Susan Blouin, Business Analytical Tools Business Management
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Transcript of Susan Blouin, Business Analytical Tools Business Management
SEB.10.11. (01)
Analytical Tools
Business Statistics Study
October 11, 2001
Susan Blouin
ATHABASCA UNITVERISTY, Centre for Innovative Management – MBA Program, Analytical Tools Final Project
PART I ANALYSIS: Quantitative and Qualitative Statistical Business Analysis:
European Management Consultancy Firms
PART II ANALYSIS: Quantitative Assessment: Statistical Summary
A. Stem and Leaf Integers as Stems
B. Probability Theory Analysis
C. Population Parameters Estimating and Confidence Intervals
D. Correlation Analysis
Page 1.
PART I ANALYSIS
Quantitative and Qualitative Statistical Business Analysis:
European Management Consultancy Firms
INTRODUCTION:
The management consultancy landscape is a promising industry for European
management consultancy firms deploying their services across the European
marketplace. Consolidation in the European management consultancy field shows to
be in a positive position for business end users as the consultancy firms have a strong
core expertise in areas such as information technology, human resources, operations
and strategy. Business end users are at an advantage through the use of a consultancy
firms that can provide a variety of expertise in business management.
This report examines and provides various key strategic observations based on the
collection of quantitative data. Businesses and the general economy manage
processes for influencing quantitative data and rely on statistics and analysis as a
fundamental part of their business development. Sr. Operating Teams of
organizations rely on data results to make critical decisions about company direction
or strategic initiative. Statistics has sometimes caused a misconception in the display
of data. The importance that statistics has is beyond the results of the data. It’s what
the data tells us that is key in making management decisions and in analyzing current
trends and situational objectives. This analysis is focused on the data results of
European management consultant firms and provides an evaluation that draws
conclusions about its current global business, through the summary and interpretation
of graphical data.
Page 2.
STATISTICAL BUSINESS ANALYSIS:
Percentage Breakdown of the Turnover generated by European Management
Consultancy Firms
Figure 1 is a pie graph that shows the percentage breakdown of the revenue
generated by European management consultancy firms Internationally. The
classifications for Western Europe, Eastern Europe and Rest of World represent
qualitative variables that show a quantitative observation on the percentage of
revenue generation. European management consultancy firms show a primary focus
in Western Europe. There is very little business in Eastern Europe and Rest of
World. For example, European management consultancy firms generate 87%
revenue in Western Europe, 5% generation of revenue in Eastern Europe and 8%
revenue generation throughout the rest of the world.
Figure 1
Page 3.
Market by Industry Sector Analysis: Strategy for Market Penetration
The market by industry sector analysis shows the deployment of business
development relative to industry focus by European management consultancy
firms in the European marketplace.
The communications and business services share similar focus of market
penetration, where the penetration of communications is 12.1% and business
services 11.1% of European management consultancy firms business focus. The
primary focus is in financial services and insurance, which represents 24.7% of
management consultancy business. Subsequent to its primary focus, is
manufacturing which represents 21.2% of management consultancy business.
Collectively, the manufacturing and the financial services and insurance
industries make up almost half of the management consultancy business in the
industry sector.
Figure 2
Page 4.
The percentage of Market Penetration by Region
The findings in figure 3 indicate that both Germany and the UK provide the
greatest percentage of management consultancy. However, it does not
conclusively indicate that a greater percentage of revenue is derived from these
regions. Additional information would be required in order to conclude
statements on global spend. Figure 3 shows a market by region that is spread
unusually thin. Its shows consultancy firms operating in many markets.
Management consultancy firms could lower their distribution in the areas that
have a smaller percentage of business in order to strengthen the more economical
markets similar to Germany and the U.K. However this is not limited to the
economical growth of France which represents 8.9% of management consultancy
and Nordic at 8%. Both of these regions have more of a demand than Eastern
Europe, Belgium, Austria, Swiss, Spain, Portugal, Netherlands, Greece and the
percentage shown for other. However if European management consultancy
firms continue to grow and provide new initiatives, the smaller percentage
market regions could be a vital part of their business growth and stability.
There is a demand fore European management consultancy firms in Germany
where 32% is representative of the market and in the U.K. 27.2%. Germany has a
powerful economy and provides a need for outside business consulting. The
effect of European management consulting shows a critical economic
requirement for Germany and the U.K. This could be an indication of a good
economy or it could suggest that it is strong since European management
consultancy firms could be driving the economy of both Germany and the U.K.
The percentage of the management consultancy market demands of the four
highest regions, Germany, U.K. France and Nordic, suggests that there is more Page 5.
enlightened management requirements for outside consulting which therefore
increases economic spending.
Figure 3
Market Breakdown by Key Consulting Areas
European management consultancy firms are showing to have an established set
of services in Human Resource Management, Strategy Planning, Information
Technology and Operations. The findings of Figure 4 indicate that Information
Technology is the primary strategic focus, representing 44% of consulting
services based on the aggregated findings of the 4 key consulting areas. Second,
Strategy Planning shows a 27% strategic focus, followed by Operations at 23%
and Human Resources Management at 6%.
Page 6.
Figure 4
The data results of both IT and Operations could show an indication of overlap.
IT consulting is known to bring in technology such as business application tools
or a network expansion with varying degrees of new technology. The statistics
may possibly be unclear due to the role operations plays in operationalizing new
technology. A company’s new IT application development could also cause the
development of operational techniques, process development and operations
management. Recent IT networks that consultant firms formulate for a business
plan, could require new provisioning planing activities within operations. A more
detailed analysis would need to be provided in order to develop more clear
definitions that scope out the elements of IT versus Operations to prevent
vagueness in the statistics.
Traditionally, companies have handled HR internally and have relied very little
on outside consulting services. The percentage of European management
consultancy firms providing Human Resources Management consulting is shown
Page 7.
to be 6% of the 4 key consulting areas. Although this number appears low,
businesses are continuing to rely on outside consulting for Human Resources
planning. It could possibly become a key area of growth that evolves in the
European business market. Strategic planning is seen as a critical business skill
required by the European business markets. It shows that outside knowledge is
appropriately required for business firms, and that the European market finds
outside knowledge to be essential and useful in providing management
techniques in consulting.
CONCLUSION:
European management consultancy firms are best positioned in the European
market. Possible expansion towards Eastern Europe would be more adaptable for
business opportunities than in the rest of the world, since the European
consultancy firms have more than likely established practices that are related to
the European market. Expansion in Eastern Europe represents a good opportunity
for continuous emergence of outsourced services for consulting practices where
these practices could be transferable and remodeled. The rest of world presents
unclear statistics as to the different global areas. Although it only has 8% of
European consultancy efforts, many European firms have a greater opportunity to
expand their practice globally. Germany and the U.K. represent a long-term
demand for outside firms to engage in consulting. These two regions are
economically stable in by means of the management consultant firms. Companies
will continue to develop in Europe as the world economy continues to evolve.
European management consultancy firms will require evolving their business
however; they will need to pose some concern on what the impact of the global
economy might have on their business.
Page 8.
PART II ANALYSIS Quantitative Assessment: Statistical Summary on Business Examples
1. Stem and Leaf “Integers as Stems”
The price earning ratios for 21 stocks in the retail trade category are:
8.3 9.6 9.5 9.1 8.8 11.2 7.710.1 9.9 10.8 10.2 8.0 8.4 8.111.6 9.6 8.8 8.0 10.4 9.8 9.2
The following analysis will show the above information organized into a stem-and-leaf display. The analysis will also show the following principles:
(a) Values that are there less than 9.0 (b) A list of values in the 10.0 up to 10.9 category (c) The middle value (d) What the largest and the smallest price-earnings ratio are
Stem and Leaf Display:
The following table displays the above information into an organized stem and leaf display.
STEM LEAF
7 .7
8 .0,.0,.1,.3,.4,.8,.8
9 .1,.2,.5,.6,.8,.9
10 .1,.2,.4,.8
11 .2,.6
The above data has only one value that is less than 9.0. That value is 8.
The list of values in the 10.0 up to 10.9 category are 10.1, 10.2, 10.4 and 10.8 respectively.
Page 9.
The numeric middle value that is also the 11th or median value in the data set, is 9.5. The smallest
price earnings shown in the data set is 7.7 and the largest is 11.6.
2. Probability Theory Analysis
Routine physical examinations are conducted annually as part of a health program for General Cement employees. It was discovered that 8 percent of the employees needed corrective shoes, 15 percent need major dental work, and 3 percent need both corrective shoes and major dental work.
The following analysis will show probability methods to justify the following measures:(a) The probability that an employee selected at random will need either corrective shoes or
major dental work (b) Venn Diagram.
(a) The probability that an employee selected at random will need either corrective shoes or
major dental work
The following formulas are described to outline the probability theory that an employee selected
at random, where “P” represents a random event. These methods are used to calculate the
probability of a particular outcome happening (the probability of an employee selected at random
needing either shoes or major dental work), and represents a ratio of the number of times it can
happen to the total number of possible outcomes. The probability is expressed as a fraction (or
decimal) and is between zero and one. These formulas are considered for one event occurring.
P (need corrective shoes) = 0.08
P (need major dental work) = 0.15
P (need shoes and dental work) = 0.03
Page 10.
In order to find the probability that an employee is selected at random for either corrective shoes
or major dental work, the following formula is used:
P (need shoes and dental work)
= P (need shoes) + P (need dental work) – P (shoes and dental work)
= 0.08 + 0.15 – 0.03
= 0.2
Therefore, the probability factor is 0.2 that an employee is selected at random for either corrective
shoes or major dental work.
(b) Venn Diagram.
The following illustration is a Venn diagram that shows the unison of two events, A and B. The
surrounding area of the circles represent the sample space. Circle A represents “corrective shoes”
and circle B represents “ the need for major dental work”. The intersection of A and B (shown as
A and B), represents the event that both A and B occur.
The two full circles represent the “union”. Figure 5 – Venn Diagram
Page 11.
P (shoes dental) = P (shoes) + P (dental) – P (shoes dental)
Need Dental Work
Need Shoes 0.08
Need Shoes and Dental Work 0.3= P ()
Sample Space
3. Population Parameters Estimating and Confidence Intervals
The wildlife department is feeding a special food to rainbow trout fingerlings in a pond. The sample of weights of 40 trout revealed that the sample mean is 402.7 grams and the sample standard deviation 8.8 grams.
The following analysis will show the following principals:
(a) The estimated mean weight of the population and its naming convention;(b) The 99% confidence interval; (c) The 99% confidence limits; (d) The degree of confidence being used; (e) Review on findings.
a. The estimated mean weight of the population and its naming convention
We are giving the following details in the study. Therefore let n= 40 , , = 402.7 grams
(sample mean), and S = 8.8 grams (sample standard deviation).
The estimate of the population mean is always the sample mean. Therefore the estimated
mean weight is shown as: û = = 402.7 grams.
b. The 99% confidence interval
If we took 100 samples, by this technique 99 out of 100 would capture the true mean 99% of
the time. A 99% confidence interval implies that the confidence coefficient = .01 and the
confidence coefficient divided by 2 = .005. The first measure is in finding the alpha over 2
(the confidence coefficient). The confidence coefficient line item and the Z score once found
in the Normal Curve Areas table, are used to calculate the confidence interval1
1
1 Terry Sinch. Business Statistics by Example. Fifth Edition (Upper Saddle River, NJ: Prentice Hall, 1996), p. 1198.table 5
Page 12.
Figure 6 Standard Normal Curve
Since the sample size is large there is a 99% confidence interval. What we know in reference to
the formula shows the final confidence intervals:
Page 13.
11 2 3
X= 402.7 (sample mean)S= 8.8 (sample standard. deviation)
S = 40
Confidence coefficient = .01, confid. Coefficient divided by 2 = .005
X + Ζ x S 2
402.7 + or - (2.575)(8.8)
402.7 + or - 3.583
n
40
0 1 2
Normal Curve and Probability Area
99 % Confidence
.495Area under curve
0 1 2 3-1
-2
-3
.005
Z confid. Coefficient / 2 = 2.575
SEH.10.11.(01)
c. The 99% confidence limits
The final 99% confidence limits are (399.117, 406.283). Therefore there is a confidence level
with a true mean 99% of the time, and it is between (399.117, 406.283).
d. The degree of confidence being used
The degree of confidence equals 99%, which is the confidence coefficient less “one” multiplied
by 100%. By using any different level of confidence intervals between 0 and 1, the degree of
confidence can be obtained.
e. Review on findings
Using this technique we can expect to capture the true mean in our confidence interval 99% of the
time.
4. Correlation Analysis
Reliable Furniture is a family business that has been selling to retail customers in the Chicago area for many years. They advertise extensively on radio and TV, emphasizing their low prices and easy credit terms. The owner would like to review the relationship between sales and the amount spent on advertising. Below is information on sales and advertising expense for the last 4 months.
Month Advertising Expense ($ million) Sales Revenue ($ million)July 2 7
August 1 3September 3 8October 4 10
The following analysis will answer the following key principals:(a) Forecast on sales based on advertising expense – Dependant/Independent Variables(b) Scatter diagram.
Page 14.
(c) Determining the coefficient of correlation. (d) Interpreting the strength of the correlation coefficient.(e) Determining the coefficient of determination with Interpretation.
A. Forecast on sales based on advertising expense – Dependant/Independent Variables
In order for the owner to forecast sales based on advertising expense, the dependant variable
and independent variable are required. The dependant variable is “sales”. The independent
variable is “advertising”.
b. Scatter diagram
The following diagram shows the scattergram of advertising expense as X, for the dependant
sales revenue.
Figure 7, Scattergram
Page 15.
C. Determining the coefficient of correlation
The following Sum of Squares table was used to gather the data for the formulas:
X Y X Squared Y Squared XY2 7 4 49 141 3 1 9 33 8 9 64 244 10 16 100 40
10X
28y
30x2
222y2
81Xy
Formula for coefficient of correlation:
2
2
Page 16.
SSxx SSyy
SSxy
R =
= xy - ( x) (y) n [x - (x) ][ y - (y) ] 2
n n
2 2 2
2
= 81 - (10) (28) 4
[ 30 - (10 ) ] [ 222 - (28 ) 2
44
= 81 - 70
(5) (26)
R = 0.9647
=11
130
D. Interpreting the strength of the correlation coefficient.
The results of the above coefficient of correlation show a strong linear relationship between sales
and advertising expense.
E. Determining the coefficient of determination with Interpretation
R = (.9647) = .93062 2
R squared is the percentage of the variability in sales that can be explained by the explanatory
variable advertising expense.
Analytical Tools, Business Statistics Study October 11, 2001S. Blouin
Page 17.