CASE: PERFORMANCE LAWN EQUIPMENT
We use a a fictitious company, Performance Lawn Equipment (PLE), within a case exercise for applying different tools and techniques. To put the case in perspective, we first provide some background about the company, so that the applications of business analytic tools will be more meaningful.
PLE, headquartered in St. Louis, Missouri, is a privately owned designer and producer of traditional lawn mowers used by homeowners. In the past ten years, PLE has added another key product, a medium-size diesel power lawn tractor with front and rear power takeoffs, Class I three-point hitches, four-wheel drive, power steering, and full hydraulics. This equipment is built primarily for a niche market consisting of large estates, including golf and country clubs, resorts, private estates, city parks, large commercial complexes, lawn care service providers, private homeowners with five or more acres, and govemment (federal, state, and local) parks, building complexes, and military bases. PLE provides most of the products to dealerships, which, in turn, sell directly to end users. PLE employs 1,660 people world- wide. About half the workforce is based in St. Louis; the remainder is split among their manufacturing plants.
In the United States, the focus of sales is on the eastern seaboard, California, the Southeast, and the south central states, which have the greatest concentration of customers. Outside the United States, PL E’s sales include a European market, a growing South American market, and developing markets in the Pacific Rim and China.
Both end users and dealers have been established as important customers for PLE. Collection and analysis of end-user data showed that satisfaction with the products depends on high quality, easy attachment/dismount of implements, low maintenance, price value, and service.
For dealers, key requirements are high quality, parts and feature availability, rapid restock, discounts, and timeliness of support.
PLE has several key suppliers: Mitsitsiu, Inc., the sole source of all diesel engines; LANTO Axles, Inc., which provides tractor axles; Schorst Fabrication, which provides subassemblies; Cuberillo, Inc, supplier of trans- missions; and Specialty Machining, Inc., a supplier of precision machine parts.
To help manage the company, PLE managers have developed a balanced scorecard of measures. These data, which are summarized shortly, are stored in the form of a Microsoft Excel workbook (Performance Lawn Equipment) accompanying this book. The database con- tains various measures captured on a monthly or quarterly basis and is used by various managers to evaluate business performance. Data for each of the key measures are stored in a separate worksheet. A summary of these worksheets is given next:
In addition to these business measures, the PLE database contains worksheets with data from special studies:
Elizabeth Burke has received several questions from, the PLE managers regarding quality, customer satisfaction, and operational performance. She would like you to summarize some data from the Performanee Lawn Equipment Database using statistical tools and analysis:
Write a report that clearly shows these results and explain the key insights specifically for the correlation analysis.
Assignment Criteria
| Weight | Above Expectation
80% and above |
Meets Expectation
60%-80% |
Below Expectation
0-50% |
|
| Ques 1 Analysis | 10% | |||
| Ques 2 Analysis | 10% | |||
| Ques 3 Analysis | 10% | |||
| Ques 4 Analysis | 30% | |||
| Presentation | 40% |
To analyze the data in the Customer Survey worksheet, you can create frequency distributions and histograms for each of the attributes: quality, ease of use, price, and service. This will help you visualize the distribution of customer ratings for each attribute.
For each attribute:
To analyze the engine production time data in the Engines worksheet, you can calculate various descriptive statistical measures to understand the central tendency and variability of the data.
For the blade weight samples in the Blade Weight worksheet, you can perform similar steps as in Question 1:
To analyze the correlations among the satisfaction survey variables in the Purchasing Survey worksheet, you can calculate correlation coefficients between pairs of variables. Highlight correlations above +0.5 and below -0.5 for further insights.
For your report, you can structure it as follows:
Remember to keep the report concise and clear. Use headings, bullet points, and visuals to enhance readability. Additionally, ensure that you address each of the questions thoroughly and provide meaningful insights based on the analysis.
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