Quantitative Analysis

2. Scientific approach to
managerial decision
making whereby raw data
are processed and
manipulated resulting in
meaningful information.

3. Information that may be difficult to quantify but
can affect the decision-making process such as
the weather, state, and federal legislation.
 Employment turnover may be due to law &
order situation
 Several markets trembled due to Osama Bin
Laden death
 Increase in sales due to weather etc.

4. Input Processed Output

5.  Define the Problem
 Develop a model
 Acquire input data
 Develop a solution
 Test a solution
 Analyze the results
 Implement the results

6. Problem Definition:
A clear and concise statement that gives
direction and meaning to the subsequent QA
Steps and requires specific, measurable
objectives.
e.g. Why a certain phenomenon exists?
Employee turnover in HR, Market boom or
fall in Finance, Product/Brand failures in
marketing.

7. …………because true problem causes must be
identified and the relationship of the problem
to other organizational processes must be
considered.

8. Quantitative Analysis Model:
A realistic, solvable, and understandable
mathematical statement showing the relationship
between variables.
Models contain both controllable (decision
variables) and uncontrollable variables and
parameters. Typically, parameters are known
quantities (salary of sales force) while variables are
unknown (sales quantity).

9. Employee turnover was the problem in HR
 Due to work load
 Due to pay level
 Due to working conditions
Brand Failure was the problem in Marketing
 Marketing strategy failure
 Customer Taste
 Timing issues

10. Accurate input data that may come from a
variety of sources such as company reports,
company documents, interviews, on-site
direct measurement, or statistical sampling.
Accurate data will give accurate results and
vice versa.

11. GIGO
Garbage In Garbage Out

12. The best model solution is found by
manipulating the model variables until a
practical and implemental solution is
obtained.
Manipulation can be done by solving the
equation(s), trying various approaches (trial
and error), trying all possible variables
(complete enumeration), and/or
implementing an algorithm (repeating a
series of steps).

13. Model Testing:
The collection of data from a different source to
validate the accuracy and completeness and
sensibility of both the model and model input data
“consistency of results is key!”

14. Results Analysis:
Understanding actions implied by the
solution and their implications, as well
as conducting a sensitivity analysis (a
change to input values or the model) to
evaluate the impact of a change in model
parameters.
Sensitivity analyses allow the “what-
ifs” to be answered.

15. Results Implementation:
The incorporation of the solution
into the company and the monitoring of
the results.