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Chapter 13Decision Support SystemsDecision Support Systems
MANAGEMENT INFORMATION SYSTEMS 8/ERaymond McLeod, Jr. and George Schell
Copyright 2001 Prentice-Hall, Inc.
13-1
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
13-2
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
13-3
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.
13-4
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
13-6
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.”
13-7
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
13-9
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
13-10
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
13-11
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
13-12
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
13-13
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
13-14
How GDSS Contributes How GDSS Contributes to Problem Solvingto Problem Solving
Improved communicationsImproved communications Improved discussion focusImproved discussion focus Less wasted timeLess wasted time
13-15
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.
13-16
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
13-17
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
13-18
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
13-19
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.
13-21
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
13-22
Areas of Artificial IntelligenceAreas of Artificial Intelligence
ExpertExpertsystemssystems AIAI
hardwarehardware
RoboticsRobotics
PerceptivePerceptive systemssystems (vision,(vision, hearing)hearing)
NeuralNeuralnetworksnetworks
NaturalNatural languagelanguage
Learning
Artificial IntelligenceArtificial Intelligence
13-23
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
13-24
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
13-26
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
13-27
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.
13-28
Evidence
Conclusion
Conclusion
Evidence Evidence Evidence Evidence
Evidence Evidence Evidence
Conclusion
A Rule Set That Produces One Final
Conclusion
13-29
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
13-30
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)
13-31
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
13-32
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
13-33
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
13-34
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
13-35
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
13-36
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
13-37
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
13-38
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
13-39
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
13-40
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
13-41
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
13-42
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
13-43
Soma(processor)
Axon
Synapse
Dendrites (input)
Axonal Paths (output)
Simple Biological NeuronsSimple Biological Neurons
13-44
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)
13-45
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
13-46
y1
y2
y3
yn-1
y
w1
w2
w3
wn-1
Single Artificial NeuronSingle Artificial Neuron
13-47
The Multi-The Multi-Layer Layer
PerceptronPerceptron
Yn2
ININnn
OUTOUTnnOUTOUT11
ININ11
YY11
Input Input LayerLayer
OutputLOutputLayerayer
13-48
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
13-49
Summary [cont.]Summary [cont.]
AIAI– Neural networksNeural networks– Expert systemsExpert systems
Limitations and promiseLimitations and promise
13-50