Chapter 1 - Introduction Flashcards
In this topic: - Body of Knowledge - Problem Solving and Decision Making - Quantitative Analysis and Decision Making - Quantitative Analysis - Models of Cost, Revenue, and Profit - Quantitative Methods in Practice
The body of knowledge involving quantitative approaches to decision making is referred to as: (3)
- Management Science
- Operations Research
- Decision Science
It had its early roots in World War II and is flourishing in business and industry, in part, to: (2)
- Numerous Methodological Developments (E.g., Simplex Method for Solving Linear Programming Problems)
- A Virtual Explosion in Computing Power
The 7 steps of Problem Solving (The First 5 Steps are the Process of Decision Making)
- Define the Problem
- Determine the Set of Alternative Solutions
- Determine the Criteria for Evaluating Alternatives
- Evaluate the Alternatives
- Choose an Alternative (Make a Decision)
- Implement the Selected Alternative
- Evaluate the Results
Decision-Making Process: Structuring the Problem: (3)
- Define the Problem
- Identify the Alternatives
- Determine the Criteria
Decision-Making Process: Analyzing the Problem: (2)
- Identify the Alternatives
- Choose an Alternative
Problems in which the objective is to find the best solution with respect to one criterion are referred to as:
Single-Criterion Decision Problems
Problems that involve more than one criterion are referred to as:
Multicriteria Decision Problems
Based largely on the manager’s judgement and experience; Includes the manager’s intuitive “feel” for the problem; Is more of an art than a science.
Qualitative Analysis
Analyst will concentrate on the quantitative facts or data associated with the problem; Analyst will develop mathematical expressions that describe the objectives, constraints, and other relationships that exist in the problem; Analyst will use one or more quantitative methods to make a recommendation.
Quantitative Analysis
Potential Reasons for a Quantitative Analysis Approach to Decision Making: (4)
The problem is:
1. Complex
2. Important
3. New
4. Repetitive
Quantitative Analysis Process: (4)
- Model Development
- Data Preparation
- Model Solution
- Report Generation
Are representations of real objects or situations:
Models
Three Forms of Models: (3)
- Iconic Models
- Analog Models
- Mathematical Models
Physical replicas (Scalar Representations) of real objects:
Iconic Models
Physical in form, but do not physically resemble the object being modeled:
Analog Models
Represent real world problems through a system of mathematical formulas and expressions based on key assumptions, estimates, or statistical analyses:
Mathematical Models
Generally, experimenting with models (compared to experimenting with the real situation): (3)
- Requires less time
- Is less expensive
- Involves less risk
The more closely the model represents the real situation, the accurate the conclusions and predictions will be.
Model Development
A mathematical expression that describes the problem’s objective, such as maximizing profit or minimizing cost.
Consider a simple production problem:
Suppose x denotes the number of units produced and sold each week, and the firm’s objective is to maximize total weekly profit. With a profit of $10 per unit, the ——— ——– is 10x.
Objective Function
A set of restrictions or limitations, such as product capacities.
To continue our example:
- A production capacity ———- would be necessary if, for instance, 5 hours are required to produce each unit and only 40 hours are available per week. The production capacity ———- is given by 5x <_ 40.
- The value of 5x is the total time required to produce x units; the symbol indicates that the production time required must be less than or equal to the 40 hours available.
Constraints
Environmental factors that are not under the control of the decision maker.
In the preceding mathematical model, the profit per unit ($10), the production time per unit (5 hours), and the production capacity (40 hours) are environmental factors not under the control of the manager or decision maker.
Uncontrollable Inputs
Controllable inputs; decision alternatives specified by the decision maker, such as the number of units of a product to produce.
In the preceding mathematical model, the production quantity x is the controllable input to the model.
Decision Variables
If all uncontrollable inputs to the model are known and cannot vary:
Deterministic Model
If any uncontrollable are uncertain and subject to variation:
Are often more difficult to analyze:
In our simple production example, if the number of hours of production per unit could vary from 3 to 6 hours depending on the quality of the raw material, the model would be ———-.
Stochastic (or Probabilistic) Model
