Assignment: Project Guidelines
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 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.
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.
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.
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
|