Performance Lawn Equipment

The worksheet Purchasing Survey in the Performance Lawn Care database provides data related to predicting the level of business (Usage Level) obtained from a third-party survey of purchasing managers of customers Performance Lawn Care.8 

The seven PLE attributes rated by each respondent are

8 The data and description of this case are based on the HATCO example on pages 28–29 in Joseph F. Hair, Jr., Rolph E. Anderson, Ronald L. Tatham, and William C. Black, Multivariate Analysis, 5th ed. (Upper Saddle River, NJ: Prentice Hall, 1998).

• Delivery speed —the amount of time it takes to deliver the product once an order is confirmed
• Price level —the perceived level of price charged by PLE
• Price flexibility —the perceived willingness of PLE representatives to negotiate price on all types of purchases
• Manufacturing image —the overall image of the manufacturer
• Overall service —the overall level of service necessary for maintaining a satisfactory relationship between PLE and the purchaser
• Sales force image —the overall image of the PLE’s sales force
• Product quality —perceived level of quality

Responses to these seven variables were obtained using a graphic rating scale, where a 10-centimeter line was drawn between endpoints labeled “poor” and “excellent.” Respondents indicated their perceptions using a mark on the line, which was measured from the left endpoint. The result was a scale from 0 to 10 rounded to one decimal place.

Two measures were obtained that reflected the outcomes of the respondent’s purchase relationships with PLE:

• Usage level —how much of the firm’s total product is purchased from PLE, measured on a 100-point scale, ranging from 0% to 100%
• Satisfaction level —how satisfied the purchaser is with past purchases from PLE, measured on the same graphic rating scale as perceptions 1 through 7

The data also include four characteristics of the responding firms:

• Size of firm —size relative to others in this market (0=small;1=large)(0=small;1=large)
• Purchasing structure —the purchasing method used in a particular company (1=centralized procurement,0=decentralized procurement)(1=centralized procurement,0=decentralized procurement)
• Industry —the industry classification of the purchaser [1=retail(resale such as Home Depot),0=private(nonresale, such as a landscaper)][1=retail(resale such as Home Depot),0=private(nonresale, such as a landscaper)]
• Buying type —a variable that has three categories (1=new purchase,2=modified rebuy,3=straight rebuy)(1=new purchase,2=modified rebuy,3=straight rebuy)

Assignment:

• Elizabeth Burke would like to understand what she learned from these data.
• Apply appropriate data-mining techniques to analyze the data. For example, can PLE segment customers into groups with similar perceptions about the company?

Can cause-and-effect models provide insight about the drivers of satisfaction and usage level? Summarize your results in a report to Ms. Burke.

Assignment:

Part 1:

The Performance Lawn Equipment database contains data needed to develop a pro forma income statement. Dealers selling PLE products all receive 18% of sales revenue for their part of doing business, and this is accounted for as the selling expense. The tax rate is 50%.

• Develop an Excel worksheet to extract and summarize the data needed to develop the income statement for 2014 and implement an Excel model in the form of a pro forma income statement for the company.

Part 2:

The CFO of Performance Lawn Equipment, J. Kenneth Valentine, would like to have a model to predict the net income for the next 3 years. To do this, you need to determine how the variables in the pro forma income statement will likely change in the future.

• Using the calculations and worksheet that you developed along with other historical data in the database, estimate the annual rate of change in sales revenue, cost of goods sold, operating expense, and interest expense.

Use these rates to modify the pro Forma income statement to predict the net income over the next 3 years.

• Because the estimates you derived from the historical data may not hold in the future, conduct appropriate what-if, scenario, and/or parametric sensitivity analyses to investigate how the projections might change if these assumptions don’t hold.

Construct a tornado chart to show how the assumptions impact the net income in your model.

Summarize your results and conclusions in a report to Mr. Valentine.

Assignment:

One of PLE’s manufacturing facilities produces metal engine housings from sheet metal for both mowers and tractors. Production of each product consists of five steps: stamping, drilling, assembly, painting, and packaging to ship to its final assembly plant.

The production rates in hours per unit and the number of production hours available in each department are given in the following table:

Department

Mower Housings

Tractor Housings

Production Hours Available

Stamping

0.03

0.07

200

Drilling

0.09

0.06

300

Assembly

0.15

0.10

300

Painting

0.04

0.06

220

Packaging

0.02

0.04

100

In addition, mower housings require 1.2 square feet of sheet metal per unit and tractor housings require 1.8 square feet per unit, and 2,500 square feet of sheet metal is available.

The company would like to maximize the total number of housings they can produce during the planning period.

• Formulate and solve a linear optimization model and recommend a production plan.

Illustrate the results visually to help explain them in a presentation to Ms. Burke. In addition, conduct whatever what-if analyses (e.g., run different scenarios and apply parameter analysis) you feel are appropriate to include in your presentation.

Summarize your results in a well-written report.

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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.