MGS 3100 Business Investigation Presentation - Why Business Examination Aug 23, 2010

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MGS 3100 Business Analysis. Course Overview. Deterministic Models versus ... Choice Analysis. Compelled Optimization. Monte Carlo Simulation. By information sort. Time ...

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MGS 3100 Business Analysis Introduction - Why Business Analysis Aug 23, 2010

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Introduction to Decision Sciences Agenda Business Analysis - Models The Modeling Process

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What is Decision Sciences Grocery Industry Kroger Travel Industry Delta SkyMiles Marriott Rewards Gambling Industry MGM Mirage Players Club The Mirage Treasure Island Bellagio New York New York MGM Grand Retail Business Best Buy Circuit City Macy

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Agenda Introduction to Decision Sciences Business Analysis - Models The Modeling Process

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MGS 3100 Business Analysis Course Overview

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Deterministic Models versus Probabilistic (Stochastic) Models Deterministic Models will be models in which every single significant dat are thought to be known with sureness. can deal with complex circumstances with numerous choices and limitations are exceptionally valuable when there are couple of uncontrolled model information sources that are unverifiable. are valuable for an assortment of administration issues. are anything but difficult to consolidate imperatives on factors. programming is accessible to streamline compelled models. takes into account administrative understanding of results. obliged enhancement gives valuable approach to edge circumstances. will build up your capacity to define models all in all.

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Deterministic Models versus Probabilistic (Stochastic) Models Probabilistic (Stochastic) Models will be models in which a few contributions to the model are not known with conviction. instability is joined through probabilities on these "arbitrary" factors. exceptionally helpful when there are just a couple of unverifiable model information sources and few or no requirements. frequently utilized for key basic leadership including an association's relationship to its surroundings.

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Classification of Models By issue sort Forecasting Decision Analysis Constrained Optimization Monte Carlo Simulation By information sort Time arrangement Exponential smoothing Moving normal Cross sectional Multiple straight relapse By causality Causal: causal variable Non-causal: surrogate variable Methodologies 1. Subjective Delphi Methods 2. Quantitative - Non-measurable Using "comparables " 3. Quantitative - Statistical Time-arrangement Regression

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Reasons for Using Models compel you to: Be unequivocal about your targets Identify and record the choices that impact those destinations Identify and record collaborations and exchange offs among those choices Think painstakingly about factors to incorporate and their definitions in wording that are quantifiable Consider what information are appropriate for evaluation of those factors and deciding their associations Recognize imperatives (confinements) on the qualities that those evaluated factors may expect Allow correspondence of your thoughts and comprehension to encourage cooperation

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Introduction to Decision Sciences Agenda Business Analysis - Models The Modeling Process

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The Modeling Process Quantitative - Statistical Describe Problem/opportunity Identify Overall Objective Organize Sub-Objectives into a chain of command Objective Hierarchies Variables and Attributes Identify Model's Objective Determine all factors and their characteristics Decide on Measurement/Data Collection Influence Diagrams Graphically delineate connections among factors Distinguish amongst Decision and result factors Mathematical Representation Determine scientific connections among factors Develop numerical model(s) Testing and Validation Evaluate dependability and legitimacy Understand impediments Implementation and utilize Implement models in DSSs Clarify suppositions, sources of info, and yields

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Manager examines circumstance (choices) These means Use Spreadsheet Modeling Makes choice to determine strife Decisions are actualized Consequences of choice The Modeling Process Quantitative – Non-Statistical Managerial Approach to Decision Making

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The Modeling Process As connected to the initial two phases of basic leadership Model Results Analysis Symbolic World Abstraction Interpretation Real World Management Situation Decisions Intuition

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The Modeling Process The Role of Managerial Judgment in the Modeling Process: Analysis Model Results Symbolic World Managerial Judgment Abstraction Interpretation Real World Management Situation Decisions Intuition

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Building Models To demonstrate a circumstance, you first need to edge it (i.e. build up a sorted out state of mind about the circumstance). An issue proclamation includes conceivable choices and a technique for measuring their viability. Ventures in displaying: Study the Environment to Frame the Managerial Situation Formulate a particular representation Construct a typical (quantitative) show

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Model Decisions (Controllable) Performance Measure(s) Endogenous Variables Exogenous Variables Parameters (Uncontrollable) Consequence Variables Building Models Studying the Environment Select those parts of reality important to the current circumstance. Definition Specific suspicions and improvements are made. Choices and goals must be expressly distinguished and characterized. Distinguish the model's major reasonable fixings utilizing "Discovery" approach. The "Discovery" View of a Model

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A + B Cost B Var. Y Cost A Var. X Building Models Study the Environment to Frame the Managerial Situation The following stride is to develop a typical model. Numerical connections are produced. Diagramming the factors may characterize the relationship. To do this, utilization "Displaying with Data" method.

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Iterative Model Building DEDUCTIVE MODELING Decision Modeling ('What If?' Projections, Optimization) Decision Modeling ('What If?' Projections, Decision Analysis, Decision Trees, Queuing) Models Model Building Process PROBABILISTIC MODELS DETERMINISTIC Models Data Analysis (Forecasting, Simulation Analysis, Statistical Analysis, Parameter Estimation) Data Analysis (Data Base Query, Parameter Evaluation INFERENTIAL MODELING

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Modeling and Real World Decision Making Four Stages of applying displaying to true basic leadership: Stage 1: Study the earth, detail the model and develop the model. Arrange 2: Analyze the model to create comes about. Arrange 3: Interpret and approve show comes about. Organize 4: Implement approved information.

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Modeling and Real World Decision Making Management Lingo Modeling Term Formal Definition Example Decision Variable Lever Controllable Exogenous Investment Input Quantity Amount Parameter Gage Uncontrollable Exogenous Interest Rate Input Quantity Consequence Outcome Endogenous Output Commissions Variable Variable Paid Performance Yardstick Endogenous Variable Return on Measure Used for Evaluation Investment (Objective Function Value)