhancing! and finding irregularities. $irline management* seat demand forecasting and cre

scheduling.

6rediction of consumer "ehavior on the Internet* predictingconsumer "ehavior in order to plan e%commerceadvertising.

Stocks! "onds! and commodity selection and trading*analy)ing various investment alternatives! including pricingof initial pu"lic offerings.

Signature validation* matching against previous signatures. 'valuation of personnel and +o" candidates* matchingpersonnel data to +o" re#uirements and performancecriteria.

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-u))y ogic

It deals ith uncertainties "y simulating theprocess of human reasoning.

These systems allo computers to "ehave

less precisely and logically than conventionalcomputers do. The idea "ehind this approach is that

decision making is not alays a matter oftrue or false! "lack and hite.

It often involves gray areas here the termsapproximately ! possible! and similar aremore appropriate.

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-u))y ogic

'&* The varia"le heightJ. Most people ould agree that if you are a"ove K feet! you are

tall. Similarly! if your height is less than > feet! you are short. Aut "eteen K feet and >.@> feet! there is less pro"a"ility that

you ill "e considered tall. Similarly! "eteen > and >.=> feet some ill consider you short. Notice that in the area "eteen >.=> and >.@> feet you have a

chance for "eing considered either short or tall. -u))y logic systems can process such data that are am"iguous!

that is fu))y data! instead of relying only on crisp data! such as"inary 0yesLno1 choices.

It #uickly provides appro&imate! "ut accepta"le solutions topro"lems.

It allos for appro&imate values and inferences. Currently there are only a fe e&amples of fu))y logic

applications in "usiness. The apanese trade shares on the Tokyo Stock '&change using a

stock%trading program "ased on fu))y logic rules.

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Ma+or $reas of $I 0cont2d1

=.Ho"otics* Ho"ot machines areelectromechanical devices that can "eprogrammed and reprogrammed to

automate manual tasks. In computer aided manufacturing

ro"otics are used.

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Ma+or $reas of $I 0cont2d1

7.Natural Interfaces* Birtual reality is an e&ample of naturalinterfaces.

Birtual reality is a computer%simulated reality. uman users can e&perience computer%simulated o"+ects!

spaces! activities as if they actually e&ist. It is interactive! uses computer%generated! three%

dimensional graphics. '&* C$D. '&* N'C Corporation 0apan1 developed a ski simulator!

hich is availa"le in amusement centers. It is also usedfor training.

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'&pert Systems

'&pert systems are an attempt to mimichuman e&perts.

It is a decision%making softare that canreach a level of performance compara"le toa human e&pert in some speciali)ed andusually narro pro"lem area.

The idea is simple* e&pertise is transferredfrom an e&pert or other source of e&pertiseto the computer.

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'&pert systems

It captures the e&pertise of an e&pert or groupof e&perts in a computer%"ased informationsystem.

The necessary e&pertise is stored electronically

in a knoledge "ase. The computer is programmed so that it can

make inferences. During the past fe years! the technology of

e&pert systems have "een successfully applied

in thousands of organi)ations orldide topro"lems ranging from identifying credit cardfraud to medical diagnosis to the analysis ofdust in mines.

chapter 8

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