Performance Lawn Equipment Final Copy PDF

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Running head: PLE CASE ANALYSIS

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Performance Lawn Equipment Case 8 Analysis Group 6.2 EBTM 365 Professor Zhang

PLE CASE ANALYSIS

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Summary When conducting the necessary analysis for the worksheets we were given provided by Ms. Burke, we had to take many outcomes that played into our resolutions so that they will provide Ms. Burke with details of how to resolve the issues she is willing to handle at Performance Lawn Equipment company. Our duty was to address the problems that included predicting what might have happened if the supplier initiative wasn’t implemented including the number of defects as how further it could be reduced, where and how its recruitment policies involving specific characteristics could lead to a high retention rate, and how we can predict the time required to produce new engines on the production line. After considering these issues, we had to take the necessary steps to resolve them. We have completed the following analysis of the three worksheets which will be explained very briefly later on in this report and how we decide to approach those issues by using the critical 6 phases that including Recognizing the problem, Defining the problem, Structuring the problem, Analyzing the problem, Interpreting the results and Making decisions and finally Implement the solution and put it to action. We have completed that task by the use of Regression, Trendline Charts, and Forecasting charts to complete the three projects needed in which we have been able to conclude and influence Ms. Burke to make crucial decisions that can help her. The Performance Lawn Equipment company to grow dramatically, increase production times and improving their recruitment that can generate new employees are self-efficient to take on any labor task.

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Defects on Delivery On the analysis of the raw data provide by Ms. Burke, she had noticed that the defects form its suppliers has decreased. She had noticed that in 2014, PLE had an issues with quality due to the increasing amount of defects it had experienced so in 2015,it implemented a plan to decrease that back in 2015 which proved very evident that the number of defects ranging from the five years shown on the worksheet depicted the information as a representation of errors decreasing between the years of 2014 to 2018. It has also illustrated that around 800 million defects were founded in 2018, so it shows that at least 50% of errors as the reduction rate for suppliers that year. Understandably, PLE changed its ways to reduce a massive shortage of supplies so that way they could save money and have the assurance that their supplies were appropriately manufactured to decrease scarcity of supplies. The steps necessary to resolve this matter ensure that the relationship between suppliers and manufacturers is maintained respectfully and do an analysis that we have done to show what could potentially happen if Ms. Burke’s PLE didn’t implement the initiative. So, before we concluded, we had to set the data up to retrieve a precise regression if the action wasn’t fulfilled. Below you will find that we create a regression using the following data and we separated all those data values and put them into two columns of Month and the year of 2014 and 2015 which up being 19 observation points, which shows that multiple R has a strong positive correlation of 0.634.

PLE CASE ANALYSIS

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By looking at the regression statistics, we can predict by this data that our regression formula results in “Defects=817.333-1.33509” and that the countermeasure proves to tell that the deficits showed to be decreasing meaning that the idea of implementing the plan was significant and looking at that 0.40 R Square value it issues that 40% increase of defects can be received and presented in the year of 2015 which means that if the initiative process weren’t implemented in the first place, the number of deficits would get bigger. And by looking at the Significance F value, it's small compare to the P-value of time; we can see that deficits are increasing within a specified period. As provided below, we also have decided to use a forecasting sheet and a line chart with trendlines to show how the shortage began and how it decreased from the beginning of 2015 to 2018 that depicts the actions that were taken if the initiative was not applied back in September 2015.

PLE CASE ANALYSIS

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No Defects 12000

9916

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10049

f(x) = − 994.2 x + 11658.6 R² = 0.84

9431 8029 5955

6000 4000 2000 0

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Employee Retention As for this chart, Ms. Burke wants to know how to resolve a high rate of turnover that’s currently affecting the field service staff service. As for the recruitment part, PLE tends to hire based on characteristics of individuals that have led to vast employee retention. But unfortunately, in a recent meeting, managers couldn’t agree on the components. So, the staff decided to conduct a statistical study to determine the effect of the Yrs. of Education, Grade point average, and age when those employees are hired. We did this search by using a sample of

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40 field service employees who were hired ten years ago to determine the influence of these characteristics by using regression analysis. Before we used regression analysis, we decided to remove Gender, College Graduate, and Local as factors that didn’t provide as much use as we first thought, so we continued with Yrs. of education, GPA, and Age. As you can see that standard error is 2.7255, R Square is 0.1502 which proves to be a significant impact of retention and the Adjusted R rate is 0.079 and by looking at the P values for all the characteristics of the employees show that age is superior to Yrs. of education and GPA. We also conducted two more regression tests and eliminated P-Values that are bigger than 0.05 which we proceeded to do until we finally discover that the characteristic of age having a P-value of 0.0376 is the essential characteristics for employee retention and there is a 96% correlation

PLE CASE ANALYSIS

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Engines

that

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Age

characteristic

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any

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hired.

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For our final task to help Ms. Elizabeth Burke, wants PLE to continue to be competitive with the latest new production technology, which is very critical for any lawn mowing business where you can guarantee an advantage if you can develop cost-effective ideas to increase production. We also have considered that with all the technology that is out there, there’s a time to learn which could result in a gradual decrease in production time and improvement if PLE is unable to proceed to create units. To start the analysis, we begin with the 50 units that PLE has produced and use that to come up with future costs without having to run trials which will help Ms. Elizabeth Burke to handle the situation better. The first step is to create a useful regression model and a linear regression model with trendlines. As you see below from our regression data, we can see that our R Square result is 85%, it shows that the model is worth looking at but can be improved.

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Linear Regression 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0

f(x) = − 0.29 x + 58.18 R² = 0.85

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7 10 13 16 19 22 25 28 31 34 37 40 43 46 49

By looking at the residual plots, we can see that finding the best fit for ourselves is to use a polynomial version model which proves to be more accurate which is discovered after completing the production of the more units after 50. And if we look at ANOVA, our significance F value is zero so we would reject the null hypothesis and predict the slope is significant by mirroring the P-Values that are less than 0.05. In conclusion, it shows that our independent value that’s our sample is substantial and affects our dependent variable value that is our engine production time which means the farther the engines are created, we would witness a smaller production time to manufacture PLE products.

Sample Residual Plot Residuals

8 6 4 2 0 -2 0

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Polynomial 70.0 60.0

f(x) = − 0 x³ + 0.03 x² − 1.22 x + 64.11 R² = 0.99

50.0 40.0 30.0 20.0 10.0 0.0

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Conclusion After reviewing all the analysis for Defects After Delivery, Employee Retention, and Engines Worksheet. We can conclude by taking notice of this updated report on how to improve PLE Business, which will provide a considerable impact when it comes to future sales and help Ms. Burke to improve PLE operations. We were able to resolve and provide ideas on the issues that included predicting what might have happened if the supplier initiative wasn’t implemented including the number of defects as how further it could be reduced, where and how its recruitment policies involving specific characteristics could lead to a high retention rate, and how we can predict the time required to produce new engines on the production line. For our defects, PLE should continue to use the plan that was implemented back in early 2015 and require supplies to perform quality control guidelines that are made on PLE’s behalf to reduce the amounts of defects in the future. For our Employee Retention rate, we believe that even though age is a significant factor when it comes to hiring for our field services, PLE shouldn’t focus only on that factor. They should be able to possibly hire people that have a

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minimum amount of years of experience in the workforce to increase the average age of the employees currently at PLE. And finally, for Production times on engines, if PLE can continue to expand its sample size, the production lines can decrease which can provide a massive rate of improvement and a high standard of experience with tackling new technology to use which could result in giving PLE a respectable reputation and give Ms. Elizabeth Burke an idea of how the analysis provided in the three worksheets can improve PLE as an entire business. For our Employee Retention rate, we believe that even though age is a significant factor when it comes to hiring for our field services, PLE shouldn’t focus only on that factor. They should be able to possibly hire people that have a minimum amount of years of experience in the workforce to increase the average age of the employees currently at PLE. And finally, for Production times on engines, if PLE can continue to expand its sample size, the production lines can decrease which can provide a massive rate of improvement and a high standard of experience with tackling new technology to use which could result in giving PLE a respectable reputation and give Ms. Elizabeth Burke an idea of how the analysis provided in the three worksheets can improve PLE and keep them on the path towards success.

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