CHAPTER 1: Introduction to Management Optional steps:

Science and Quantitative Techniques 6. Implementing the selected

alternative
➢ decision making based on 7. Evaluating the result.
the scientific method
➢ makes extensive use of 1.2: Quantitative Analysis and
quantitative analysis. Decision-Making
➢ two other widely known and figure 1.2: (classification of decision
accepted names; making process)
(operations research &
decision science)
➢ scientific management
revolution in; early 1900s,
initiated by Frederic W.
Taylor (provided the
foundation for the use of ➢ Qualitative analysis
quantitative methods in ○ based primarily on the
management) manager’s judgement and
experience.
1.1: Problem Solving and Decision ○ it includes the manager’s
Making intuitive “feel” for the
1) problem solving: process of problem.
identifying a difference between ○ more an art than a
the actual and the desired state of science.
affairs and then taking action to ○ applies for relatively simple
resolve the difference. problems
2) decision making: term generally ➢ Quantitative Analysis
associated with the first five steps ○ quantitative facts or data
of the problem-solving process: associated with the
1. Define the problem. problem and develop
2. Identify the alternatives. mathematical expressions.
3. Determine the criteria. ○ applies for sufficiently
i) single-criterion complex problems.
decision problems:
problems in which figure 1.3: (role of quantitative and
the objective is to qualitative analysis)
find the best
solution for one
criterion.
ii) multicriteria decision
problems: problems
that involve more
than one.
4. Evaluate the alternatives.
5. Choose an alternative
1.3: Quantitative Analysis - also referred to as ‘decision
variables’
Process
❖ Model Development figure 1.4: (process of transforming model
○ models; representations of inputs into output)
real objects or situations
and can be presented in
various forms.
○ physical replicas of real
objects.
○ enables us to make
inferences about the real
situation by studying and figure 1.5: (flowchart for the production
analyzing the model. model)

Classification of Models
1) Iconic Model (physical replicas)
2) Analog Model
- physical in form but do not
have the same physical
appearance as the object
being modeled.
- Example: thermometer Classification of Mathematical Model
3) Mathematical Model ➢ Deterministic Model
- representations of a - if all uncontrollable inputs to
problem by a system of a model are known in
symbols and mathematical advance and cannot vary.
relationships or ➢ Stochastic (probabilistic) Model
expressions. - mathematical model that
- the model that is focused treats future demand with
on management science. uncertainty.
- the value of the output
➢ Objective Function- mathematical cannot be determined even
expression that describes the if the value of the
problem’s objective. controllable input is known
because the specific values
➢ Constraints- set of restrictions. of the uncontrollable inputs
are unknown.
➢ Uncontrollable Inputs
- environmental factors, that
are not under the control of
the decision makers

➢ Controllable Inputs
- inputs that are completely
controlled or determined by
the decision-maker.
 ❖ Data Preparation ❖ Report Generation
- refer to the values of the - the results of the model must
uncontrollable inputs to the model. appear in a managerial report that
can be easily understood by the
using the general notation; decision maker.
c= profit per unit - report includes
a= production time in hours per unit a. the recommended decision
b= production capacity in hours b. other pertinent information
about the results that may
❖ Model Solution be helpful to the decision
- In this step, the analyst will attempt maker.
to identify the alternatives decision
variables that provide the “best” ➢ Technical Tools
output for the model. ○ Software for Management
- optimal solution; best output Science: Advances in
- infeasible; rejected if the computer technology have
alternatives does not satisfy all of made management science
the model constraints. techniques more
- feasible; if the alternatives satisfies accessible. Tools like
all of the model constraint. Microsoft Excel and LINGO
are commonly used for
➢ Model Solution For the solving management
Production Problem science problems.
- involves finding the value of ○ Resources: For practical
the production quantity guidance on using these
decision variable x that tools, there are appendices
maximizes profit while not in the textbook that provide
causing a violation of the step-by-step instructions.
production capacity
constraint.
- other procedure;
trial-and-error approach in
which the model is used to
test and evaluate various
decision alternatives.

➢ Model Testing and Validation

- can use small test
problems having known, or
atleast expected.
1.4: Models of Cost, Revenue, and
Profit ❖ Profit and Volume Models
- profit–volume model can be
❖ Cost and Volume Models derived from the revenue–volume
- cost of manufacturing or producing and cost–volume models.
a product is a function of the
volume produced. ❖ Breakeven Analysis
- can usually be defined as a sum of - profit–volume model can be
two costs: fixed cost and variable derived from the revenue–volume
cost; and cost–volume models.
a. Fixed Cost- the portion of - determine the total profit
the total cost that does not associated with any production
depend on the production volume x.
volume. This cost remains - breakeven point: the volume that
the same no matter how results in total revenue equaling
much is produced. total cost.
b. Variable Cost- is the - the breakeven point for a product
portion of the total cost that provides valuable information for a
is dependent on and varies manager who must make a yes/no
with the production volume. decision concerning the production
of the product.
➢ Marginal Cost
- defined as the rate of change of figure 1.5: (example of graph breakeven
the total cost with respect to analysis)
production volume.
- it is the cost increase associated
with a one-unit increase in the
production volume.
- marginal cost may depend on the
production volume (we could have
marginal cost increasing or
decreasing with the production
volume x).

❖ Revenue and Volume Models

- projected revenue associated with
selling a specified number of units.

➢ Marginal Revenue
- defined as the rate of change of
total revenue with respect to sales
volume.
- it is the increase in total revenue
resulting from a one-unit increase
in sales volume.
- we may find marginal revenue
increases or decreases as the
sales volume x increases.
1.5: Management Science Techniques systems involving waiting
lines.
Numerous Applications for the ➢ Stimulation
Following Techniques: - a technique used to model
➢ Linear Programming- is a the operation of a system.
problem-solving approach - employs a computer
developed for situations involving program to model the
maximizing or minimizing a linear operation and perform
function. simulation computations.
➢ Integer Linear Programming ➢ Decision Analysis
- is an approach used for - used to determine optimal
problems that can be set up strategies in situations
as linear programs. involving several decision
alternatives and an
➢ Distribution and Network Models uncertain or risk-filled
- a network is a graphical pattern of events.
description of a problem ➢ Goal Programming
consisting of circles called - a technique for solving
nodes that are multicriteria decision
interconnected by lines problems, usually within the
called arcs. framework of linear
➢ Nonlinear Programming programming.
- is a technique that allows ➢ Analytic Hierarchy Process
for maximizing or - this multicriteria
minimizing a nonlinear decision-making technique
function subject to permits the inclusion of
nonlinear constraints. subjective factors in arriving
➢ Project Scheduling: PERT/CPM at a recommended
- The PERT (Program decision.
Evaluation and Review -
Technique) and CPM ➢ Forecasting
(Critical Path Method) - techniques that can be
techniques help managers used to predict future
carry out their project aspects of a business
scheduling responsibilities. operation.
➢ Inventory Models ➢ Markov Process Models
- used by managers faced - useful in studying the
with the dual problems of evolution of certain systems
maintaining sufficient over repeated trials.
inventories to meet demand
for goods. (methods frequently used):
- incurring the lowest - linear programming,
possible inventory holding - integer programmin
costs. - network models (including supply
➢ Waiting Line or Queueing Model chain models),
- help managers understand - simulation.
and make better decisions
concerning the operation of
SUMMARY OF TERMS
● Breakeven Point: the volume at
which total revenue equals total
cost.
● Constraints: restrictions or
limitations imposed on a problem.
● Controllable Inputs: the inputs that
are controlled or determined by the
decision maker.
● Decision: the alternative selected.
● Decision Making: the process of
defining the problem, identifying
the alternatives, determining the
criteria, evaluating the alternatives,
and choosing an alternative.
● Decision Variable: another term for
controllable input.
● Feasible Solution: a decision
alternative or solution that satisfies
all constraints.
● Fixed Cost: the portion of the total
cost that does not depend on the
volume; this cost remains the
same no matter how much is
produced.
● Infeasible Solution: a decision
alternative or solution that does not
satisfy one or more constraints.
● Objective Function: a mathematical
expression that describes the
problem’s objective.
● Optimal Solution: the specific
decision-variable value or values
that provide the “best” output for
the model.
● Variable Cost: the portion of the
total cost that is dependent on and
varies with the volume.