Ch. 1 Overview Flashcards
A set of mathematical relationships and logical assumptions implemented in a computer as a representation of some real world situation, decision, problem, or phenomenon. Organizations use these to help make decisions.
Name an example of one of these.
Computer Model
Example: electronic spreadsheets is the best way to implement and analyze computer models
A computer model implemented via a spreadsheet
Spreadsheet Model
A field of study that uses data, computers, statistics, and mathematics to solve business problems. It involves using the methods and tools of science to drive decision making.
Business Analytics
Also referred to as operations research, management science or decision science.
Organizations can use analytics to identify the best decision alternatives for a given application or situation
Business Analytics
The current trend of business analytics is:
Increasing usage to gain competitive advantage
Focusing on business intergration
Growing Demand for analytics talent
To conduct business analytics you need:
Models
Human insights
Data
Picturing how something will look in your mind’s eye
Mental Model
set of drawings or blueprints for a house
Visual Model
uses a mathematical relationship to describe or represent an object or decision problem
Mathematical Model
- Delivers needed information on a more timely basis
- Helpful in examining situations that would be impossible to do in reality
- Less expensive than implementing several alternative solutions
Benefits of Mathematical Model
What is the dependent variable and independent variable in this equation: Profit = Revenue - Expenses
Profit depends on the revenue and expenses so it is the DEPENDENT VARIABLE
Revenue and Expenses are the INDEPENDENT VARIABLES
the number of independent variables involved is this. Also the relationship between the dependent and independent variables is this.
F = function
What does Y equal in the regression/time series equation
Y = the dependent variable
the value of the inputs in the cell
Input Cells
the results of the input cells
Output Cells
a model that accurately represents the relevant characteristics of the object or decision problem being studied
Valid Model
- the solutions tell the decision maker what actions to take
- known well defined functions
-the independent variables are known or under the decision makers control
Prescriptive Models (OPTIMIZATION)
Examples of Prescriptive Models
Examples: Linear Programming, Networks, Integer Programming, Goal Programming
Real world: Investment portfolio design and Airline Flight schedules
- the function might be unknown and must be estimated in order for the decision maker to make predictions about the dependent variables
- unknown, ill defined functions
-the independent variables are known or under the decision makers control
Predictive Models (FORECASTING)
Examples of Predictive Models
Examples: Regression Analysis, Time Series Analysis, Discriminant Analysis
Realworld: FICO credit scoring and Fraud Detection
- there is a well defined functional relationship, however there is great uncertainty as to the exact values that will be assumed by one or more of the independent variables
- seeks to measure and explain situations
- known, well-defined functions
- the independent variables are unknown or uncertain
Descriptive Models (ANALYSIS)
Examples of Descriptive Models
Examples: Simulation, Queuing, Inventory Models
Realworld: inventory turns and employee absenteeism
Synonym for
Prescriptive Models
Predictive Models
Descriptive Models
- Prescriptive = Optimization
- Predictive = Forecasting
- Descriptive = Analysis
5 steps to the Problem Solving Process (DECISION MAKING PROCESS)
- Identify Problem
- Formulate and Implement Model
- Analyze Model
- Test Results (if unsatisfactory result, go back to forumulate step)
- Implement Solutions (this is considered the most difficult step)
Arise when a seemingly trivial factor serves as a starting point for estimations in a decision making problem
Anchoring Effects
Refer to how a decision maker views or perceives the alternatives in a decision problem - involves a win/loss perspective
Framing Effects
Good Bad
Good Deserved Success Bad Luck
Bad Dumb Luck Poetic Justice
Decision Quality
the factor that plays a role in determining whether a good or bad outcome occurs regardless of the quality of the decision.
Chance
Economy - its often less costly to analyze decision problems using models rather than trying out a decision
Timeliness - Models can often deliver needed
information more quickly than their real-world
counterparts.
Feasibility - Models can be used to do things
that would otherwise be impossible.
Understanding - Models often provide insights
that can improve the decision-making process.
Benefits of Modeling
Machine learning example: Labeled data (X and Y) applications
Example: predicting sports outcomes based on team characteristics
Supervised Machine Learning
unlabeled data is widespread and inexpensive to acquire, while labeled data is limited and expensive to acquire
Example: detecting employee fraud where most of the fraud cases remain unidentified
Semi-Supervised Machine Learning
Unlabeled data only (X) applications
Example: reducing customer churn
Unsupervised Machine Learning
What is a factor that illustrates a constraint.
Budget
Break Even Model formula?
Break-Even point (units) = Fixed Costs ÷ (Sales price per unit – Variable costs per unit)
describes many people using the cloud when working together on one project
E-collaboration
type of management system includes the integration of technology and software across an organization
Enterprise resource planning systems
