Chapter 13Decision Support SystemsDecision Support Systems

MANAGEMENT INFORMATION SYSTEMS 8/ERaymond McLeod, Jr. and George Schell

Copyright 2001 Prentice-Hall, Inc.

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Simon’s Types of Simon’s Types of DecisionsDecisions

Programmed decisionsProgrammed decisions– repetitive and routinerepetitive and routine– have a definite procedurehave a definite procedure

Nonprogrammed decisionsNonprogrammed decisions– Novel and unstructuredNovel and unstructured– No cut-and-dried method for handling problemNo cut-and-dried method for handling problem

Types exist on a continuumTypes exist on a continuum

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Simon’s Problem Solving Simon’s Problem Solving PhasesPhases

IntelligenceIntelligence– Searching environment for conditions calling for a Searching environment for conditions calling for a

solutionsolution

DesignDesign– Inventing, developing, and analyzing possible courses of Inventing, developing, and analyzing possible courses of

actionaction

ChoiceChoice– Selecting a course of action from those available Selecting a course of action from those available

ReviewReview– Assessing past choicesAssessing past choices

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Definitions of a Decision Definitions of a Decision Support System (DSS)Support System (DSS)

General definition - General definition - a system providing both a system providing both problem-solving and communications capabilities problem-solving and communications capabilities for semistructured problemsfor semistructured problems

Specific definition - Specific definition - a system that supports a a system that supports a single manager or a relatively small group of single manager or a relatively small group of managers working as a problem-solving team in managers working as a problem-solving team in the solution of a semistructured problem by the solution of a semistructured problem by providing information or making suggestions providing information or making suggestions concerning concerning specific specific decisions.decisions.

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The DSS ConceptThe DSS Concept Gorry and Scott Morton coined the phrase ‘DSS’ in Gorry and Scott Morton coined the phrase ‘DSS’ in

1971, about ten years after MIS became popular1971, about ten years after MIS became popular Decision types in terms of problem structureDecision types in terms of problem structure

– Structured problems can be solved with algorithms and Structured problems can be solved with algorithms and decision rulesdecision rules

– Unstructured problems have no structure in Simon’s Unstructured problems have no structure in Simon’s phasesphases

– Semistructured problems have structured and Semistructured problems have structured and unstructured phasesunstructured phases

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Degree ofDegree ofproblemproblemstructurestructure

The Gorry and Scott Morton GridThe Gorry and Scott Morton GridManagement levelsManagement levels

StructuredStructured

SemistructuredSemistructured

UnstructuredUnstructured

OperationalOperational controlcontrol

ManagementManagement controlcontrol

StrategicStrategicplanningplanning

Accountsreceivable

Order entry

Inventory control

Budget analysis--engineered costs

Short-term forecasting

Tanker fleet mix

Warehouse andfactory location

Productionscheduling

Cashmanagement

PERT/COST systems

Variance analysis-- overall budget

Budget preparation

Sales and production

Mergers and acquisitions

New product planning

R&D planning

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Alter’s DSS TypesAlter’s DSS Types

In 1976 Steven Alter, a doctoral student In 1976 Steven Alter, a doctoral student built on Gorry and Scott-Morton framework built on Gorry and Scott-Morton framework – Created a taxonomy of six DSS typesCreated a taxonomy of six DSS types– Based on a study of 56 DSSsBased on a study of 56 DSSs

Classifies DSSs based on “degree of Classifies DSSs based on “degree of problem solving support.”problem solving support.”

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Levels of Alter’s DSSsLevels of Alter’s DSSs

Level of problem-solving support from Level of problem-solving support from lowest to highest lowest to highest – Retrieval of information elementsRetrieval of information elements– Retrieval of information filesRetrieval of information files– Creation of reports from multiple filesCreation of reports from multiple files– Estimation of decision consequencesEstimation of decision consequences– Propose decisionsPropose decisions– Make decisionsMake decisions

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Importance of Alter’s Importance of Alter’s StudyStudy

Supports concept of developing systems Supports concept of developing systems that address particular decisionsthat address particular decisions

Makes clear that DSSs need not be Makes clear that DSSs need not be restricted to a particular application typerestricted to a particular application type

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Retrieve Retrieve information information

elementselements

Analyze Analyze entire entire filesfiles

Prepare Prepare reports reports

from from multiple multiple

filesfiles

Estimate Estimate decision decision

consequen-consequen-cesces

Propose Propose decisionsdecisions

Degree Degree of of problem problem solving solving supportsupport

Degree of Degree of complexity of the complexity of the problem-solving problem-solving

systemsystem

LittleLittle MuchMuch

Alter’s DSS TypesAlter’s DSS Types

Make Make decisionsdecisions

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Three DSS ObjectivesThree DSS Objectives

1.1. Assist in solving semistructured problems Assist in solving semistructured problems

2.2. Support, not replace, the manager Support, not replace, the manager

3.3. Contribute to decision effectiveness, rather Contribute to decision effectiveness, rather than efficiencythan efficiency

Based on studies of Keen and Scott-Morton

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GDSS software

MathematicalMathematicalModelsModels

OtherOther group group membersmembers

DatabaseDatabase

GDSSGDSSsoftwaresoftware

EnvironmentEnvironment

IndividualIndividual problemproblem solverssolvers

Decision support system

EnvironmentEnvironment Legend:

Data Information Communication

A DSS ModelA DSS Model

ReportReportwritingwriting

softwaresoftware

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Database ContentsDatabase Contents Used by Three Software SubsystemsUsed by Three Software Subsystems

– Report writers Report writers » Special reportsSpecial reports» Periodic reportsPeriodic reports» COBOL or PL/ICOBOL or PL/I» DBMSDBMS

– Mathematical modelsMathematical models» Simulations Simulations » Special modeling languagesSpecial modeling languages

– Groupware or GDSSGroupware or GDSS

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Group Decision Support Group Decision Support SystemsSystems

Computer-based system that supports groups of Computer-based system that supports groups of people engaged in a common task (or goal) and people engaged in a common task (or goal) and that provides an interface to a shared that provides an interface to a shared environment.environment.

Used in problem solvingUsed in problem solving Related areasRelated areas

– Electronic meeting system (EMS) Electronic meeting system (EMS) – Computer-supported cooperative work (CSCW)Computer-supported cooperative work (CSCW)– Group support system (GSS)Group support system (GSS)– GroupwareGroupware

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How GDSS Contributes How GDSS Contributes to Problem Solvingto Problem Solving

Improved communicationsImproved communications Improved discussion focusImproved discussion focus Less wasted timeLess wasted time

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GDSS Environmental GDSS Environmental SettingsSettings

Synchronous exchange Synchronous exchange – Members meet at same timeMembers meet at same time– Committee meeting is an exampleCommittee meeting is an example

Asynchronous exchangeAsynchronous exchange– Members meet at different timesMembers meet at different times– E-mail is an exampleE-mail is an example

More balanced participation.More balanced participation.

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GDSS TypesGDSS Types Decision roomsDecision rooms

– Small groups face-to-faceSmall groups face-to-face– Parallel communicationParallel communication– AnonymityAnonymity

Local area decision networkLocal area decision network– Members interact using a LANMembers interact using a LAN

Legislative sessionLegislative session– Large group interactionLarge group interaction

Computer-mediated conferenceComputer-mediated conference– Permits large, geographically dispersed group interactionPermits large, geographically dispersed group interaction

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Smaller Larger

GROUPGROUP SIZESIZE

Face-to-face

Dispersed

DecisionRoom

Local Area Decision Network

Legislative Session

Computer-Mediated

Conference

MEMBERMEMBERPROXIMITYPROXIMITY

Group Size and Location Determine Group Size and Location Determine GDSS Environmental SettingsGDSS Environmental Settings

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GroupwareGroupware

FunctionsFunctions– E-mailE-mail– FAXFAX– Voice messagingVoice messaging– Internet accessInternet access

Lotus Notes Lotus Notes – Popular groupware productPopular groupware product– Handles data important to managersHandles data important to managers

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Electronic mail X X X XFAX X X O XVoice messaging O XInternet access X X O XBulletin board system X 3 OPersonal calendaring X X 3 XGroup calendaring X X O XElectronic conferencing O X 3 3Task management X X 3 XDesktop video conferencingODatabase access O X 3Workflow routing O X 3 XReengineering O X 3Electronic forms O 3 3 OGroup documents O X X O

Main Groupware FunctionsMain Groupware Functions IBM TeamWARE Lotus Novell IBM TeamWARE Lotus Novell Function Workgroup Office Notes GroupWiseFunction Workgroup Office Notes GroupWise

X = standard featureX = standard feature O = optional featureO = optional feature 3 = third party offering3 = third party offering13-20

Artificial Intelligence (AI)Artificial Intelligence (AI)

The activity of providing such machines as computers with the ability to display behavior that would be regarded as intelligent if it were observed in humans.

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History of AIHistory of AI

Early historyEarly history– John McCarthy coined term, AI, in 1956, at John McCarthy coined term, AI, in 1956, at

Dartmouth College conference.Dartmouth College conference.

– Logic Theorist (first AI program. Herbert Simon Logic Theorist (first AI program. Herbert Simon played a part)played a part)

– General problem solver (GPS)General problem solver (GPS)

Past 2 decadesPast 2 decades– Research has taken a back seat to MIS and DSS Research has taken a back seat to MIS and DSS

developmentdevelopment

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Areas of Artificial IntelligenceAreas of Artificial Intelligence

ExpertExpertsystemssystems AIAI

hardwarehardware

RoboticsRobotics

PerceptivePerceptive systemssystems (vision,(vision, hearing)hearing)

NeuralNeuralnetworksnetworks

NaturalNatural languagelanguage

Learning

Artificial IntelligenceArtificial Intelligence

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Appeal of Expert SystemsAppeal of Expert Systems Computer program that codes the Computer program that codes the

knowledge of human experts in the form of knowledge of human experts in the form of heuristicsheuristics

Two distinctions from DSSTwo distinctions from DSS– 1. Has potential to extend manager’s problem-1. Has potential to extend manager’s problem-

solving abilitysolving ability– 2. Ability to explain how solution was reached2. Ability to explain how solution was reached

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Know-ledgebase

User

Userinterface

Instructions &information

Solutions &explanations Knowledge

Inference engine

Problem Domain

Expert and knowledge engineer

Developmentengine ExpertExpert

systemsystemAn Expert An Expert

System ModelSystem Model13-25

Expert System ModelExpert System Model User interfaceUser interface

– Allows user to interact with systemAllows user to interact with system Knowledge baseKnowledge base

– Houses accumulated knowledgeHouses accumulated knowledge Inference engineInference engine

– Provides reasoningProvides reasoning– Interprets knowledge baseInterprets knowledge base

Development engineDevelopment engine– Creates expert systemCreates expert system

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User InterfaceUser Interface

User enters:User enters:– InstructionsInstructions– InformationInformation

Expert system provides:Expert system provides:– SolutionsSolutions– Explanations ofExplanations of

» QuestionsQuestions

» Problem solutionsProblem solutions

}Menus, commands, natural language, GUI

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Knowledge BaseKnowledge Base

Description of problem domainDescription of problem domain RulesRules

– Knowledge representation techniqueKnowledge representation technique– ‘‘IF:THEN’ logicIF:THEN’ logic– Networks of rulesNetworks of rules

» Lowest levels provide evidenceLowest levels provide evidence

» Top levels produce 1 or more conclusionsTop levels produce 1 or more conclusions

» Conclusion is called a Goal variable.Conclusion is called a Goal variable.

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Evidence

Conclusion

Conclusion

Evidence Evidence Evidence Evidence

Evidence Evidence Evidence

Conclusion

A Rule Set That Produces One Final

Conclusion

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Rule SelectionRule Selection

Selecting rules to efficiently solve a Selecting rules to efficiently solve a problem is difficultproblem is difficult

Some goals can be reached with only a few Some goals can be reached with only a few rules; rules 3 and 4 identify bird rules; rules 3 and 4 identify bird

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Inference EngineInference Engine

Performs reasoning by using the contents of Performs reasoning by using the contents of knowledge base in a particular sequenceknowledge base in a particular sequence

Two basic approaches to using rulesTwo basic approaches to using rules– 1. Forward reasoning (data driven)1. Forward reasoning (data driven)– 2. Reverse reasoning (goal driven)2. Reverse reasoning (goal driven)

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Forward ReasoningForward Reasoning(Forward Chaining)(Forward Chaining)

Rule is evaluated as: Rule is evaluated as: – (1) true, (2) false, (3) unknown(1) true, (2) false, (3) unknown

Rule evaluation is an iterative processRule evaluation is an iterative process When no more rules can fire, the reasoning When no more rules can fire, the reasoning

process stops even if a goal has not been process stops even if a goal has not been reachedreached

Start with inputs and work to solution

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Rule 1Rule 1

Rule 3Rule 3

Rule 2Rule 2

Rule 4Rule 4

Rule 5Rule 5

Rule 6Rule 6

Rule 7Rule 7

Rule 8Rule 8

Rule 9Rule 9

Rule 10Rule 10

Rule 11Rule 11

Rule 12Rule 12

IF ATHEN B

IF CTHEN D

IF MTHEN E

IF KTHEN F

IF GTHEN H

IF ITHEN J

IF B OR DTHEN K

IF ETHEN L

IF K AND L THEN N

IF M THEN O

IF N OR OTHEN P

F

IF (F AND H)OR JTHEN M

IF (F AND H)OR JTHEN M

The The ForwardForward

ReasoningReasoningProcessProcess

T

TT

T

T

T

T

T

T

F

T

Legend:Legend: First pass

Second pass

Third pass

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Reverse Reasoning StepsReverse Reasoning Steps(Backward Chaining)(Backward Chaining)

Divide problem into subproblemsDivide problem into subproblems Try to solve one subproblemTry to solve one subproblem Then try anotherThen try another

Start with solution and work back to inputs

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T

Rule 1

Rule 2

Rule 3

Rule 9

Rule 11 Legend:Problems to be solved

Step 4

Step 3

Step 2

Step 1

Step 5

IF A THEN B

IF B OR DTHEN K

IF K AND LTHEN N

IF N OR O THEN P

IF CTHEN D

IF MTHEN E

IF ETHEN L

IF (F AND H)OR JTHEN M

IF MTHEN O

IF MTHEN O

T

The First Five The First Five Problems Problems

Are IdentifiedAre IdentifiedRule 7

Rule 10

Rule 12

Rule 8

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If KThen F

Legend:Problems to be solved

If GThen H

If IThen J

If MThen O

Step 8

Step 9Step 7 Step 6

Rule 4

Rule 5

Rule 11Rule 6

T

IF (F And H)Or J

Then MT

Rule 9

T T

Rule 12

T

If N Or OThen P

The Next Four Problems AreThe Next Four Problems AreIdentifiedIdentified

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Forward Versus Reverse Forward Versus Reverse ReasoningReasoning

Reverse reasoning is faster than forward Reverse reasoning is faster than forward reasoningreasoning

Reverse reasoning works best under certain Reverse reasoning works best under certain conditionsconditions– Multiple goal variablesMultiple goal variables– Many rulesMany rules– All or most rules do not have to be examined in All or most rules do not have to be examined in

the process of reaching a solutionthe process of reaching a solution

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Development EngineDevelopment Engine Programming languages Programming languages

– LispLisp– PrologProlog

Expert system shellsExpert system shells– Ready made processor that can be tailored to a Ready made processor that can be tailored to a

particular problem domainparticular problem domain Case-based reasoning (CBR)Case-based reasoning (CBR) Decision treeDecision tree

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Expert System Expert System AdvantagesAdvantages

For managersFor managers– Consider more alternativesConsider more alternatives– Apply high level of logicApply high level of logic– Have more time to evaluate decision rulesHave more time to evaluate decision rules– Consistent logicConsistent logic

For the firmFor the firm– Better performance from management teamBetter performance from management team– Retain firm’s knowledge resourceRetain firm’s knowledge resource

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Expert System Expert System DisadvantagesDisadvantages

Can’t handle inconsistent knowledgeCan’t handle inconsistent knowledge Can’t apply judgment or intuitionCan’t apply judgment or intuition

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Keys to Successful ES Keys to Successful ES DevelopmentDevelopment

Coordinate ES development with strategic planningCoordinate ES development with strategic planning Clearly define problem to be solved and understand Clearly define problem to be solved and understand

problem domainproblem domain Pay particular attention to ethical and legal Pay particular attention to ethical and legal

feasibility of proposed systemfeasibility of proposed system Understand users’ concerns and expectations Understand users’ concerns and expectations

concerning systemconcerning system Employ management techniques designed to retain Employ management techniques designed to retain

developersdevelopers

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Neural NetworksNeural Networks

Mathematical model of the human brainMathematical model of the human brain– Simulates the way neurons interact to process Simulates the way neurons interact to process

data and learn from experiencedata and learn from experience Bottom-up approach to modeling human Bottom-up approach to modeling human

intuitionintuition

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The Human BrainThe Human Brain

Neuron -- the information processorNeuron -- the information processor– Input -- dendritesInput -- dendrites– Processing -- somaProcessing -- soma– Output -- axonOutput -- axon

Neurons are connected by the synapseNeurons are connected by the synapse

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Soma(processor)

Axon

Synapse

Dendrites (input)

Axonal Paths (output)

Simple Biological NeuronsSimple Biological Neurons

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Evolution of Artificial Evolution of Artificial Neural Systems (ANS)Neural Systems (ANS)

McCulloch-Pitts mathematical neuron McCulloch-Pitts mathematical neuron function (late 1930s) was the starting pointfunction (late 1930s) was the starting point

Hebb’s learning law (early 1940s)Hebb’s learning law (early 1940s) NeurocomputersNeurocomputers

– Marvin Minsky’s Snark (early 1950s)Marvin Minsky’s Snark (early 1950s)– Rosenblatt’s Perceptron (mid 1950s)Rosenblatt’s Perceptron (mid 1950s)

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Current MethodologyCurrent Methodology

Mathematical models don’t duplicate Mathematical models don’t duplicate human brains, but exhibit similar abilitieshuman brains, but exhibit similar abilities

Complex networksComplex networks Repetitious trainingRepetitious training

– ANS “learns” by exampleANS “learns” by example

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y1

y2

y3

yn-1

y

w1

w2

w3

wn-1

Single Artificial NeuronSingle Artificial Neuron

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The Multi-The Multi-Layer Layer

PerceptronPerceptron

Yn2

ININnn

OUTOUTnnOUTOUT11

ININ11

YY11

Input Input LayerLayer

OutputLOutputLayerayer

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Knowledge-based Systems Knowledge-based Systems

in Perspectivein Perspective Much has been accomplished in neural nets Much has been accomplished in neural nets

and expert systemsand expert systems Much work remainsMuch work remains Systems abilities to mimic human Systems abilities to mimic human

intelligence are too limited and regarded as intelligence are too limited and regarded as primitiveprimitive

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Summary [cont.]Summary [cont.]

AIAI– Neural networksNeural networks– Expert systemsExpert systems

Limitations and promiseLimitations and promise

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