Chapters 1&2 lecture notes

Data Analysis
• Describing data and datasets

Introduction to Data Analysis and Decision Making

• Making inferences from data and datasets
• Searching for relationships in data and datasets Decision Making

Uncertainty

• Optimization

• Measuring uncertainty

• Decision analysis with uncertainty

• Modeling and simulation

• Sensitivity Analysis

1

What is Management Science?
• Logical, systematic approach to decision making using quantitative methods.
• Science Scientific methods used to solve business related problems.
• Goal for this class: logically approach and solve many different problems.

Observation
• Identify the problem
• Problem does not imply that there is something wrong with the process
• “Problem” could imply need for improvement Management Science Approach to Problem Solving
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Observation
Definition of the Problem
Constructing the Model
Solving the Model/problem
Implementation of Solution
(process is never really complete)

Definition of the Problem
• Clearly define problem
• Prevents incorrect/inappropriate solution
• Listing goals could be helpful

2

Constructing the Model
• Represents the problem in abstract form
• Schematic, scale, mathematical relationship between variables (equation)
• Ex: Income = Hours Worked * Pay

Model Solution
• Same as solving the problem:
• Ex:
Z = $20X – 5X subject to
4X = 100
• Solution:

Components of the Model
• Variable/Decision Variables
– Independent
– Dependent

• Objective Function
• Parameter
• Constraints

Implementation of Solution
• Solution aids us in making a decision but does not constitute the actual decision making. X=25 Z = $375

3

Example

Msci Approach to Problem Solving

Blue Ridge Hot Tubs manufactures and sell hot tubs.
The company needs to decide how many hot tubs to produce during the next production cycle. The company buys prefabricated fiberglass hot tub shells from a