# MBA 7020 Business Investigation Establishments Presentation - Why Business Examination June 13, 2005

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Business Analysis - Models. The Modeling Process. Prologue to Decision Sciences ... Choice Analysis and Influence Diagrams for Visualizing Models and Choices ...

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﻿MBA 7020 Business Analysis Foundations Introduction - Why Business Analysis June 13, 2005

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

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Analytical Methods Information Technology Decision Making Decision Sciences: Conceptualized!

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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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MBA 7020 Business Analysis Foundations Course Overview

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Deterministic Models versus Probabilistic (Stochastic) Models Deterministic Models will be models in which every single applicable dat are thought to be known with conviction. can deal with complex circumstances with numerous choices and imperatives are exceptionally helpful when there are couple of uncontrolled model data sources that are questionable. are valuable for an assortment of administration issues. are anything but difficult to consolidate imperatives on factors. programming is accessible to advance obliged models. considers administrative translation of results. compelled enhancement gives valuable approach to edge circumstances. will build up your capacity to detail models when all is said in done.

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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 assurance. instability is consolidated by means of probabilities on these "irregular" factors. extremely helpful when there are just a couple of questionable model information sources and few or no requirements. regularly 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 direct relapse By causality Causal: causal variable Non-causal: surrogate variable Methodologies 1. Subjective Delphi Methods 2. Quantitative - Non-factual Using "comparables " 3. Quantitative - Statistical Time-arrangement Regression

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Analytical Methods Qualitative Methods Nominal Group Techniques Heuristic Based Methods Expert Systems/AI Quantitative Methods Mathematical/Algebraic/Calculus Methods Statistical Modeling and Analysis Management Science/Operations Research Techniques Accounting/Financial Modeling

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Decision Environment DATA BASED MODEL BASED KNOWLEDGEBASED UNCERTAINTY COMPLEXITY EQUIVOCALITY Facts not known Gather Information Fact Finding/.Analysis Too numerous realities Generate Information Simulation/Synthesis Facts not Clear Interpret Information Application of Expertise

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Decision Making Process INTELLIGENCE Fact Finding Problem/Opportunity Sensing Analysis/Exploration Formulation of Solutions Generation of Alternatives Modeling/Simulation DESIGN Alternative Selection Goal Maximization Decision Making Implementation CHOICE

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STRATEGIC TACTICAL OPERATIONAL Types and Levels of Decisions DECISION SUPPORT UNSTRUCTURED MANAGEMENT INFORMATION STRUCTURED TRANSACTION PROCESSING

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Applications of Information Technology Transaction Processing Systems Management Information Systems Decision Support Systems

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Data Base Decision Support Systems Model Base Knowledge Base User Interface

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

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Organizational Context Managing Organizations Informed basic leadership as an essential for achievement Vision Mission Values, Purpose, Structure, Politics, Environment, and so forth. Givens Strategic Direction Policies, Goals, and Objectives What ought to be done ? Basic leadership Analytics, Decision Making When and how ?? Usage Project Management Action

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Complexity What does it signify? Instability What can happen? MODELS INTELLIGENCE DATA DESIGN CHOICE Managerial Decision Making Information Technology Solutions for Improving Effectiveness Variables (Measures and Estimates) Probabilities and Estimates Structuring Relationships Problem Representation Generation of Alternatives Decision Analysis and Influence Diagrams for Visualizing Models and Choices Spreadsheet Models for overseeing complex connections and detail

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Modeling Decision Situations Process for Developing Meaningful and Robust Models Values, Goals, Strategies, and so forth Fundamental and Means Objectives (practical?) Objective Hierarchies Decision, Intermediate, and Outcome Variables Data, Probabilities, Distributions Variables and Measures Influence Diagrams and Decision Trees Situation Structuring Spreadsheet Modeling Statistical, OR, Financial, Acctg. Models Modeling Relationships Testing and Validation DSS Implementation and Use Communicate Analyze & Synthesize

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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 properties 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 unwavering quality and legitimacy Understand confinements Implementation and utilize Implement models in DSSs Clarify presumptions, sources of info, and yields

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The Decision Analysis Process Tools for Visualizing and Evaluating Alternatives Identify choice circumstance and comprehend destinations Decision, Chance, and Consequence Variables Arcs and Relationship Formulas Model Representation Identify options Tornado Diagrams N-way Sensitivity Deterministic Analysis Decompose and display issue structure vulnerability inclinations Uncertainty Assessment Risk Profiles Probabilistic Analysis Sensitivity Analyses Choose best option Evaluation of Alternatives EMV, NPV, and so on. Actualize Decision

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Manager dissects circumstance (options) These means Use Spreadsheet Modeling Makes choice to determine struggle Decisions are executed Consequences of choice The Modeling Process Quantitative – Non-Statistical Managerial Approach to Decision Making