Chapter 1
Introduction
Exposure to quantitative methods will teach
managers to ask the right questions.
Quantitative
Decision Making
1
Introduction
• Quantitative methods can be applied to
decision making (business problems) in
general and can be used by individuals
(managers) or groups, in every type of
organizations.
• It is important to know quantitative methods
and how they can be used in solving variety
of actual problems such as:
2
Managing R&D
Determining Number of Bank Tellers
Locating Warehouses
Designing Ports
Developing Fire Fighting Companies
Investing
Distribution
Managing Inventory
Scheduling Flights
3
Further Successes




4
Optimizing Agriculture
Waiting in Lines
Satellite Communications
Political Campaign Strategies
Quantitative Methods
Classification
I.
II.
III.
IV.
5
Resource Allocation
Distribution, Routing, and Scheduling
Inventory Management
Simulation and Waiting Lines
Management Science and
Operations Research
 Management Science & Operation
Research: are concerned with selecting the
best alternative course of action whenever
mathematics can be helpful in reaching a
decision.
 Finding Optimal Solutions: an optimal
solution is the one that yields maximum
profit or minimizing cost.
6
Management Science and
Operations Research
 Moreover, is some applications, an optimal
solution might be the most effective
alternative in terms of time, reliability, or
one of many kinds of measures.
 Mathematical Optimization Procedures: a
Mathematical Optimization Procedure can
be defined as the particular quantitative
method for finding the best solution.
7
Models and Decision Making
 Alternatives & Quantitative Methods:
every decision-making situation involves possible
alternatives. Therefore, quantitative methods are
used to select the alternative that best satisfies the
decision maker’s goals.
 The Mathematical Model (Parameters &
Variables):
the first step in applying quantitative methods is
generally to express the problem mathematically.
Such a formulation is called a mathematical
model.
8
Models and Decision Making
 All mathematical models consists of “variables”
and constant terms, which are sometimes referred
to as “parameters”. The variables and parameters
are usually linked by algebraic expressions that
reflect the decision maker’ goals and any special
limitations on the kinds of alternatives to be
considered.
 Constraints & Feasible Solutions:
sometimes a mathematical model incorporates
constraints are often expressed algebraically.
9
Models and Decision Making
 Constraints and Feasible Solutions
Q < 300
Feasible solution: values of Q not exceeding 300
units.
whereas infeasible solution values of Q exceeding
300 units.
 Optimal Solutions
2 Ak
Q
hc
Quantitative methods are employed to solve the
problem by finding the value of the variables
that meets the requirements of the mathematical
model
10
Algorithms and Model Types
 Algorithms: refer to the solution procedures
used to solve a problem.
 Models classes are deterministic and
stochastic models.
 Deterministic models contain certain
(known & fixed) constants throughout their
formulation.
 Stochastic models are developed to solve
problems that involve one or more
uncertain quantities.
11
The Importance of Studying
Quantitative Methods




12
Increases decision-making confidence.
Provides problem-solving skills.
Raises ability to cope with decisions.
Learn spreadsheet skills.
Computer Solutions
 Excel Spreadsheets
A
13
B
C
D
E
F
INVENTORY ANAYLSIS
1
2
3 Parameter Values:
4
Fixed Cost per Order: k =
$
4.00
5
Annual Number of Items Demanded: A =
1,000
6
Unit Cost of Procuring an Item: c =
$
1.00
7
Annual Holding Cost per Dollar Value: h =
$
0.20
8
9
Decision Variables:
10
Order Quantity: Q =
200.0
11
F
12
Results:
10 =SQRT((2*F5*F4)/(F7*F6))
13
Total Annual Relevant Cost: TC =
$ 40.00
F
14
13 =(F5/F10)*F4+F7*F6*(F10/2)
15
16
Computer Solutions
 QuickQuant
14