Remarks from the professor on milestone 1 and for milestone 2A.docx

Remarks from the professor on milestone 1 and for milestone 2
A good start on the paper. Please look at the introduction
again. It does not match the case problem or what the issues of
the company are. If you look at the first paragraph it does not
lead into the next paragraph and the facts and flow are
disjointed.
I read through the paper and it is a series of unjoined statements
that don't really flow into a research work. You do have the
correct issue that monthly variance in demand is the key to
solving the problem but the supporting work for this statement
is not there.
The next step for milestone 2 is to analyze the descriptive
statistics, ANOVA and correlation/regression asked for in the
case. From that you should see how to develop a model to
correct the forecasting problem. Make sure your Milestone 2,
gives a model that solves the problem.
QSO 510 Milestone Two Guidelines and Rubric
The final project for this course is the creation of a statistical
analysis report. Operations management professionals are often
relied upon to make decisions
regarding operational processes. Those who utilize a data-
driven, structured approach have a clear advantage over those
offering decisions based solely on
intuition. You will be provided with a scenario often
encountered by an operations manager. Your task is to review

the “A-Cat Corp.: Forecasting” scenario, the
addendum, and the accompanying data in the case scenario and
addendum.
In Module Seven, you will submit your selection of statistical
tools and data analysis, which are critical elements III and IV.
You will submit a 3- to 4-page paper
and a spreadsheet that provides justification for the appropriate
statistical tools needed to analyze the company’s data, a
hypothesis, the results of your analysis,
any inferences from your hypothesis test, and a forecasting
model that addresses the company’s problem.
Specifically, the following critical elements must be addressed:
III. Identify statistical tools and methods to collect data:
A. Identify the appropriate family of statistical tools that you
will use to perform your analysis. What are your statistical
assumptions concerning
the data that led you to selecting this family of tools? In other
words, why did you select this family of tools for statistical
analysis?
B. Determine the category of the provided data in the given case
study. Be sure to justify why the data fits into this category
type. What is the
relationship between the type of data and the tools?
C. From the identified family of statistical tools, select the most
appropriate tool(s) for analyzing the data provided in the given
case study.
D. Justify why you chose this tool to analyze the data. Be sure
to include how this tool will help predict the use of the data in
driving decisions.
E. Describe the quantitative method that will best inform data-

driven decisions. Be sure to include how this method will point
out the relationships
between the data. How will this method allow for the most
reliable data?
IV. Analyze data to determine the appropriate decision for the
identified problem:
A. Outline the process needed to utilize your statistical analysis
to reach a decision regarding the given problem.
B. Explain how following this process leads to valid, data-
driven decisions. In other words, why is following your outlined
process important?
C. After analyzing the data sets in the case study, describe the
reliability of the results. Be sure to include how you know
whether the results are
reliable.
D. Illustrate a data-driven decision that addresses the given
problem. How does your decision address the given problem?
How will it result in
operational improvement?
Guidelines for Submission: Your paper must be submitted as a
3- to 4-page Microsoft Word document and attached spreadsheet
with double spacing, 12-point
Times New Roman font, one-inch margins, and at least six
sources cited in APA format.
Instructor Feedback: This activity uses an integrated rubric in
Blackboard. Students can view instructor feedback in the Grade
Center. For more information,
review these instructions.

http://snhu-
media.snhu.edu/files/course_repository/graduate/qso/qso510/qso
510_final_project_case_addendum.pdf
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http://snhu-
media.snhu.edu/files/production_documentation/formatting/rubr
ic_feedback_instructions_student.pdf
Rubric
Critical Elements Exemplary Proficient Needs Improvement
Not Evident Value
Statistical Tools and
Methods: Family of
Statistical Tools
Meets “Proficient” criteria and
identification demonstrates
nuanced understanding of
statistical tools (100%)
Identifies the appropriate
family of statistical tools used
to perform statistical analysis,
including statistical
assumptions (90%)
Identifies a statistical family of
tools used to perform
statistical analysis but either
the tools are not the most

appropriate to use or
discussion lacks statistical
assumptions (70%)
Does not determine a family
of statistical tools (0%)
7
Methods: Category
of Provided Data
demonstrates insight into the
relationship of categorical
data and statistical tools
(100%)
Determines the category of
the provided data, including
justification to support claims
(90%)
Determines the category of
the provided data but
category is either inaccurate
or discussion lacks justification
to support claims (70%)
Does not determine a
category for the data (0%)
7

Methods: Most
Appropriate Tool
Selects the most appropriate
statistical tool used to analyze
the data (100%)
Selects a statistical tool but
selection is not the most
appropriate given the data
(70%)
Does not select a tool to be
used for analysis (0%)
7
Methods: Justify Tool
justification demonstrates
insight into the relationship
between statistical tools and
type of data (100%)
Justifies why the tool chosen
is the most appropriate for
analysis of this data (90%)
Justifies why the tool chosen
is the most appropriate for the
analysis but justification is
either illogical or cursory

(70%)
Does not justify why a
particular tool was chosen
(0%)
7
Methods:
Quantitative Method
description demonstrates
insight into the relationship
between the quantitative
method and data relationships
(100%)
Describes the quantitative
method that will best inform
the decision, including how
this method will point out the
relationships between the
data (90%)
method but either the
method selected will not
result in the most reliable data
or discussion lacks how the
method will point out the
relationships between the
data (70%)

Does not describe the
quantitative method (0%)
7
Analyze Data:
Process
offers great detail for each
identified step (100%)
Outlines the process needed
to utilize the statistical
analysis (90%)
Outlines the process needed
to utilize the statistical
analysis but steps are either
inappropriate or
overgeneralized (70%)
Does not outline the process
needed to utilize the statistical
analysis (0%)
15
Analyze Data: Valid,
Data-Driven
Decisions

explanation demonstrates a
how following a process will
lead to a valid decision (100%)
Explains how following the
outlined process leads to a
valid data-driven decision
(90%)
outlined process leads to a
valid decision but explanation
is inappropriate or cursory
(70%)
Does not offer an explanation
why following the outlined
process leads to a valid
decision (0%)
15
Analyze Data:
Reliability of Results
keen insight into identifying
reliable data (100%)
Describes the reliability of the
results based on data sets,
including a justification to
support claims (90%)

results but description is
either cursory or lacks
(70%)
reliability of the results (0%)
15
Analyze Data: Data-
Driven Decision
illustration demonstrates a
deep understanding of the
interplay between a problem,
the operation, and operational
improvement (100%)
Illustrates a data-driven
decision that addresses the
problem and operational
improvement (90%)
Illustrates a data-driven
decision that addresses the
problem but illustration is
either inappropriate or
Does not illustrate a decision
that addresses the problem
(0%)

15
Articulation of
Response
Submission is free of errors
related to citations, grammar,
spelling, syntax, and
organization and is presented
in a professional and easy to
read format (100%)
Submission has no major
errors related to citations,
grammar, spelling, syntax, or
organization (90%)
Submission has major errors
spelling, syntax, or
organization that negatively
impact readability and
articulation of main ideas
(70%)
Submission has critical errors
spelling, syntax, or
organization that prevent
understanding of ideas (0%)
5
Earned Total 100%

QSO 510 Milestone One Guidelines and Rubric
analysis report. Operations management professionals are often
relied upon to make decisions
regarding operational processes. Those who utilize a data-
driven, structured approach have a clear advantage over those
offering decisions based solely on
intuition. You will be provided with a scenario often
encountered by an operations manager. Your task is to review
the “A-Cat Corp.: Forecasting” scenario, the
addendum, and the accompanying data in the case scenario and
addendum.
In Module Three, you will submit your introduction and
analysis plan, which are critical elements I and II of the final
project. You will submit a 3- to 4-page paper
that describes the scenario provided in the case study, identifies
quantifiable factors that may affect operational performance,
develops a problem statement,
and proposes a strategy for resolving a company’s problem.
I. Introduction to the problem:
A. Provide a concise description of the scenario that you will be
analyzing. The following questions might help you describe the
scenario: What is

the type of organization identified in the scenario? What is the
organization’s history and problem identified in the scenario?
Who are the key
internal and external stakeholders?
II. Create an analysis plan to guide your analysis and decision
making:
A. Identify any quantifiable factors that may be affecting the
performance of operational processes. Provide a concise
explanation of how these
factors may be affecting the operational processes.
B. Develop a problem statement that addresses the given
problem in the scenario and contains quantifiable measures.
C. Propose a strategy that addresses the problem of the
organization in the given case study and seeks to improve
sustainable operational
processes. How will adjustments be identified and made?
Guidelines for Submission: Your paper must be submitted as a
3- to 4-page Microsoft Word document with double spacing, 12-
point Times New Roman font,
one-inch margins. Sources should be cited according to APA
style.
http://snhu-

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Rubric
Critical Elements Exemplary (100%) Proficient (90%) Needs
Improvement (70%) Not Evident (0%) Value
Introduction:
Description of the
Scenario
insightful understanding of the
situation described in the
scenario
Concisely and accurately
describes the scenario
Describes the scenario but
description is not concise or
contains inaccuracies
Does not describe the scenario 20
Analysis Plan:
Quantifiable Factors

demonstrates insight into
operational processes and
factors that may affect
performance
Identifies quantifiable factors
that may be affecting the
performance of operational
processes and supports claims
with explanations
processes but identification is
not supported with explanations
or is cursory
Does not identify quantifiable
factors that may be affecting the
processes
30
Analysis Plan: Problem
Statement
statement demonstrates insight
into the relationship between
the quantifiable measures and
problem addressed in the
scenario

Develops a problem statement
appropriate to the scenario that
addresses the given problem and
contains quantifiable measures
addresses the given problem but
statement does not contain
quantifiable measures or is
cursory or inappropriate
Does not develop a problem
statement appropriate to the
scenario that addresses the given
problem
30
Analysis Plan: Strategy Meets “Proficient” criteria and
strategy demonstrates insight
into how the strategy impacts
additional operations
Proposes a strategy that
addresses the problem of the
company and seeks to improve
processes
Proposes a strategy but strategy
either does not address the
problem or does not seek to
improve operational processes

Does not propose a strategy that
company
10
Articulation of
Response
spelling, syntax, and organization
and is presented in a
professional and easy to read
format
Submission has no major errors
spelling, syntax, or organization
that negatively impact
readability and articulation of
main ideas
that prevent understanding of
ideas
10
Earned Total 100%

QSO 510 Final Project Guidelines and Rubric
Overview
analysis report.
Each day, operations management professionals are faced with
multiple decisions affecting various aspects of the operation.
The ability to use data to drive
decisions is an essential skill that is useful in any facet of an
operation. The dynamic environment offers daily challenges that
require the talents of the operations
manager; working in this field is exciting and rewarding.
Throughout the course, you will be engaged in activities that
charge you with making decisions regarding inventory
management, production capacity, product
profitability, equipment effectiveness, and supply chain
management. These are just a few of the challenges encountered
in the field of operations management.
The final activity in this course will provide you with the
opportunity to demonstrate your ability to apply statistical tools
and methods to solve a problem in a
given scenario that is often encountered by an operations
manager. Once you have outlined your analysis strategy and

analyzed your data, you will then report
your data, strategy, and overall decision that addresses the
given problem.
The project is divided into two milestones, which will be
submitted at various points throughout the course to scaffold
learning and ensure quality final
submissions. These milestones will be submitted in Modules
Three and Seven. The final project is due in Module Nine.
In this assignment, you will demonstrate your mastery of the
following course outcomes:
-based strategies in guiding a focused approach for
improving operational processes
atistical methods for informing
valid data-driven decision making in professional settings
-driven decision
making resulting in sustainable operational processes
-driven decision making
for fostering continuous improvement activities
internal and external stakeholders based on relevant data
Prompt

Operations management professionals are often relied upon to
make decisions regarding operational processes. Those who
utilize a data-driven, structured
approach have a clear advantage over those offering decisions
based solely on intuition. You will be provided with a scenario
often encountered by an operations
manager. Your task is to review the “A-Cat Corp.: Forecasting”
scenario, the addendum, and the accompanying data in the case
scenario and addendum; outline
the appropriate analysis strategy; select a suitable statistical
tool; and use data analysis to ultimately drive the decision.
Once this has been completed, you will
be challenged to present your data, data analysis strategy, and
overall decision in a concise report, justifying your analysis.
I. Introduction to the problem:
A. Provide a concise description of the scenario that you will be
analyzing. The following questions might help you describe the
scenario: What is
the type of organization identified in the scenario? What is the
organization’s history and problem identified in the scenario?
Who are the key
internal and external stakeholders?
II. Create an analysis plan to guide your analysis and decision
making:
A. Identify any quantifiable factors that may be affecting the
performance of operational processes. Provide a concise
explanation of how these
factors may be affecting the operational processes.

B. Develop a problem statement that addresses the given
problem in the scenario and contains quantifiable measures.
C. Propose a strategy that addresses the problem of the
organization in the given case study and seeks to improve
processes. How will adjustments be identified and made?
III. Identify statistical tools and methods to collect data:
A. Identify the appropriate family of statistical tools that you
will use to perform your analysis. What are your statistical
assumptions concerning
the data that led you to selecting this family of tools? In other
words, why did you select this family of tools for statistical
analysis?
B. Determine the category of the provided data in the given case
study. Be sure to justify why the data fits into this category
type. What is the
relationship between the type of data and the tools?
C. From the identified family of statistical tools, select the most
appropriate tool(s) for analyzing the data provided in the given
case study.
D. Justify why you chose this tool to analyze the data. Be sure
to include how this tool will help predict the use of the data in
driving decisions.
E. Describe the quantitative method that will best inform data-
driven decisions. Be sure to include how this method will point
out the relationships
between the data. How will this method allow for the most
reliable data?

IV. Analyze data to determine the appropriate decision for the
identified problem:
A. Outline the process needed to utilize your statistical analysis
to reach a decision regarding the given problem.
B. Explain how following this process leads to valid, data-
driven decisions. In other words, why is following your outlined
process important?
http://snhu-
C. After analyzing the data sets in the case study, describe the
reliability of the results. Be sure to include how you know
whether the results are
reliable.
D. Illustrate a data-driven decision that addresses the given
problem. How does your decision address the given problem?
How will it result in
operational improvement?
V. Recommend operational improvements to stakeholders:
A. Summarize your analysis plan for both internal and external
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.
Milestones
Milestone One: Introduction and Analysis Plan
In Module Three, you will submit your introduction and
analysis plan, which are critical elements I and II. You will
submit a 3- to 4-page paper that describes the
scenario provided in the case study, identifies quantifiable
factors that may affect operational performance, develops a
problem statement, and proposes a
strategy for resolving a company’s problem. This milestone will
be graded with the Module One Rubric.
Milestone Two: Statistical Tools and Data Analysis
In Module Seven, you will submit your selection of statistical
tools and data analysis, which are critical elements III and IV.
You will submit a 3- to 4-page paper
and a spreadsheet that provides justification of the appropriate
statistical tools that are needed to analyze the company’s data, a
hypothesis, the results of your
analysis, any inferences from your hypothesis test, and a
forecasting model that addresses the company’s problem. This
milestone will be graded with the
Module Two Rubric.
Final Project Submission: Statistical Analysis Report
In Module Nine, you will 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. It should reflect the

incorporation of feedback gained throughout the course. This
submission will be graded with the
Final Project Rubric.
Final Project Rubric
Guidelines for Submission: Your statistical analysis report must
be 10–12 pages in length (plus a cover page and references) and
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.
Critical Elements Exemplary Proficient Needs Improvement
Not Evident Value
Introduction:
Description of the
Scenario
insightful understanding of the
situation described in the
scenario (100%)

Concisely and accurately
describes the scenario (90%)
Describes the scenario but
description is not concise or
contains inaccuracies (70%)
Does not describe the scenario
(0%)
4.05
Analysis Plan:
Quantifiable Factors
demonstrates insight into
operational processes and factors
that may affect performance
(100%)
processes and supports claims
with explanations (90%)
processes but identification is not
supported with explanations or is
cursory (70%)
Does not identify quantifiable

factors that may be affecting the
processes (0%)
6.13
Analysis Plan: Problem
Statement
statement demonstrates insight
into the relationship between the
quantifiable measures and
problem addressed in the
scenario (100%)
addresses the given problem and
contains quantifiable measures
(90%)
addresses the given problem but
statement does not contain
quantifiable measures or is
cursory or inappropriate (70%)
Does not develop a problem
statement appropriate to the
scenario that addresses the given
problem (0%)
6.13

Analysis Plan: Strategy
strategy demonstrates insight
into how the strategy impacts
additional operations (100%)
Proposes a strategy that
company and seeks to improve
sustainable operational processes
(90%)
Proposes a strategy but strategy
either does not address the
problem or does not seek to
improve operational processes
(70%)
Does not propose a strategy that
company (0%)
6.13
Methods: Family of
Statistical Tools
identification demonstrates

Identifies the appropriate family
of statistical tools used to
perform statistical analysis,
including statistical assumptions
(90%)
Identifies a statistical family of
tools used to perform statistical
analysis but either the tools are
not the most appropriate to use
or discussion lacks statistical
assumptions (70%)
Does not determine a family of
6.13
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Methods: Category of
Provided Data
demonstrates insight into the
relationship of categorical data
and statistical tools (100%)

Determines the category of the
provided data, including
(90%)
Determines the category of the
provided data but category is
either inaccurate or discussion
lacks justification to support
claims (70%)
Does not determine a category
for the data (0%)
6.13
Methods: Most
Appropriate Tool
Selects the most appropriate
statistical tool used to analyze the
data (100%)
Selects a statistical tool but
selection is not the most
appropriate given the data (70%)
Does not select a tool to be used
for analysis (0%)
6.13

Methods: Justify Tool
justification demonstrates insight
into the relationship between
statistical tools and the type of
data (100%)
Justifies why the tool chosen is
the most appropriate for analysis
of this data (90%)
Justifies why the tool chosen is
the most appropriate for the
analysis but justification is either
illogical or cursory (70%)
Does not justify why a particular
tool was chosen (0%)
6.13
Methods: Quantitative
Method
description demonstrates insight
into the relationship between the
quantitative method and data
relationships (100%)

method that will best inform the
decision, including how this
method will point out the
relationships between the data
(90%)
method but either the method
selected will not result in the
most reliable data or discussion
lacks how the method will point
out the relationships between
the data (70%)
quantitative method (0%)
6.13
Analyze Data: Process
offers great detail for each
identified step (100%)
Outlines the process needed to
utilize the statistical analysis
(90%)
Outlines the process needed to
utilize the statistical analysis but
steps are either inappropriate or

Does not outline the process
needed to utilize the statistical
analysis (0%)
6.13
Analyze Data: Valid,
Data-Driven Decisions
explanation demonstrates a
nuanced understanding of how
following a process will lead to a
valid decision(100%)
outlined process leads to a valid
data-driven decision (90%)
outlined process leads to a valid
decision but explanation is
inappropriate or cursory (70%)
Does not offer an explanation
why following the outlined
process leads to a valid decision
(0%)
6.13
Analyze Data:
Reliability of Results

description demonstrates keen
insight into identifying reliable
data (100%)
results based on data sets,
including a justification to
results but description is either
cursory or lacks justification to
Does not describe the reliability
of the results (0%)
6.13
Analyze Data: Data-
Driven Decision
illustration demonstrates a deep
understanding of the interplay
between a problem, the
operation, and operational
improvement (100%)
Illustrates a data-driven decision
that addresses the problem and
operational improvement (90%)
Illustrates a data-driven decision

that addresses the problem but
illustration is either inappropriate
or overgeneralized (70%)
Does not illustrate a decision that
addresses the problem (0%)
6.13
Recommend
Operational
Improvements:
Analysis Plan
summary demonstrates keen
insight into appropriately
communicating an analysis plan
to stakeholders (100%)
Summarizes analysis plan for
internal and external
stakeholders using audience-
appropriate jargon (90%)
Summarizes analysis plan for
internal and external
stakeholders but summary either
inappropriately uses jargon or is
cursory (70%)

Does not summarize the analysis
plan for stakeholders (0%)
6.13
Recommend
Operational
Improvements:
Decision
explanation demonstrates keen
insight into appropriately
communicating a decision and
how it was reached to
stakeholders (100%)
Explains the decision for the
problem and how that decision
was reached, using audience-
Explains the decision for the
problem but explanation either
lacks how the decision was
reached or uses inappropriate
jargon (70%)
Does not explain decision for the
problem (0%)
6.13
Recommend

Operational
Improvements:
Best Option
justification demonstrates keen
insight as to why the decision is
valid and why it is the optimal
solution, using audience-
Justifies why the decision is the
best option for addressing the
problem and how it will result in
operational improvement, using
audience-appropriate jargon
(90%)
Justifies why the decision is the
best option but justification lacks
how it will result in operational
improvement, is cursory, or uses
inappropriate jargon (70%)
Does not justify to stakeholders
that the decision is the best
option (0%)
6.13
Articulation of
Response

spelling, syntax, and organization
and is presented in a professional
and easy to read format (100%)
Submission has no major errors
(90%)
that negatively impact readability
and articulation of main ideas
(70%)
that prevent understanding of
ideas (0%)
4
Earned Total 100%
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 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
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
(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
4972 2814
5411 2874
7678 3774
5774 3247

6007 3107
6290 2776
8332 3571
6107 3354
6729 3513
Running Head: Introduction and Quantitative Analysis Plan
1
Introduction and Quantitative Analysis Plan
6
Introduction and Quantitative Analysis Plan
Robert Shulzinsky:
SNHU:
Quanitative Analysis:
July 6 2017
Introduction
A cat company is a technological company which deals with
gadgets that vary with the level of technology and more
especially products that need consistent quality research to
improve their quality. These type of products gives a greater
demand for the company to have a quality control mechanism
that will help develop the quality of the company products. The

riskiest stand such an organization can do to ignore the quality
enhancement aspect of the products. Moreover, the management
system of the company depends on the information that is a
given from the operation l functional area of the firm such as
sales, production and research departments. The sensitive part
of this type of an organization relies on improving its products
and ensuring that the products are not wasted, they meet the
demand in the market and above all, they are of customer
quality specifications.
Moreover, the organization deals with the different level of
workers with a different understanding. Any form of decision
making information should be prepared in a simple way that is
easily understood to enhance understanding between different
parties that will use the information. For instance, the demand
for the generators produced by the company varies with the
season. For the last few years, the demand for the refrigerators
and transformers of the company has consistently increased.
The increasing trend provides a viable forecasting solution of
the future demand for its products. Therefore, this organization
will consider seasonal demand, past increasing trend, the period
in a year and the consistency of increase in its operations as
provided in the historical data of the company.
Analysis plan
A. Quantifiable Factors
1. Sales
Sales level of the company is different in different periods of
the month. The company can record its sales demand; it can
give the record of every sale done by the company which will
give the basis to determine the likely current sales and also the
future sales of the company. Future forecasting of the company
sales will depend on the past data of how the company has been

selling its products in the market. The operation process result
is to produce a quality product that will be sold. If the operation
process of the company is not efficient, the number of products
that are produced will reduce which will result to reduced level
of sales since will be no products to sell
2. Demand
The demand involves the requirement level of the company
products. The company transformer demand as outlined was 745
requirements, and for the last few years, the requirement is
expected to increase up to 1000. This future forecasting is
intuitive based on simple observation. Tom establish the exact
value of the increase of decrease of the company requirement, t
will need the calculation of the trend of increase in
consideration of the aspects that entail seasonal changes and
quality standards. The demand calls for the urgency and needs
for the operation process. The demand is the reason for the sales
of the company and hence influences the sales. Also, demand
can be of the product.
3. Quality requirements
The quality system of the company goes a measure that
determines the quality standards of the company product. This
can be quantified through outlining all the aspects that consist
of a quality product. The product will be evaluated and
quantified in an expressed percentage of the level of quality
standards that it has. Through such, the management can be able
to understand that the product has met the requirement as
provided by the quality levels set by the management. The
essence of fighting to have an effective process is to enhance
the quality of products. A production process is developed to
meet all the quality requirement, and hence there will be no
production method if the quality is not considered.

B. Problem Statement.
Operation prerequisite of the organization needs monitoring to
keep the quality standards of the product. There is also needed
requirement of the process to enhance the production of the
product required. Likewise, it is needed a forecast of the
number of products that should be produced in the future. If the
company struggles to keep the quality of the products,
establishing satisfaction on the requirement side will be
difficult. And also, if the process is enhanced with the
associated risk, what is the future sales in the market? The
worry is whether the production will continue to increase in the
future and also whether there demand of the products in the
market will be quenched.
C. Strategy to Address the Problem
Three aspects are involved in the whole processes of the
company. One of the aspects is the season fluctuation in demand
of the company products. The monthly indifferences indicate
these in the sales of the company. Another aspect is meeting the
future demand through enhancing quality and establishing
quality control system. What the company should do it to
analyze the historical data by also considering any permit
changes that the company has made and their influence on the
production. Select a method for analysis which in this case will
involve every aspect of concern in the case. The best method to
use which will utilize the past data for future prediction
includes time series and trend analysis.
References
Chang, J. F. (2016). Business process management systems:
strategy and implementation. CRC
Press.
Pierce, W. C., & Sawyer, D. T. (2013). Quantitative analysis.

John Wiley And Sons, Inc;
London; Toppon Company, Ltd; Japan.

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