Presentation on theme: "Chapter 1: Introduction"— Presentation transcript:
1 Chapter 1: Introduction MGS3100 General ModelingChapter 1: Introduction
2 THE MODELING PROCESS Managerial Approach to Decision Making Manager analyzessituation (alternatives)These stepsUseSpreadsheetModelingMakes decision toresolve conflictDecisions areimplementedConsequences of decision
3 A Detailed View of the Modeling Process Diagnose the problemSelect relevant aspects of realityOrganize the facts; identify the assumptions, objectives, and decisions to be madeSelect the methodologyConstruct the modelSolve the model (generate alternatives)Interpret results (in “lay” terms!)Validate the model (does it work correctly?)Do sensitivity analysis (does the solution change?)Implement the solutionMonitor results
4 THE MODELING PROCESS Model Results Management Situation Decisions AnalysisSymbolicWorldAbstractionInterpretationRealWorldManagementSituationDecisionsIntuition
5 The Modeling Process Managerial Judgment Model Results Management AnalysisModelResultsSymbolicWorldManagerialJudgmentAbstractionInterpretationRealWorldManagementSituationDecisionsIntuition
6 Reasons for Using Models Models force you to:Be explicit about your objectivesThink carefully about variables to include and their definitions in terms that are quantifiableIdentify and record the decisions that influence those objectivesIdentify and record interactions and trade-offs among those decisions
7 Reasons (cont.)Consider what data are pertinent for quantification of those variables and determining their interactionsRecognize constraints (limitations) on the values that those quantified variables may assumeAllow communication of your ideas and understanding to facilitate teamwork
9 Building Models The “Black Box” View of a Model Model Endogenous PerformanceMeasure(s)Decisions(Controllable)Parameters(Uncontrollable)ExogenousVariablesModelConsequenceEndogenous
10 MODELING VARIABLES Management Lingo Modeling Term Formal Definition ExampleDecision Variable Lever Controllable Exogenous InvestmentInput Quantity AmountParameter Gauge Uncontrollable Exogenous Interest RateInput QuantityConsequence Outcome Endogenous Output CommissionsVariable Variable PaidPerformance Yardstick Endogenous Variable Return onMeasure Used for Evaluation Investment(Objective Function Value)
11 Examples of Decision Model Assumptions - Profit Models If it is beyond your control, do not consider it!Overhead costs - a convenient fiction - we ignoreSunk costs - we ignoreDepreciation - only include if we can use to shield future taxesCosts are linear in the short term
12 Building Models Symbolic Model Construction Mathematical relationships are developed from data.Graphing the variables may help define the relationship.Var. XVar. YCost ACost BA + B
13 Modeling with Data Consider the following data. Graphs are created to view any relationship(s) between the variables.This is the first step in formulating the equations in the model.
15 DETERMINISTIC AND PROBABILISTIC MODELS Deterministic Models are models in which all relevant data are assumed to be knownwith certainty.can handle complex situations with many decisions and constraints.are very useful when there are few uncontrolled model inputsthat are uncertain.are useful for a variety of management problems.allow for managerial interpretation of results.will help develop your ability to formulate models in general.
16 DETERMINISTIC AND PROBABILISTIC MODELS Probabilistic (Stochastic) Modelsare models in which some inputs to the model are not knownwith certainty.uncertainty is incorporated via probabilities on these “random”variables.very useful when there are only a few uncertain model inputs andfew or no constraints.often used for strategic decision making involving an organization’srelationship to its environment.
17 ITERATIVE MODEL BUILDING Deductive Modelingfocuses on the variables themselves before data are collected.variables are interrelated based on assumptions about algebraic relationships and values of the parameters.places importance on modeler’s prior knowledge and judgments ofboth mathematical relationships and data values.tends to be “data poor” initially.Inferential Modelingfocuses on the variables as reflected in existing data collections.variables are interrelated based on an analysis of data to determine relationships and to estimate values of parameters.available data need to be accurate and readily available.tends to be “data rich” initially.
18 ITERATIVE MODEL BUILDING DEDUCTIVE MODELING(‘What If?’ Projections,Decision ModelingOptimization)(‘What If?’ Projections, DecisionAnalysis, Decision Trees, Queuing)Decision ModelingModelsModelsModel BuildingProcessPROBABILISTICMODELSDETERMINISTICMODELSModelsModelsAnalysis, Statistical Analysis,(Forecasting, SimulationData AnalysisParameter Estimation)Data Analysis(Data Base Query,Parameter EvaluationINFERENTIAL MODELING
19 Philosophy of Modeling RealismA model is valuable if you make better decisions when you use it than when you don’t.IntuitionA manager’s intuition arbitrates the content of the abstraction, resulting model, analysis, and the relevance and interpretation of the results.
20 Chapter 11: Implementation MGS3100 General ModelingChapter 11: Implementation
21 INTRODUCTIONJust as knowledge of Excel is insufficient without modeling concepts, your knowledge of spreadsheet modeling alone is insufficient for truly affecting decision making in organizations.Creating a model itself, although an important first step, is far from sufficient in the process of systematically improving decision making in the real world of business enterprise.Inadequate modeling is just one of the reasons why decision-makers do not make good decisions.
22 The purpose of this chapter is to help you understand why improving the quality of modeling alone will not necessarily lead to improved real-world decisions.This chapter will cover critical oversights that users new to the concepts of modeling make in attempting to move forward to apply those ideas in actual decision-making situations.The upside and downside potential risks of applying modeling concepts will be discussed so that you will come away with a balanced perspective of the pros and cons of applying modeling in business practical situations.
23 WHAT, AFTER ALL, IS A MODEL? It is difficult to define a model. One definition might be:A model is an abstraction of a business situation suitable for spreadsheet analysis to support decision making and provide managerial insights.To many managers, a model is exquisitely crafted and professionally polished in appearance, highly intuitive, self-documenting, easy to use, completely validated and generalizable enough to be applied in a variety of settings by many people.Consider the following evolution of a model:
24 An Institutionalized Model Sustained by the Organization Integrated into Organization'sDecision ProcessesCoordinated in Function withOther Models and SystemsUseable by Other ManagersMaintainable and Extensibleby OthersNeed Data Supplied andMaintained by OthersA Prototype ModelCompleteDebuggedRunable by Its AuthorValidated with Test DataBelieved to Deliver ValueEffort: 1XEffort: 10X-100XA Modeling ApplicationUsable by a Client ManagerWell DocumentedHardened to Reject UnusualData InputsExtendable by Author or Client Manager Validated withReal-World DataKnown to Deliver ValueAn InstitutionalizedModeling ApplicationEffort: 10XEffort: 100X – 1000X
25 The Separation of Players Curse This framework is a variation of one originally proposed by C. West Churchman, et. al.Modeler,ProjectManager,DecisionMaker,ClientCurse ofPlayer SeparationModelerProject ManagerMaker
26 The Curse of Scope Creep Narrow Modeling ProjectSingle ModelSingle ObjectiveFocused ActivityFew PlayersFew StakeholdersLow EffortLow CostLow Development RiskInformal Coordination & Project ManagementLow Project VisibilityScale Diseconomies in Information Systems for ModelScale Diseconomies in Model & Database MaintenanceDeterioration in Model Use as Early Adopters Move on Low Potential Organization-wide ImpactWide Modeling ProjectMultiple (Replicated) ModelsMultiple ObjectivesDiffused ActivityMany PlayersMany StakeholdersHigh EffortHigh CostHigh Development RiskFormal Coordination & Project ManagementHigh Project VisibilityScale economies in Information Systems for ModelScale Economies in model & Database MaintenanceSupport for Model Use Independent of Early AdoptersHigh Potential Organizational-wide ImpactCurse ofScope Creep
27 Other Frequent Sources of Implementation Failure Easily addressed issues in modeling failure are model logic, model inadequacy, etc.However, inadequate attention to political issues that arise from the use of a model is far more prevalent as a source of failure in modeling.When a model fails, it is all too common to blame the model when in fact, it was due to inadequacies of the whole process of developing and implementing the model.
28 Another problem is the potential loss of continuity either during the development of a model itself or later during implementation caused by departure of key players, or the loss of organizational memory of a successful model.A source of difficulty in modeling is the attempt to develop a modeling application before assessing issues of the data availability necessary to support that application.An important consideration early in the model development phase is the matching of available data to a possibly less-adequate model as a way of avoiding implementation problems later.
29 An infrastructure must be created that guarantees that the data and systems will be maintained in a way that serves the users of the model.A more subtle and insidious shortcoming of modeling concerns the identification of shortcomings at one level of an organization as being caused by failures or inadequacies at a higher, often more abstract, level of the organization.In this case, the best thing to do is to tune the model to work well given other organizational inadequacies that might be addressed more effectively at a later time.