Chapter 2: Decision Making, Systems, Modeling, and Support

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Chapter 2 Decision Making Modeling and Support Conceptual Foundations of Decision The Systems Approach How Support is Provided.
Typical Business Decision Aspects Decision may be made by a group Several possibly contradictory objectives Hundreds or thousands of alternatives Results can occur in the future.
Attitudes towards risk What if scenarios Trial and error experimentation with the real system mayresult in a loss Experimentation with the real system can only be done once.
Changes in the environment can occur continuouslyDecision Support Systems and Intelligent Systems Efraim Turban and Jay E AronsonCopyright 1998 Prentice Hall Upper Saddle River NJ How are decisions made What methodologies can be applied .
What is the role of information systems insupporting decision making Decision Support Systems.
Decision Support Systems and Intelligent Systems Efraim Turban and Jay E AronsonCopyright 1998 Prentice Hall Upper Saddle River NJ 2 3 Systems A SYSTEM is a collection of objectssuch as people resources concepts .
and procedures intended to performan identifiable function or to serve a System Levels Hierarchy Allsystems are subsystemsinterconnected through interfaces.
Decision Support Systems and Intelligent Systems Efraim Turban and Jay E AronsonCopyright 1998 Prentice Hall Upper Saddle River NJ The Structure of aThree Distinct Parts of Systems Inputs.
Processes Outputs Are surrounded by an environment Frequently include a feedback mechanismA human the decision maker is usually.
considered part of the systemDecision Support Systems and Intelligent Systems Efraim Turban and Jay E AronsonCopyright 1998 Prentice Hall Upper Saddle River NJEnvironmentInput s Output s .
Boundary 6 How to Identify theEnvironment Answer Two Questions1 Does the element matter relative to the system s.
goals YES 2 Is it possible for the decision maker to significantlymanipulate this element NO Environmental Elements Can Be Social.
Political Physical Economical Often Other Systems Closed and Open Systems.
A Closed System is totally independent ofother systems and subsystems An Open System is very dependent on itsenvironmenta continuum.
Defining manageable boundaries is closingthe system TABLE 2 1 A Closed Versus an Open Inventory SystemManagementScience EOQ Inventory DSS.
Factors Closed System Open System Demand Constant Variable influenced by manyUnit cost Constant May changedailyLead time Constant Variable difficult to predictVendors and users Excluded from May beincluded in analysis.
Weather and other Ignored May influencedemand andenvironmental factors lead timeDecision Support Systems and Intelligent Systems Efraim Turban and Jay E AronsonCopyright 1998 Prentice Hall Upper Saddle River NJ An Information System.
Collects processes stores analyzes anddisseminates information for a specific Is often at the heart of many organizations Accepts inputs and processes data toprovide information to decision makers.
and helps decision makers communicatetheir resultsDecision Support Systems and Intelligent Systems Efraim Turban and Jay E AronsonCopyright 1998 Prentice Hall Upper Saddle River NJ Performance.
MeasurementEffectiveness and EffectivenessEfficiencyis the degree to which goals areDoing the right thing .
Efficiency is a measure of the use of inputs orresources to achieve outputsDoing the thing right Productivity efficiency effectiveness Measurable .
several non quantifiable conflicting goals MSS effectiveness focusDecision Support Systems and Intelligent Systems Efraim Turban and Jay E AronsonCopyright 1998 Prentice Hall Upper Saddle River NJ 2 4 Models.
Major Component of DSS Use Models instead of experimenting on thereal system A model is a simplified representation orabstraction of reality .
Reality is generally too complex to copy exactly Much of the complexity is actually irrelevant inproblem solvingDecision Support Systems and Intelligent Systems Efraim Turban and Jay E AronsonCopyright 1998 Prentice Hall Upper Saddle River NJ.
Degrees of ModelAbstraction Least to Most Iconic Scale Model Physical replica of a Analog Model behaves like the real system.
but does not look like it symbolicrepresentation Mathematical Quantitative Models usemathematical relationships to representcomplexity.
Used in most DSS analysesDecision Support Systems and Intelligent Systems Efraim Turban and Jay E AronsonCopyright 1998 Prentice Hall Upper Saddle River NJ Benefits of ModelsAn MSS employs models because.
1 Time compression2 Easy model manipulation3 Low cost of construction4 Low cost of execution especially that of errors 5 Can model risk and uncertainty.
6 Can model large and extremely complex systemswith possibly infinite solutions7 Enhance and reinforce learning and enhanceComputer graphics advances complement mathmodels using more iconic and analog models visual.
simulation Modeling PreviewExample How Much to Order for the Ma Pa The Question How much bread to stockSeveral Solution Approaches.
Trial and Error Simulation Optimization HeuristicsDecision Support Systems and Intelligent Systems Efraim Turban and Jay E Aronson.
Copyright 1998 Prentice Hall Upper Saddle River NJ Decision Making Decision a reasoned choice among alternatives Examples Where to advertise a new product.
What stock to buy What movie to see Where to go for dinnerDecision Making a process of choosing amongalternative courses of action for the purpose.
of attaining a goal or goals Decision making vs problem solving ART or SCIENCE The Decision MakingSystematic Decision Making Process Simon s.
Intelligence Design Choice ImplementationModeling is Essential to the Process.
Decision Support Systems and Intelligent Systems Efraim Turban and Jay E AronsonCopyright 1998 Prentice Hall Upper Saddle River NJ Intelligence phase Reality is examined The problem is identified and defined.
Design phase Representative model is constructed The model is validated and evaluation criteria are set Choice phase Includes a proposed solution to the model.
If reasonable move on to the Implementation phase Solution to the original problemFailure Return to the modeling processOften Backtrack Cycle Throughout the Process.
Decision Support Systems and Intelligent Systems Efraim Turban and Jay E AronsonCopyright 1998 Prentice Hall Upper Saddle River NJ 2 6 The Intelligence PhaseScan the environment to identify problemsituations or opportunities.
Identify organizational goals and objectives Determine whether they are being met Explicitly define the problemClassify the problemDecompose into sub problems.
Is it my problem ownership Can I solve itOutcome Problem statementProblem or Symptom Decision Support Systems and Intelligent Systems Efraim Turban and Jay E Aronson.
Copyright 1998 Prentice Hall Upper Saddle River NJ 2 7 The Design Phase Generating developing and analyzingpossible courses of action Understanding the problem.
Testing solutions for feasibility A model is constructed tested and validated Conceptualization of the problem Abstraction to quantitative and or qualitative formsDecision Support Systems and Intelligent Systems Efraim Turban and Jay E Aronson.
Copyright 1998 Prentice Hall Upper Saddle River NJ Types of Decisions Type of structure Nature of taskStructured UnstructuredLevel of decision making Scope.
ManagerialOperational Nature of Decision Structured Problems Routine and repetitive with standard solution.
Well defined decision making procedure Given a well defined set of input a well defined set of output is Semi structured Problems Has some structured aspect Some of the inputs or outputs or procedures are not well.
Unstructured Problems All phases of decision making process are unstructured Not well defined input output set and procedures Scope of Decision Operational Planning and Control .
Focus on efficient and effective execution of specific tasks They affect activities taking place right now E g What should be today s production level Management Control and Tactical Planning Focus on effective utilization of resources.
more longer range planning horizon E g What is next years production level Strategic Planning Long range goals and policies for resource allocation E g What new products should be offered.
Information CharacteristicsCharacteristics Operational Managerial StrategicAccuracy High LowLevel of detail Detailed AggregateTime horizon Present Future.
Use Frequent InfrequentSource Internal ExternalScope Narrow WideNature Quantitative QualitativeAge Current Current old.
3x3 Decision Type GridOperational Tactical StrategicStructuredstructuredUnstructured.
Assume you own a car dealership State three decisionsthat you or your employees would make that fit in the grid Explain the placement of each decision in the grid Mathematical Model Identify Variables.
Establish Equations describing theirRelationships Simplifications through Assumptions Balance Model Simplification and theAccurate Representation of Reality.
Modeling An Art and ScienceDecision Support Systems and Intelligent Systems Efraim Turban and Jay E AronsonCopyright 1998 Prentice Hall Upper Saddle River NJ Quantitative ModelsUncontrollable.
Decision Mathematical ResultVariables Relationships VariablesDecision Variables Describe alternative courses of action The decision maker controls them.
Result Variables Reflect the level of effectiveness of the system Dependent variablesResults of Decisions are Determined by the Decision.
Uncontrollable Factors Relationships among Variables TABLE 2 2 Examples of the Components of Models UncontrollableDecision Result Variables and.
Area Variables Variables ParametersFinancial investment Investment Total profit Inflation ratealternatives and Rate of return ROI Prime rateamounts Earnings per share CompetitionHow long to invest Liquidity level.
When to investMarketing A dvertising budget Market share Customers incomeWhere to advertise Customer satisfaction Competitors actionsManufacturing What and how much Total cost Machine capacityto produce Quality level Technology.
Inventory levels Employee Materials pricesCompensation satisfactionA ccounting Use of computers Data processing cost ComputerA udit schedule Error rate technologyLegal requirements.
Transportation Shipments schedule Total transport cost Delivery distanceRegulationsServices Staffing levels Customer satisfaction Demand for services Uncontrollable Variables orParameters.
Factors that affect the result variables Not under the control of the decision maker Generally part of the environment Some constrain the decision maker and arecalled constraints.
Intermediate Result Variables Reflect intermediate outcomes The Structure ofQuantitative Models Mathematical expressions e g .
equations or inequalities connectthe components Simple financial type model Present value modelP F 1 i n.
Decision Support Systems and Intelligent Systems Efraim Turban and Jay E AronsonCopyright 1998 Prentice Hall Upper Saddle River NJThe Product Mix Linear Programming Model MBI Corporation Decision How many computers to build next month .
Two types of computers Labor limit Materials limit Marketing lower limitsConstraint CC7 CC8 Rel Limit.
Labor days 300 500 200 000 moMaterials 10 000 15 000 8 000 000 moUnits 1 100Units 1 200Profit 8 000 12 000 Max.
Objective Maximize Total Profit MonthDecision Support Systems and Intelligent Systems Efraim Turban and Jay E AronsonCopyright 1998 Prentice Hall Upper Saddle River NJ Example LinearProgramming Model.
ComponentsDecision variablesResult variableUncontrollable variables constraints .
SolutionX1 333 33Profit 5 066 667Decision Support Systems and Intelligent Systems Efraim Turban and Jay E AronsonCopyright 1998 Prentice Hall Upper Saddle River NJ.
The Principle of ChoiceIn a DSS the what-if and the goal-seeking options must be easy to perform 2.10 The Implementation Phase There is nothing more difficult to carry out, nor more doubtful of success, nor more dangerous to handle, than to initiate a new order of things (Machiavelli [1500s]) *** The Introduction of a Change *** Important Issues Resistance to change ...

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