QSO 510 Final Project Case Addendum Vice-president Arun Mittra speculates: We have always estimated how many transformers will be needed to meet demand. The usual method is to look at the sales figures of the last two to three months and also the sales figures of the last two years in the same month. Next make a guess as to how many transformers will be needed. Either we have too many transformers in stock, or there are times when there are not enough to meet our normal production levels. It is a classic case of both understocking and overstocking. Ratnaparkhi, operations head, has been given two charges by Mittra. First, to develop an analysis of the data and present a report with recommendations. Second, “to come up with a report that even a lower grade clerk in stores should be able to fathom and follow.” In an effort to develop a report that is understood by all, Ratnaparkhi decides to provide incremental amounts of information to his operations manager, who is assigned the task of developing the complete analyses. A-Cat Corporation is committed to the pursuit of a robust statistical process control (quality control) program to monitor the quality of its transformers. Ratnaparkhi, aware that the construction of quality control charts depends on means and ranges, provides the following descriptive statistics for 2006 (from Exhibit 1). 2006 Mean 801.1667 Standard Error 24.18766 Median 793 Mode 708 Standard Deviation 83.78851 Sample Variance 7020.515 Kurtosis -1.62662 Skewness 0.122258 Range 221 Minimum 695 Maximum 916 Sum 9614 Count 12 The operations manager is assigned the task of developing descriptive statistics for the remaining years, 2007–2010, that are to be submitted to the quality control department. A-Cat’s president asks Mittra, his vice-president of operations, to provide the sales department with an estimate of the mean number of transformers that are required to produce voltage regulators. Mittra, recalling the product data from 2006, which was the last year he supervised the production line, speculates that the mean number of transformers that are needed is less than 745 transformers. His analysis reveals the following: t = 2.32 p = .9798 This suggests that the mean number of transformers needed is not less than 745 but at least 745 transformers. Given that Mittra uses older (2006) data, his operations manager knows that he substantially underestimates current transformers requirements. She believes that the mean number of transformers required exceeds 1000 transformers and decides to test this using the most recent (2010) data. Initially, the operations manager possessed only data for years 2006 to 2008. However, she strongly believes that the mean number of transformers needed to produce voltage regulators has increased over the three-year period. She performs a one-way analysis of variance (ANOVA) analysis that follows: 2006 2 ...

1. QSO 510 Final Project Case Addendum
Vice-president Arun Mittra speculates:
We have always estimated how many transformers will be
needed to meet demand. The usual method
is to look at the sales figures of the last two to three months and
also the sales figures of the last two
years in the same month. Next make a guess as to how many
transformers will be needed. Either we
have too many transformers in stock, or there are times when
there are not enough to meet our normal
production levels. It is a classic case of both understocking and
overstocking.
Ratnaparkhi, operations head, has been given two charges by
Mittra. First, to develop an analysis of the
data and present a report with recommendations. Second, “to
come up with a report that even a lower
grade clerk in stores should be able to fathom and follow.”
In an effort to develop a report that is understood by all,
Ratnaparkhi decides to provide incremental
amounts of information to his operations manager, who is
assigned the task of developing the complete
analyses.
A-Cat Corporation is committed to the pursuit of a robust
statistical process control (quality control)
program to monitor the quality of its transformers. Ratnaparkhi,
aware that the construction of quality

2. control charts depends on means and ranges, provides the
following descriptive statistics for 2006 (from
Exhibit 1).
2006
Mean 801.1667
Standard Error 24.18766
Median 793
Mode 708
Standard
Deviation 83.78851
Sample Variance 7020.515
Kurtosis -1.62662
Skewness 0.122258
Range 221
Minimum 695
Maximum 916
Sum 9614
Count 12

3. The operations manager is assigned the task of developing
descriptive statistics for the remaining years,
2007–2010, that are to be submitted to the quality control
department.
A-Cat’s president asks Mittra, his vice-president of operations,
to provide the sales department with an
estimate of the mean number of transformers that are required
to produce voltage regulators. Mittra,
recalling the product data from 2006, which was the last year he
supervised the production line,
speculates that the mean number of transformers that are needed
is less than 745 transformers. His
analysis reveals the following:
t = 2.32
p = .9798
This suggests that the mean number of transformers needed is
not less than 745 but at least 745
transformers. Given that Mittra uses older (2006) data, his
operations manager knows that he
substantially underestimates current transformers requirements.
She believes that the mean number of
transformers required exceeds 1000 transformers and decides to
test this using the most recent (2010)
data.
Initially, the operations manager possessed only data for years
2006 to 2008. However, she strongly

4. believes that the mean number of transformers needed to
produce voltage regulators has increased
over the three-year period. She performs a one-way analysis of
variance (ANOVA) analysis that follows:
2006 2007 2008
779 845 857
802 739 881
818 871 937
888 927 1159
898 1133 1072
902 1124 1246
916 1056 1198
708 889 922
695 857 798
708 772 879
716 751 945
784 820 990
Anova: Single Factor

5. SUMMARY
Groups Count Sum Average Variance
2006 12 9614 801.1667 7020.515
2007 12 10784 898.6667 18750.06
2008 12 11884 990.3333 21117.88
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 214772.2 2 107386.1 6.870739 0.003202
3.284918
Within Groups 515773 33 15629.48
Total 730545.2 35
The results (F = 6.871 and p = 0.003202) suggest that indeed
the mean number of transformers has
changed over the period 2006–2008. Mittra has now provided
her with the remaining two years of data

6. (2009 and 2010) and would like to know if the mean number of
transformers required has changed over
the period 2006–2010.
Finally, the operations manager is tasked with developing a
model for forecasting transformer
requirements based on sales of refrigerators. The table below
summarizes sales of refrigerators and
transformer requirements by quarter for the period 2006–2010,
which are extracted from Exhibits 2 and
1 respectively.
Sales of Refrigerators Transformer Requirements
3832 2399
5032 2688
3947 2319
3291 2208
4007 2455
5903 3184
4274 2802
3692 2343
4826 2675
6492 3477
4765 2918

7. 4972 2814
5411 2874
7678 3774
5774 3247
6007 3107
6290 2776
8332 3571
6107 3354
6792 3513
1
Running head: Final Project Milestone Two: Statistical Tools
and Data Analysis
2
Statistical and Data Analysis
QSO 510-Final Project Milestone Two: Statistical Tools and
Data Analysis

8. Tatiane Hatchoua
Southern New Hampshire University
Dr. Tom Griffin
11/15/15
III. A: Statistical family tools
Statistical quality control is an umbrella body that describes the
statistical tools set for data analysis. It is necessary for
evaluating quality. It also offers a way for identifying an error
during inspection (Gupta & Starr, 2014). Also, it helps in
establishing the manufacturing process capability including a
basis for achieving specifications.
B: Data category
Statistical quality control is categorized into descriptive
statistics, acceptance sampling, and statistical process control.
Descriptive statistics describes quality attributes and
relationships (Gupta & Starr, 2014). From the Addendum case
study, these include the mean, median, mode, sum, count,
maximum and minimum values, standard deviation, sample
variance, kurtosis, skewness, and range.
The mean, median, and mode are central tendency measures.
Standard deviation, sample variance, and range measure
dispersion. Standard deviation and variance describe how data
is narrowly or widely spread from the mean while the range
evaluates the spread within an interval (Gupta & Starr, 2014).
The maximum and minimum values indicate the higher and
lower limits for a data set.

9. Kurtosis and skewness are distribution measures and confirms
whether the data set forms a normal distribution (Gupta & Starr,
2014). The standard error shows the sample mean’s closeness
from the true population mean.
Acceptance sampling is the inspection of tested products in a
random manner, followed by making a decision on whether to
reject or accept the samples (Gupta & Starr, 2014). Acceptance
sampling is supported by ANOVA, standard error, P and T value
tests.
ANOVA determines whether the means of various populations
are equal. T- Value establishes the variation between population
means and hypothesized value (Gupta & Starr, 2014). P values
indicate the relationship between the data and the null
hypothesis.
Statistical process control is the inspection of a random outputs
sample from a process and analyzing if the process is generating
products with attributes that lie within the predetermined
specifications. It is used to check for any changes in the
manufacturing process and find out if it is in a controllable state
(Rubin, 2010). A control chart is a method of analyzing a
process. From the case, the maximum and minimum values
reveal the upper and lower control limits during the mean forms
the center line (Gupta & Starr, 2014). These data help in
identifying the assignable and common causes. Regression
analysis can also be used to control a process. It is a forecasting
model. A graphical representation of the transformer
requirements and refrigerators sales data will generate the best
line of fit with the outliers.
C. Appropriate tool for data analysis
Acceptance sampling
D. Reasons for choosing the tool
From the case study, the primary objective of A-Cat
Corporation’s Vice President is to estimate the number of
transformers to be manufactured to fulfill demand. The
company has been forecasting the transformers based on
previous sales figures, leading to overstocking and

10. understocking. The primary task facing the operations manager
is using data to make substantive decisions. Acceptance
sampling will help the manager to decide whether to rely on the
sampled data, from 2006 to 2008 or reject it.
E. Quantitative method
The best method for data analysis is ANOVA. The vice-
president believes that the required number of transformers
should not exceed 745; while the operations manager feels that
they should be more than 1,000. ANOVA tests the equality of
means given more than two groups.
One-way ANOVA tests for significant variations between the
class means. It bases on a normal distribution assumption. It
subdivides the sums of squares into within and between classes.
It also utilizes other formulas such as the degree of freedom, F
statistic, F value, P value; critical F value and comparison tests
(Ostertagová & Ostertag, 2013). This a reflection that ANOVA
is a reliable method while testing a hypothesis as it navigates
through several stages before making an inference.
IV. A: Process outline
The statistical process consists of defining the questions,
establishing measurement priorities, data collection, data
analysis and interpreting the results (Rubin, 2010).
B: Importance of the process
Data analysis is necessary if one has extra data that has to be
sorted out to make a decision. Voluminous information is not
challenging if the actual analysis process is selected. In any
analysis, one must first start by identifying a problem to solve
it. In this case, the problem is determining an estimate of an
average number of transformers that should be manufactured to
avoid idle capacities. Having the measurement priorities will
further help in deciding what to measure, in this case, 2006-
2008 data and how to measure it that is, regarding the mean
number, sequenced by collecting the required data. Data
analysis using statistical control tools will offer results that will
later be interpreted to arrive at the final decision.
C. Data analysis and reliability

11. From the analysis using Microsoft Excel, the mean, mode and
median are 801, 708 and 793 respectively, reflecting a positive
skew and a negative Kurtosis. This means that the data does not
present a standard distribution. Thus, the data is not reliable.
Alternatively, the standard deviation is around 84 reflecting a
widespread. This means that 16% of the average number of
transformers lies between 717 and 885. The variance is around
7021, suggesting a wider spread from the average. Thus, the
data should not be relied on.
The standard error shows the sample mean’s closeness to the
population mean. The standard error is around 24. The figure is
not wide. Hence, an implication that there is a close relationship
between the population mean and the sample mean Thus, the
data should be relied on.
Under range, the spread within the interval is 221 while the
maximum and minimum values are 916 and 695 respectively,
hence for any observations that are below or above these limits
assignable cause should be established or rather rejected. The
excel data shows that the transformers should lie between 717
and 885 hence, no relationship between this data, the range,
minimum and maximum value. The data is, therefore,
unreliable.
The P value is around 0.03 and is less than the required 0.05
value. From the data, the f value is more than the critical value.
The null hypothesis should be rejected basing on the f and p-
values. Finally, the f and p-values confirm that the average
number of transformers has changed since 2006, rejecting the
vice president’s claims that the transformers should be less than
745. Forecasting by use of the regression model indicates that
A cat should expect 85.7% transformer demand.
D. Illustration
A corporation’s problem is establishing the required number of
transformers to fulfill demand. The company should expect
around 86% demand, meaning that should not come below 717
and should not exceed 885. The data rectifies the vice
president’s view that the transformers should be less than 745

12. and the operations manager view that it should be more than
745 and surpass 1,000. The null hypothesis (Ho: µ1= µ2= µ3)
should be rejected, and this is supported by the alternative
hypothesis that reveals that the means from 2006 to 2008 are
different. The data will help the company to make the required
number of transformers, avoiding overstocking and under-
stocking in the long run. Quality will also be attained in the
long run as the company will try to maintain the transformers
within the upper and lower limits.
References
Gupta, S., & Starr, M. (2014). Production and operations
management systems. London, CRC Press.
Ostertagová, E., & Ostertag, O. (2013). Methodology and
application of oneway ANOVA. American Journal of
Mechanical Engineering, 1(7), 256-261.
Rubin, A. (2010). Statistics for evidence-based practice and
evaluation. Belmont, Calif: Brooks/Cole.
1
Running Head: A-CAT CORPORATION
A-CAT CORPORATION
2
QSO 510- Final Project Milestone One: Introduction and
Analysis Plan A-CAT CORPORATION

13. Tatiane Hatchoua
Southern New Hampshire University
Dr. Tom Griffin
10/17/15
A-CAT CORPORATION
Introduction
A-CAT Corporation is a manufacturing and distribution
company that mainly deals with domestic electrical appliances.
The company mainly focuses on producing this product for the
price-sensitive rural population. The firm is operated in two
mid-sized facilities. The company has been operational since
1986. The company manufactured a wide range of electrical
appliances such as television signal boosters, transformers, FM
radio kits, electronic ballasts battery chargers and voltage
regulators, their flagship product being the voltage regulator
(Jitendra, 2011).
The case involves a situation that the vice president of the
company faced. The company had always estimated the number
of transformers that will be needed to meet the demand for a
particular period. This was done by determining the sales
figures for the Transformers for the last two or three months
and also by the total sale figures for the last two years in that
particular month. Using that information, the number that would
be needed for that month was then estimated. Using that data,
the company could either manufacture more transformers in
case the current inventory was too low or not manufacture any
in case there is an overstock.
The vice president thus assigns the operations head the
responsibility to develop data and present a report with
recommendation. He should also come up with a report that
even the lowest level of employee can understand.
A stakeholder is someone who has an interest in
something. There internal and external interested parties in a

14. company. Internal stakeholders are groups or people who work
within the business such as employees, owners and investors.
External stakeholders are groups or people outside the business
who do not directly work within the company but are affected
by the decisions made by the corporation. They include
customers, suppliers, creditors, the community, trade unions and
the government.
The key internal stakeholders, in this case, will be the owners,
investors and employees. This is because the work that the
employees do that month will be determined by the number of
transformers that will be needed. The owners and investors also
have a stake in this because their returns in terms of dividends
will be determined by the sales that the company will make. In
case they understock the Transformers, there will be
opportunities that will be lost if the demand is higher than what
they will be supplying. In case they overstock, this will be
locking up capital that could be used to manufacture another
electrical appliance.
The key external stakeholders are the clients and suppliers. If
the company understocks, some customers will lack the
electronic goods for them to buy. For the providers, the data
will determine the amount of materials that will be required for
them thus the revenues that they will be generated.
Operational processes are processed that transform inputs to
outputs. There are various factors that affect operational
processes. They include demand. Demand is the number of
people who are willing and able to buy a product. The demand
levels is a big determinant of the operational processes. The
level of demand determines the number of products that should
be produced. That is the reason the historical data are used in
this case. The historical data can show the level of demand in
the past and help estimate the future (Canada Business Network,
2009).
The other factors could be workforce, factory overhead and
equipment. Workforce could determine the operational
processes. In the absence of key personnel involved in the

15. operational processes for example on sick days. Human insight
during the manufacturing processes is very vital. In the absence
of the key people little or no manufacturing will take place.
Factory overhead is also another factor that could affect
processes. Factory overheads include electricity which some
equipment used in the processes require. In the absence of some
of this overheads, no operations can be done. Equipment is a
major factor in operational processes. This is because they are
used mainly in the manufacturing processes. The equipment
factor can be quantified in terms of hours used. In the case of
machine breakdowns, the manufacturing processes and thus
fewer hours will be put into the manufacturing processes (Vicki,
2015).
A problem statement is a condensed summary of the issues
needed to be addressed by the problem-solving team. The
problem statement, in this case, would be the following. The A-
CAT Corporation should determine the number of transformers
that need to be manufactured in this particular month taking
into the consideration the level of demand, equipment,
workforce and overheads. This will then help determine the
number of voltage regulators and refrigerators that should also
be produced. This should be done so as to help the company
achieve optimum efficiency.
The company has data from 2006. It should use that data to
develop the 2007-2010 data. Using that data, the number of
transformers that were needed in 207-2010 can be determined.
The company can then use that date to estimate the future
demand levels for transformers required to produce the voltage
regulators. This can be done using the one-way analysis of
variance. This method uses the mean numbers for the historical
data to estimate the future demand.
Conclusion
Every company need a basis in their decision making. The A-
CAT Corporation can use that data in their various decision-
making. This allows the managers not to make uninformed

16. decisions.
References
Vicki, A. (2015). What Factors Can Affect the Manufacturing
Process? Retrieved from
http://smallbusiness.chron.com/factors-can-affect-
manufacturing-process-25326.html on October 15, 2015.
Jitendra, R. (2011). Decision-making at A-Cat Corp. Retrieved
from
https://www.iveycases.com/ProductView.aspx?id=51445 on
October 15, 2015.
Canada Business Network, (2009). Measure performance and set
targets. Retrieved from
http://www.infoentrepreneurs.org/en/guides/measure-
performance-and-set-targets/ on October 15, 2015.
Final Project Submission: Statistical Analysis Report
submit your statistical analysis report and recommendations to
management. It should be a complete, polished artifact
containing all of the critical elements of the final product.
Recommend operational improvements to stakeholders:
A. Summarize your analysis plan for both internal and external

17. stakeholders. Be sure to use audience-appropriate jargon when
summarizing for both groups of stakeholders.
B. Explain how your decision addresses the given problem and
how you reached that decision. Be sure to use audience-
appropriate jargon for both groups of stakeholders.
C. Justify why your decision is the best option for addressing
the given problem to both internal and external stakeholders and
how it will result in operational improvement. Be sure to use
audience-appropriate jargon when communicating with
stakeholders.
11 pages (cover page and reference page not included in the
count).
must be written in APA format. Use double spacing, 12-point
Times New Roman font, and one-inch margins. Include at least
six references cited in APA format.