MGS 3100 Business Analysis Introduction Why Business Analysis

MGS 3100 Business Analysis Introduction - Why Business Analysis Jan 14, 2016 Georgia State University - Confidential MGS 3100_01. ppt/Jan 14, 2016/Page 1

Agenda Introduction to Decision Sciences Georgia State University - Confidential Business Analysis Models The Modeling Process MGS 3100_01. ppt/Jan 14, 2016/Page 2

What is Decision Sciences Grocery Industry • Kroger Travel Industry • Delta Sky. Miles • Marriott Rewards Gambling Industry • MGM Mirage Players Club • The Mirage • Treasure Island • Bellagio • New York • MGM Grand Retail Business • Best Buy • Circuit City • Macy Georgia State University - Confidential MGS 3100_01. ppt/Jan 14, 2016/Page 3

Agenda Introduction to Decision Sciences Georgia State University - Confidential Business Analysis Models The Modeling Process MGS 3100_01. ppt/Jan 14, 2016/Page 4

MGS 3100 Business Analysis Course Overview Georgia State University - Confidential MGS 3100_01. ppt/Jan 14, 2016/Page 5

Deterministic Models vs. Probabilistic (Stochastic) Models Deterministic Models • are models in which all relevant data are assumed to be known with certainty. • can handle complex situations with many decisions and constraints • are very useful when there are few uncontrolled model inputs that are uncertain. • are useful for a variety of management problems. • are easy to incorporate constraints on variables. • software is available to optimize constrained models. • allows for managerial interpretation of results. • constrained optimization provides useful way to frame situations. • will help develop your ability to formulate models in general. Georgia State University - Confidential MGS 3100_01. ppt/Jan 14, 2016/Page 6

Deterministic Models vs. Probabilistic (Stochastic) Models • are models in which some inputs to the model are not known with certainty. • uncertainty is incorporated via probabilities on these “random” variables. • very useful when there are only a few uncertain model inputs and few or no constraints. • often used for strategic decision making involving an organization’s relationship to its environment. Georgia State University - Confidential MGS 3100_01. ppt/Jan 14, 2016/Page 7

Classification of Models By problem type • Forecasting • Decision Analysis • Constrained Optimization • Monte Carlo Simulation By data type • Time series • Exponential smoothing • Moving average • Cross sectional • Multiple linear regression Methodologies 1. Qualitative Delphi Methods 2. Quantitative - Nonstatistical Using “comparables” 3. Quantitative - Statistical Time-series Regression By causality • Causal: causal variable • Non-causal: surrogate variable Georgia State University - Confidential MGS 3100_01. ppt/Jan 14, 2016/Page 8

Reasons for Using Models force you to: • Be explicit about your objectives • Identify and record the decisions that influence those objectives • Identify and record interactions and trade-offs among those decisions • Think carefully about variables to include and their definitions in terms that are quantifiable • Consider what data are pertinent for quantification of those variables and determining their interactions • Recognize constraints (limitations) on the values that those quantified variables may assume • Allow communication of your ideas and understanding to facilitate teamwork Georgia State University - Confidential MGS 3100_01. ppt/Jan 14, 2016/Page 9

Agenda Introduction to Decision Sciences Georgia State University - Confidential Business Analysis Models The Modeling Process MGS 3100_01. ppt/Jan 14, 2016/Page 10

The Modeling Process Quantitative - Statistical Objective Hierarchies Variables and Attributes Influence Diagrams Mathematical Representation Testing and Validation Implementation and use Georgia State University - Confidential • Describe Problem / opportunity • Identify Overall Objective • Organize Sub-Objectives into a hierarchy • Identify Model’s Objective • Determine all variables and their attributes • Decide on Measurement / Data Collection • Graphically depict relationships among variables • Distinguish between Decision and outcome variables • Determine mathematical relationships among variables • Develop mathematical model(s) • Evaluate reliability and validity • Understand limitations • Implement models in DSSs • Clarify assumptions, inputs, and outputs MGS 3100_01. ppt/Jan 14, 2016/Page 11

The Modeling Process Quantitative – Non-Statistical Managerial Approach to Decision Making Manager analyzes situation (alternatives) These steps Use Spreadsheet Modeling Makes decision to resolve conflict Decisions are implemented Consequences of decision Georgia State University - Confidential MGS 3100_01. ppt/Jan 14, 2016/Page 12

The Modeling Process As applied to the first two stages of decision making Real World Management Situation Georgia State University - Confidential Results Interpretation Symbolic World Analysis Abstraction Model Intuition Decisions MGS 3100_01. ppt/Jan 14, 2016/Page 13

The Modeling Process The Role of Managerial Judgment in the Modeling Process: Analysis Real World Management Situation Georgia State University - Confidential Results Managerial Judgment Interpretation Symbolic World Abstraction Model Decisions Intuition MGS 3100_01. ppt/Jan 14, 2016/Page 14

Building Models • To model a situation, you first have to frame it (i. e. develop an organized way of thinking about the situation). • A problem statement involves possible decisions and a method for measuring their effectiveness. • Steps in modeling: 1. Study the Environment to Frame the Managerial Situation 2. Formulate a selective representation 3. Construct a symbolic (quantitative) model Georgia State University - Confidential MGS 3100_01. ppt/Jan 14, 2016/Page 15

Building Models 1. Studying the Environment • Select those aspects of reality relevant to the situation at hand. 2. Formulation • Specific assumptions and simplifications are made. • Decisions and objectives must be explicitly identified and defined. • Identify the model’s major conceptual ingredients using “Black Box” approach. Decisions (Controllable) Parameters (Uncontrollable) Georgia State University - Confidential Model Performance Measure(s) Consequence Variables Endogenous Variables Exogenous Variables The “Black Box” View of a Model MGS 3100_01. ppt/Jan 14, 2016/Page 16

Building Models Study the Environment to Frame the Managerial Situation • The next step is to construct a symbolic model. • Mathematical relationships are developed. Graphing the variables may help define the relationship. Var. Y 3. Co st B A+B Cost A Var. X • To do this, use “Modeling with Data” technique. Georgia State University - Confidential MGS 3100_01. ppt/Jan 14, 2016/Page 17

Iterative Model Building DEDUCTIVE MODELING (‘W Dec ha isio t n Op If? ’ M tim Pr ode iza oje lin tio ctio g n) ns , Models on ) si ing i g lin Dec ueu e od s, Q M ion es, on ct re si oje n T i c r o De ? ’ P isi c Models f t I , De a h is (‘W lys a An Model Building PROBABILISTIC MODELS DETERMINISTIC MODELS Process An (Fo al re Dat Models y c a Pa sis ast An ra , S ing aly m tat , S s et is er tic im is Es al ula tim An tio at aly n io n) sis , Models is , ys ery ion l a u t An e Q alua ta as v Da a B er E at et (D am r Pa INFERENTIAL MODELING Georgia State University - Confidential MGS 3100_01. ppt/Jan 14, 2016/Page 18

Modeling and Real World Decision Making Four Stages of applying modeling to real world decision making: • Stage 1: Study the environment, formulate the model and construct the model. • Stage 2: Analyze the model to generate results. • Stage 3: Interpret and validate model results. • Stage 4: Implement validated knowledge. Georgia State University - Confidential MGS 3100_01. ppt/Jan 14, 2016/Page 19

Modeling and Real World Decision Making Modeling Term Management Lingo Formal Definition Decision Variable Lever Parameter Gauge Uncontrollable Exogenous Input Quantity Consequence Variable Outcome Endogenous Output Commissions Variable Paid Performance Measure Yardstick Endogenous Variable Used for Evaluation (Objective Function Value) Georgia State University - Confidential Controllable Exogenous Input Quantity Example Investment Amount Interest Rate Return on Investment MGS 3100_01. ppt/Jan 14, 2016/Page 20