SOLVED!! Team Case Analysis – Performance Lawn Equipment – Chapter 9

Team Case Analysis

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. Using the data in the Performance Lawn Equipment Database, develop forecasting models for these data and prepare a report of your results with appropriate charts and output from Excel.


An important aspect of business analytics is good communication. Summarize your findings and write up your answers to this case formally in a well-written report as if you were a consultant to Ms. Burke USING our class:

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.


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. 

Table of Contents “To do List”

1. Executive Summary

2. Overview Case Analysis – What’s this case analysis about?

3. Scope of Consulting Work and Report – 

· Detailed List of Assignments this case analysis is requesting.

4. Introduction

Additional Information:

Find attached samples of a case analysis. Do not copy material is just as a reference (to have an idea)


ANLY 502-91-2017/Late Fall – Analytics: Principles and Application

Trendlines and Regression Analysis In reviewing the PLE data, Elizabeth Burke noticed that defects received from suppliers have decreased (DefectsAfterDelivery.csv data file). 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.

Use techniques of regression analysis to assist her Page 3 of 24 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.



> library(readxl)

> DefectsAfterDelivery <- read_excel(“Harrisburg Courses/ANLY 500-91-2017/Lab 1/DefectsAfterDelivery.xlsx”)

> View(DefectsAfterDelivery)

> Data_1<- c(DefectsAfterDelivery$`2010`,DefectsAfterDelivery$`2011`)

> Data_1 [1] 812 810 813 823 832 848 837 831 827 838 826 819 828 832 847 839 832 840 849 857 839 842 828 816

> Data_1<- Data_1[1:22]

> month.number<- c(1:22)

> Data_1 <- matrix(c(month.number,Data_1),ncol =2)

> Data_1 [,1] [,2] [1,] 1 812 [2,] 2 810 [3,] 3 813 [4,] 4 823 [5,] 5 832 [6,] 6 848 [7,] 7 837 [8,] 8 831 [9,] 9 827 [10,] 10 838 [11,] 11 826 [12,] 12 819 [13,] 13 828 [14,] 14 832 [15,] 15 847 [16,] 16 839 [17,] 17 832 [18,] 18 840 [19,] 19 849 [20,] 20 857 [21,] 21 839 [22,] 22 842

> Data_1.Mod <- lm(Data_1[,2]~Data_1[,1]) > summary(Data_1.Mod) Call: lm(formula = Data_1[, 2] ~ Data_1[, 1])

Residuals: Min 1Q Median 3Q Max -14.440 -6.678 -1.944 6.976 22.572

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 817.4156 4.0677 200.952 < 2e-16 *** Data_1[, 1] 1.3354 0.3097 4.312 0.000339 *** —

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 9.216 on 20 degrees of freedom

Multiple R-squared: 0.4818, Adjusted R-squared: 0.4558

F-statistic: 18.59 on 1 and 20 DF, p-value: 0.0003394

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