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:

?

?

?

?

?

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 organizations 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 companys 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 companys data, a hypothesis, the results of your

analysis, any inferences from your hypothesis test, and a forecasting model that addresses the companys 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 1012 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,

20072010, that are to be submitted to the quality control department.

A-Cats 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

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