We have always estimated how many transformers will be neede

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

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1. What type of organization is identified in the scenario: Qualitative/Quantitative, etc.

2. Identify Quantifiable factors that may be affecting the performance

3. Develop a problem statement the addresses the given problem and contains quantifiable measures

4. Strategy that addresses the problem given the study, that seeks to improve operational processes.

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

Solution

1. The organization identified in the scenario is Quantitative.

2. The quantifiable factors that may be affecting the performance are Mean, Standard Error, Standard Deviation, Sample Variance, Skewness, Kurtosis.

3. I dont have the subject knowledge for the rest of the part.