Dear International Data Analytics for Business Executives:

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Please read the following Data Analytics for Business case assigned for this Learning Week and please analyze and answer the requested in this Case Analysis.

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In reviewing the PLE data, Elizabeth Burke noticed that defects received from suppliers have decreased (worksheet Defects After Delivery). Upon investigation, she learned that in 2010, PLE experienced some quality problems due to an increasing number of defects in materials received from suppliers.

The company instituted an initiative in August 2011 to work with suppliers to reduce these defects, to more closely coordinate deliveries, and to improve materials quality through reengineering supplier production policies. Elizabeth noted that the program appeared to reverse an increasing trend in defects; she would like to predict what might have happened had the supplier initiative not been implemented and how the number of defects might further be reduced in the near future.

In meeting with PLE’s human resources director, Elizabeth also discovered a concern about the high rate of turnover in its field service staff. Senior managers have suggested that the department look closer at its recruiting policies, particularly to try to identify the characteristics of individuals that lead to greater retention. However, in a recent staff meeting, HR managers could not agree on these characteristics.

Some argued that years of education and grade point averages were good predictors. Others argued that hiring more mature applicants would lead to greater retention. To study these factors, the staff agreed to conduct a statistical study to determine the effect that years of education, college grade point average, and age when hired have on retention. A sample of 40 field service engineers hired 10 years ago was selected to determine the influence of these variables on how long each individual stayed with the company. Data are compiled in the Employee Retention worksheet.

Finally, as part of its efforts to remain competitive, PLE tries to keep up with the latest in production technology. This is especially important in the highly competitive lawn-mower line, where competitors can gain a real advantage if they develop more cost-effective means of production. The lawn-mower division therefore spends a great deal of effort in testing new technology. When new production technology is introduced, firms often experience learning, resulting in a gradual decrease in the time required to produce successive units. Generally, the rate of improvement declines until the production time levels off. One example is the production of a new design for lawn-mower engines. To determine the time required to produce these engines, PLE produced 50 units on its production line; test results are given on the worksheet Engines in the database. Because PLE is continually developing new technology, understanding the rate of learning can be useful in estimating future production costs without having to run extensive prototype trials, and Elizabeth would like a better handle on this.

Assignment:

Use techniques of regression analysis to assist her in evaluating the data in these three worksheets and reaching useful conclusions.

Summarize your work in a formal report with all appropriate results and analyses.

Assignment:

• An important part of planning manufacturing capacity is having a good forecast of sales. Elizabeth Burke is interested in forecasting sales of mowers and tractors in each marketing region as well as industry sales to assess future changes in market share.

• She also wants to forecast future increases in production costs. Develop forecasting models for these data and prepare a report of your results with appropriate charts and output from Excel.

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Please develop YOUR OWN TEAM Business Analytics EXCEL model(s) to support all your answers and analyses and post this(these) file(s) in addition to your Case Analysis Performance Lawn Equipment Report.

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Finally, please answer ALL Questions and Sections of this Data Analytics for Business Case with great detail AND STEP BY STEP being extremely methodical and accurate in your answers. It is extremely important that for each Question and Section, you write the entire question and you LABEL and/or PLACE the appropriate headings and subheadings clearly for EACH part of the question and/or section.

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Please address, analyze, and discuss in great detail and thoroughly support and explain the what’s, how’s, and why’s of each of your answers.

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I expect high caliber Case Analysis Reports with top analyses and interesting insights!! If you have any questions, please let me know. I am here to help.

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Jose Rocha

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Learning Assignment Guidelines

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Case Analysis Written Report:

This document should be a typed professional written report in Word and PDF format with a 1.5 max line space, a suitable font such as Times Roman or Calibri of 12 point maximum, and left, right, top, bottom margins of .8 inches maximum.

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** No Word and PDF format documents: No grading.

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Please submit your Team Case Analysis Written Report and YOUR OWN TEAM Business Analytics Case Analysis EXCEL model(s) files.

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** No Team Case Analysis Written Report and YOUR OWN TEAM Business Analytics Case Analysis EXCEL model(s) files: No grading.

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TurnItIn Originality Score Index

Case Analysis Reports with a TurnItIn Originality Score Index:

* Orange (50-74% similarity index) and/or Red (75-100% similarity index)

will NOT be graded and will be reviewed and assess for potential

Academic Dishonesty misconduct.

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Using APA Style and Tips in Writing

You can find several readings and support material for your writing learning assignments and APA Style use at our Canvas Learning Management System section:

Course Content: 1.2 Using APA Style and Tips in Writing