Business Management

1. Dr. Omar Kanaan
Text Book: Quantitative Analysis for Managment,13th edition, Render Barrry
1
Introduction to Quantitative Analysis and Models
formulations

2. Introduction to Operations Research
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3.  World War II : British military leaders asked scientists and
engineers to analyze several military problems management
of materials, convoy, bombing, antisubmarine, and mining
operations.
 As these teams were generally assigned to the commanders
in charge of military operations, they were called operations
research (OR) teams.
 At the end of the war, many of the scientists who worked in
the military operations research units returned to civilian life
in universities and industries.
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4.  Operational research is the application of the methods of
science to complex problems arising in the direction and
management of large systems of men, machines, materials
and money in industry, business and government.
 The approach is to develop a scientific model of the
system, incorporating measurement of factors such as
opportunities and risk, with which to predict and compare
the outcomes of alternative decisions, strategies or controls.
 The purpose is to help management determine its policy
and actions scientifically.
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5.  Operations Research is the scientific approach to
execute decision making, which consists of :
- The art of mathematical modeling of complex situations.
- The science of the development of solution techniques
used to solve these models.
- The ability to effectively communicate the results to the
decision maker.
OR: the discipline of applying advanced analytical
methods to help make better decisions.
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6. Examples of Quantitative Analyses:
 Taco Bell saved over $150 million using
forecasting and scheduling quantitative analysis
models (1990).
 NBC television increased revenues by over $200
million between (1996 -2000) using quantitative
analysis to develop better sales plans.
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7.  Quantitative analysis :
is a scientific approach to managerial decision making in which
raw data are processed and manipulated to produce
meaningful information.
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Raw Data
Meaningful
Information
Quantitative
Analysis

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 Quantitative factors :
Data that can be accurately calculated. Examples :
◦ Inventory levels
◦ Demand
◦ Labor cost
◦ Interest rates
 Qualitative factors :
Are more difficult to quantify but affect the decision process.
Examples :
◦ The atmosphere
◦ Government and regulations
◦ Technological innovations.

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Defining the problem
Developing a model
Acquiring input data
Developing a solution
Testing the solution
Analyzing the results
Implementing the results

10. Develop a clear and brief statement that gives direction and
meaning to following steps.
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- The most important and difficult step.
- Identify true causes not the symptoms.
- May there are many problems– selecting the right problems is
very important.
- May have to develop Specific and measurable objectives.

11. Models of quantitative analysis are realistic, solvable, and
understandable mathematical representations of a situation.
- Mathematical Model :
is a set of mathematical relationships.
- Schematic model :
is a picture, drawing, or chart of reality. Automobiles, lawn mowers,
gears, fans.
- Scale models of chemical plants ( pilot plant). Architects sometimes
make a physical model of a building that they will construct.
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- Most of the models contain one or more variables and
parameters.
- A variable: is a measurable quantity that may vary or is
subject to change. Variables can be controllable or
uncontrollable. A controllable variable is also called a
decision variable. An example would be how many
inventory items to order?(Unknown)
- A parameter : is a measurable quantity that is inherent in the
problem. The cost of placing an order for more inventory
items is an example of a parameter (Known ).

13. Data may come from a variety of sources such as company
reports, company documents, interviews, on-site direct
measurement, or statistical sampling.
Obtaining accurate data for the model is essential
**
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Even if the model is a perfect representation of reality, improper
data will result in misleading results.
Garbage
in
Process
Garbage
Out

14. - The best (optimal) solution to a problem is found by
manipulating the model variables until a solution is
found that is practical and can be implemented.
Common techniques are:
◦ Solving equations.
◦ Trial and error – trying various approaches and picking the
best result.
◦ Complete enumeration – trying all possible values.
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15. Both input data and the model should be tested for
accuracy before analysis and implementation.
◦ New data can be collected to test the model.
◦ Results should be logical, consistent, and represent the real
situation.
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16. Determine the implications of the solution:
◦ Implementing results often requires change in an
organization.
◦ The impact of actions or changes needs to be studied and
understood before implementation.
Sensitivity analysis :determines how much the results
will change if the model or input data changes.
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.

17. Implementation incorporates the solution into the
company.
◦ Implementation can be very difficult.
◦ People may be resistant to changes.
◦ Many quantitative analysis efforts have failed because a
good, workable solution was not properly implemented.
Changes occur over time, so even successful
implementations must be monitored to determine if
modifications are necessary.
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.

18. Quantitative analysis models are used extensively by
real organizations to solve real problems.
◦ In the real world, quantitative analysis models can be
complex, expensive, and difficult to sell.
◦ Following the steps in the process is an important
component of success.
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.

19. is an important part of the quantitative analysis approach.
Example : following mathematical model, which represents
profit:
Profit = Revenue - Expenses
 Profit = Revenue – (Fixed cost + Variable cost)
 Profit = (Selling price per unit)(number of units sold) – [Fixed
cost + (Variable costs per unit)(Number of units sold)]
 Profit = sX – [f + vX]
 Profit = sX – f – vX
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.
where
s = selling price per unit v = variable cost per unit
f = fixed cost X = number of units sold

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20
Profits = sX – f – vX
The company buys, sells, and repairs old clocks. Rebuilt springs sell for
$20 per unit. Fixed cost of equipment to build springs is $2,000. Variable
cost for spring material is $10 per unit.
s = 20 f = 2,000 v = 10
Number of spring sets sold = X
If sales = 0, profits = -f = –$2,000.
If sales = 1,000, profits = [(20)(1,000) – 2,000 – (10)(1,000)]
= $8,000

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0 = sX – f – vX, or 0 = (s – v)X – f
Companies are often interested in the break-even point (BEP). The BEP
is the number of units sold that will result in $0 profit.
Solving for X, we have
f = (s – v)X X =
f
s – v
BEP =
Fixed cost
(Selling price per unit) – (Variable cost per unit)
The BEP for the same example is 200 units

22.  1. Models can accurately represent reality. If properly
formulated, a model can be extremely accurate.
 2. Models can help a decision maker formulate problems. In the
profit model, for example, a decision maker can determine the
important factors or contributors to revenues and expenses.
 3. Models can give us insight and information. For example,
using the profit model from the preceding section, we can see
what impact changes in revenues and expenses will have on
profits.
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23.  4. Models can save time and money in decision making and
problem solving. It usually takes less time, effort, and expense
to analyze a model.
 5. A model may be the only way to solve some large or
complex problems in a timely fashion.
 6. A model can be used to communicate problems and
solutions to others. A decision analyst can share his or her
work with other decision analysts.
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24.  Deterministic models :
Mathematical models that do not involve risk.
◦ All of the values used in the model are known with complete
certainty.
 Probabilistic models:
 Mathematical models that involve risk, chance, or
uncertainty .
◦ Values used in the model are estimates based on probabilities.
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25.  POM-QM for Windows
Easy to use decision support system for use in POM and QM
courses
 This is the main menu of quantitative models
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Break Even Point ( calculations )

27. 1- Defining the problem
◦ The Identification of problem may not be easily.
◦ There may be viewpoints conflict.
◦ There an impact on other departments.
◦ The solution outdated.
◦ 2- Developing a model
◦ There is a trade-off between complexity and ease of understanding.
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28. End of chapter
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