Chapter 13Decision Support SystemsMANAGEMENT INFORMATION SYSTEMS 8/ERaymond McLeod, Jr. and George SchellCopyright 2001 Prentice-Hall, Inc.13-*

Simons Types of DecisionsProgrammed decisionsrepetitive and routinehave a definite procedureNonprogrammed decisionsNovel and unstructuredNo cut-and-dried method for handling problemTypes exist on a continuum13-*

Simons Problem Solving PhasesIntelligenceSearching environment for conditions calling for a solutionDesignInventing, developing, and analyzing possible courses of actionChoiceSelecting a course of action from those available ReviewAssessing past choices13-*

Definitions of a Decision Support System (DSS)General definition - a system providing both problem-solving and communications capabilities for semistructured problemsSpecific definition - a system that supports a single manager or a relatively small group of managers working as a problem-solving team in the solution of a semistructured problem by providing information or making suggestions concerning specific decisions.13-*

The DSS ConceptGorry and Scott Morton coined the phrase DSS in 1971, about ten years after MIS became popularDecision types in terms of problem structureStructured problems can be solved with algorithms and decision rulesUnstructured problems have no structure in Simons phasesSemistructured problems have structured and unstructured phases13-*

Degree ofproblemstructureThe Gorry and Scott Morton GridManagement levels StructuredSemistructured UnstructuredOperational controlManagement controlStrategicplanningAccountsreceivable

Order entry

Inventory controlBudget analysis--engineered costs

Short-term forecasting Tanker fleet mix

Warehouse andfactory locationProductionscheduling

Cashmanagement

PERT/COST systemsVariance analysis-- overall budget

Budget preparation

Sales and productionMergers and acquisitions

New product planning

R&D planning13-*

Alters DSS TypesIn 1976 Steven Alter, a doctoral student built on Gorry and Scott-Morton framework Created a taxonomy of six DSS typesBased on a study of 56 DSSsClassifies DSSs based on degree of problem solving support.13-*

Levels of Alters DSSsLevel of problem-solving support from lowest to highest Retrieval of information elementsRetrieval of information filesCreation of reports from multiple filesEstimation of decision consequencesPropose decisionsMake decisions13-*

Importance of Alters StudySupports concept of developing systems that address particular decisionsMakes clear that DSSs need not be restricted to a particular application type13-*

Retrieve information elementsAnalyze entire filesPrepare reports from multiple filesEstimate decision consequen-cesPropose decisionsDegree of problem solving supportDegree of complexity of the problem-solving systemLittleMuchAlters DSS TypesMake decisions13-*

Three DSS Objectives1. Assist in solving semistructured problems2. Support, not replace, the manager3. Contribute to decision effectiveness, rather than efficiencyBased on studies of Keen and Scott-Morton13-*

GDSS softwareMathematicalModels

Other group members Database

GDSSsoftware Environment Individual problem solvers Decision support system Environment Legend:DataInformation

CommunicationA DSS ModelReportwritingsoftware13-*

Database ContentsUsed by Three Software SubsystemsReport writers Special reportsPeriodic reportsCOBOL or PL/IDBMSMathematical modelsSimulations Special modeling languagesGroupware or GDSS13-*

Group Decision Support SystemsComputer-based system that supports groups of people engaged in a common task (or goal) and that provides an interface to a shared environment.Used in problem solvingRelated areasElectronic meeting system (EMS) Computer-supported cooperative work (CSCW)Group support system (GSS)Groupware

13-*

How GDSS Contributes to Problem SolvingImproved communicationsImproved discussion focusLess wasted time

13-*

GDSS Environmental SettingsSynchronous exchange Members meet at same timeCommittee meeting is an exampleAsynchronous exchangeMembers meet at different timesE-mail is an exampleMore balanced participation.13-*

GDSS TypesDecision roomsSmall groups face-to-faceParallel communicationAnonymityLocal area decision networkMembers interact using a LANLegislative sessionLarge group interactionComputer-mediated conferencePermits large, geographically dispersed group interaction13-*

SmallerLargerGROUP SIZEFace-to-faceDispersedDecisionRoomLocal Area Decision NetworkLegislative SessionComputer-MediatedConferenceMEMBERPROXIMITYGroup Size and Location Determine GDSS Environmental Settings13-*

GroupwareFunctionsE-mailFAXVoice messagingInternet accessLotus Notes Popular groupware productHandles data important to managers13-*

Main Groupware Functions

IBM TeamWARE Lotus Novell Function Workgroup Office Notes GroupWiseX = standard featureO = optional feature3 = third party offering13-*

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-*

History of AIEarly historyJohn McCarthy coined term, AI, in 1956, at Dartmouth College conference.Logic Theorist (first AI program. Herbert Simon played a part)General problem solver (GPS)Past 2 decadesResearch has taken a back seat to MIS and DSS development13-*

Areas of Artificial IntelligenceExpertsystemsAIhardware RoboticsPerceptive systems (vision, hearing) Neuralnetworks Natural languageLearning Artificial Intelligence13-*

Appeal of Expert SystemsComputer program that codes the knowledge of human experts in the form of heuristicsTwo distinctions from DSS1. Has potential to extend managers problem-solving ability2. Ability to explain how solution was reached13-*

Know-ledgebase User

UserinterfaceInstructions &informationSolutions &explanationsKnowledgeInference engine Problem Domain

Expert and knowledge engineer Developmentengine ExpertsystemAn Expert System Model13-*

Expert System ModelUser interfaceAllows user to interact with systemKnowledge baseHouses accumulated knowledgeInference engineProvides reasoningInterprets knowledge baseDevelopment engineCreates expert system13-*

User InterfaceUser enters:InstructionsInformationExpert system provides:SolutionsExplanations ofQuestionsProblem solutions}Menus, commands, natural language, GUI13-*

Knowledge BaseDescription of problem domainRulesKnowledge representation techniqueIF:THEN logicNetworks of rulesLowest levels provide evidenceTop levels produce 1 or more conclusionsConclusion is called a Goal variable.13-*

EvidenceConclusionConclusionEvidenceEvidenceEvidenceEvidenceEvidenceEvidenceEvidenceConclusionA Rule Set That Produces One FinalConclusion13-*

Rule Selection Selecting rules to efficiently solve a problem is difficultSome goals can be reached with only a few rules; rules 3 and 4 identify bird 13-*

Inference EnginePerforms reasoning by using the contents of knowledge base in a particular sequenceTwo basic approaches to using rules1. Forward reasoning (data driven)2. Reverse reasoning (goal driven)13-*

Forward Reasoning(Forward Chaining)Rule is evaluated as: (1) true, (2) false, (3) unknownRule evaluation is an iterative processWhen no more rules can fire, the reasoning process stops even if a goal has not been reachedStart with inputs and work to solution13-*

Rule 1 Rule 3 Rule 2 Rule 4 Rule 5 Rule 6 Rule 7 Rule 8 Rule 9 Rule 10 Rule 11Rule 12 IF ATHEN B IF CTHEN D IF MTHEN EIF KTHEN F IF GTHEN HIF ITHEN J IF B OR DTHEN K IF ETHEN L IF K AND L THEN N IF M THEN OIF N OR OTHEN PFIF (F AND H)OR JTHEN MThe ForwardReasoningProcessTTTTTTTTTFT Legend: First passSecond pass Third pass13-*

Reverse Reasoning Steps(Backward Chaining) Divide problem into subproblemsTry to solve one subproblemThen try anotherStart with solution and work back to inputs13-*

TRule 1Rule 2Rule 3Rule 9Rule 11Legend:Problems to be solvedStep 4Step 3Step 2Step 1Step 5IF A THEN BIF B OR DTHEN KIF K AND LTHEN NIF N OR O THEN PIF CTHEN DIF MTHEN EIF ETHEN LIF (F AND H)OR JTHEN MIF MTHEN OIF MTHEN OThe First Five Problems Are IdentifiedRule 7Rule 10Rule 12Rule 813-*

If KThen FLegend:Problems to be solvedIf GThen HIf IThen JIf MThen OStep 8Step 9Step 7Step 6Rule 4Rule 5Rule 11Rule 6TIF (F And H)Or J Then MTRule 9TTRule 12TIf N Or OThen PThe Next Four Problems Are Identified13-*

Forward Vs Reverse ReasoningReverse reasoning is faster than forward reasoningReverse reasoning works best under certain conditionsMultiple goal variablesMany rulesAll or most rules do not have to be examined in the process of reaching a solution13-*

Development EngineProgramming languages LispPrologExpert system shellsReady made processor that can be tailored to a particular problem domainCase-based reasoning (CBR)Decision tree13-*

Expert System AdvantagesFor managersConsider more alternativesApply high level of logicHave more time to evaluate decision rulesConsistent logicFor the firmBetter performance from management teamRetain firms knowledge resource13-*

Expert System DisadvantagesCant handle inconsistent knowledgeCant apply judgment or intuition13-*

Keys to Successful ES DevelopmentCoordinate ES development with strategic planningClearly define problem to be solved and understand problem domainPay particular attention to ethical and legal feasibility of proposed systemUnderstand users concerns and expectations concerning systemEmploy management techniques designed to retain developers13-*

Neural NetworksMathematical model of the human brainSimulates the way neurons interact to process data and learn from experienceBottom-up approach to modeling human intuition13-*

The Human BrainNeuron -- the information processorInput -- dendritesProcessing -- somaOutput -- axonNeurons are connected by the synapse13-*

Soma(processor)AxonSynapseDendrites (input)Axonal Paths (output)Simple Biological Neurons13-*

Evolution of Artificial Neural Systems (ANS)McCulloch-Pitts mathematical neuron function (late 1930s) was the starting pointHebbs learning law (early 1940s)NeurocomputersMarvin Minskys Snark (early 1950s)Rosenblatts Perceptron (mid 1950s)13-*

Current MethodologyMathematical models dont duplicate human brains, but exhibit similar abilitiesComplex networksRepetitious trainingANS learns by example13-*

Single Artificial Neuron13-*

The Multi-Layer PerceptronYn2INnOUTnOUT1IN1Y1Input LayerOutputLayer13-*

Knowledge-based Systems in PerspectiveMuch has been accomplished in neural nets and expert systemsMuch work remainsSystems abilities to mimic human intelligence are too limited and regarded as primitive13-*

1325810111213141523456789101213141516212330313233353635