9-2 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. It should reflect the incorporation of feedback gained throughout the course.Note that you will need to refer to the scenario in the article “A-Cat Corp.: Forecasting.” See the syllabus for information on accessing the article.For additional details, please refer to the Final Project Guidelines and Rubric document and the Final Project Case Addendum document.
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QSO 510 Final Project Guidelines and Rubric
Overview
The final project for this course is the creation of a statistical 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:
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Apply data-based strategies in guiding a focused approach for improving operational processes
Determine the appropriate statistical methods for informing valid data-driven decision making in professional settings
Select statistical tools for guiding data-driven decision making resulting in sustainable operational processes
Utilize a structured approach for data-driven decision making for fostering continuous improvement activities
Propose operational improvement recommendations to 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.
Specifically, the following critical elements must be addressed:
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?
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?
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
Introduction:
Description of the
Scenario
Analysis Plan:
Quantifiable Factors
Exemplary
Meets “Proficient” criteria and
description demonstrates
insightful understanding of the
situation described in the
scenario (100%)
Meets “Proficient” criteria and
demonstrates insight into
operational processes and factors
that may affect performance
(100%)
Proficient
Concisely and accurately
describes the scenario (90%)
Identifies quantifiable factors
that may be affecting the
performance of operational
processes and supports claims
with explanations (90%)
Needs Improvement
Describes the scenario but
description is not concise or
contains inaccuracies (70%)
Identifies quantifiable factors
that may be affecting the
performance of operational
processes but identification is not
supported with explanations or is
cursory (70%)
Analysis Plan: Problem Meets “Proficient” criteria and
Develops a problem statement
Develops a problem statement
Statement
statement demonstrates insight appropriate to the scenario that appropriate to the scenario that
into the relationship between the addresses the given problem and addresses the given problem but
quantifiable measures and
contains quantifiable measures
statement does not contain
problem addressed in the
(90%)
quantifiable measures or is
scenario (100%)
cursory or inappropriate (70%)
Analysis Plan: Strategy Meets “Proficient” criteria and
Proposes a strategy that
Proposes a strategy but strategy
strategy demonstrates insight
addresses the problem of the
either does not address the
into how the strategy impacts
company and seeks to improve
problem or does not seek to
additional operations (100%)
sustainable operational processes improve operational processes
(90%)
(70%)
Statistical Tools and Meets “Proficient” criteria and
Identifies the appropriate family Identifies a statistical family of
Methods: Family of
identification demonstrates
of statistical tools used to
tools used to perform statistical
Statistical Tools
nuanced understanding of
perform statistical analysis,
analysis but either the tools are
statistical tools (100%)
including statistical assumptions not the most appropriate to use
(90%)
or discussion lacks statistical
assumptions (70%)
Statistical Tools and Meets “Proficient” criteria and
Determines the category of the
Determines the category of the
Methods: Category of demonstrates insight into the
provided data, including
provided data but category is
Provided Data
relationship of the category of
justification to support claims
either inaccurate or discussion
data and statistical tools (100%) (90%)
lacks justification to support
claims (70%)
Not Evident
Does not describe the scenario
(0%)
Value
4.05
Does not identify quantifiable
factors that may be affecting the
performance of operational
processes (0%)
6.13
Does not develop a problem
statement appropriate to the
scenario that addresses the given
problem (0%)
6.13
Does not propose a strategy that
addresses the problem of the
company (0%)
6.13
Does not determine a family of
statistical tools (0%)
6.13
Does not determine a category
for the data (0%)
6.13
Statistical Tools and
Methods: Most
Appropriate Tool
Selects the most appropriate
statistical tool used to analyze
the data (100%)
Statistical Tools and
Methods: Justify Tool
Selects a statistical tool but
Does not select a tool to be used
selection is not the most
for analysis (0%)
appropriate given the data (70%)
6.13
Meets “Proficient” criteria and
justification demonstrates insight
into the relationship between
statistical tools and the type of
data (100%)
Statistical Tools and Meets “Proficient” criteria and
Methods: Quantitative description demonstrates insight
Method
into the relationship between the
quantitative method and data
relationships (100%)
Justifies why the tool chosen is
Justifies why the tool chosen is
the most appropriate for analysis the most appropriate for the
of this data (90%)
analysis but justification is either
illogical or cursory (70%)
Does not justify why a particular
tool was chosen (0%)
6.13
Describes the quantitative
method that will best inform the
decision, including how this
method will point out the
relationships between the data
(90%)
Does not describe the
quantitative method (0%)
6.13
Analyze Data: Process
Outlines the process needed to
utilize the statistical analysis
(90%)
Describes the quantitative
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%)
Outlines the process needed to
utilize the statistical analysis but
steps are either inappropriate or
overgeneralized (70%)
Explains how following the
outlined process leads to a valid
decision but explanation is
inappropriate or cursory (70%)
Does not outline the process
needed to utilize the statistical
analysis (0%)
6.13
Does not offer an explanation
why following the outlined
process leads to a valid decision
(0%)
6.13
Describes the reliability of the
results based on data sets,
including a justification to
support claims (90%)
Illustrates a data-driven decision
that addresses the problem and
operational improvement (90%)
Describes the reliability of the
results but description is either
cursory or lacks justification to
support claims (70%)
Illustrates a data-driven decision
that addresses the problem but
illustration is either inappropriate
or overgeneralized (70%)
Does not describe the reliability
of the results (0%)
6.13
Does not illustrate a decision that
addresses the problem (0%)
6.13
Summarizes analysis plan for
internal and external
stakeholders using audienceappropriate jargon (90%)
Summarizes analysis plan for
Does not summarize the analysis
internal and external
plan for stakeholders (0%)
stakeholders but summary either
inappropriately uses jargon or is
cursory (70%)
Meets “Proficient” criteria and
offers great detail for each
identified step (100%)
Analyze Data: Valid, Meets “Proficient” criteria and
Data-Driven Decisions explanation demonstrates a
nuanced understanding of how
following a process will lead to a
valid decision (100%)
Analyze Data:
Meets “Proficient” criteria and
Reliability of Results description demonstrates keen
insight into identifying reliable
data (100%)
Analyze Data: Data- Meets “Proficient” criteria and
Driven Decision
illustration demonstrates a deep
understanding of the interplay
between a problem, the
operation, and operational
improvement (100%)
Recommend
Meets “Proficient” criteria and
Operational
summary demonstrates keen
Improvements:
insight into appropriately
Analysis Plan
communicating an analysis plan
to stakeholders (100%)
Explains how following the
outlined process leads to a valid
data-driven decision (90%)
6.13
Recommend
Operational
Improvements:
Decision
Recommend
Operational
Improvements:
Best Option
Articulation of
Response
Meets “Proficient” criteria and
explanation demonstrates keen
insight into appropriately
communicating a decision and
how it was reached to
stakeholders (100%)
Meets “Proficient” criteria and
justification demonstrates keen
insight as to why the decision is
valid and why it is the optimal
solution, using audienceappropriate jargon (100%)
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%)
Explains the decision for the
problem and how that decision
was reached, using audienceappropriate jargon (90%)
Explains the decision for the
problem but explanation either
lacks how the decision was
reached or uses inappropriate
jargon (70%)
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%)
Submission has no major errors
related to citations, grammar,
spelling, syntax, or organization
(90%)
Justifies why the decision is the
Does not justify to stakeholders
best option but justification lacks that the decision is the best
how it will result in operational
option (0%)
improvement, is cursory, or uses
inappropriate jargon (70%)
Submission has major errors
related to citations, grammar,
spelling, syntax, or organization
that negatively impact readability
and articulation of main ideas
(70%)
Does not explain decision for the
problem (0%)
Submission has critical errors
related to citations, grammar,
spelling, syntax, or organization
that prevent understanding of
ideas (0%)
Earned Total
6.13
6.13
4
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
Standard Error
Median
Mode
Standard
Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
801.1667
24.18766
793
708
83.78851
7020.515
-1.62662
0.122258
221
695
916
9614
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
779
802
818
888
898
902
916
708
695
708
716
784
2007
845
739
871
927
1133
1124
1056
889
857
772
751
820
2008
857
881
937
1159
1072
1246
1198
922
798
879
945
990
Anova: Single Factor
SUMMARY
Groups
2006
2007
2008
ANOVA
Source of Variation
Between Groups
Count
Sum
Average Variance
12 9614 801.1667 7020.515
12 10784 898.6667 18750.06
12 11884 990.3333 21117.88
SS
214772.2
df
MS
F
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