Packaging Products International (PPI) is a large manufacturer of packaging materials for supermarkets and other retail applications; the packages are used by customers to carry away their purchases. PPI has succeeded for many years by providing a high-quality product and superior customer service. Recently, additional competitors have entered the market, both local and foreign, and PPI is finding that it must increasingly compete on price. PPI's strategy for dealing with the increased competition is to market its products to smaller retailers that would appreciate the firm's quality and service, as well as the firm's ability to customize the product-adding different designs and colors to the packaging material. Until recently, the firm determined product costs based on simple averages of materials purchases, plant labor, and overhead. The firm's management is now interested in improving, if possible, the accuracy of its cost information. As a start, Chris Atkins, the management accountant, obtains the following sample of data (Table 1) from the plant manager, showing the machine number, the order size (quantity, in thousands), the machine setup time (in hours per unit; setup time also includes cleanup time after the order is run), run time (the operating time for the machine to produce the order), and a measure of the complexity of the order based on a subjective rating where 1 = less complex and 2 = more complex (complexity relates to the number and type of images and colors printed on the packaging material).

Chris wants to run some regression analyses to better understand these data and, as a first step, obtains a correlation analysis which shows the simple correlation between each of the variables in Table 1. The results are shown in Table 2. Chris understands that each of the correlation numbers in Table 2 is equivalent to the R-squared for a simple linear regression between the variables, as follows: (correlation between two variables)2 = the R-squared for simple regression analysis between these two variables. To illustrate, note that the correlation between machine number and order size = -.0700=. The R-squared for the regression between these two variables (with either as the dependent variable) is (-.0700=)2 = .0049. Chris also recalls that a negative correlation means that the two variables are inversely related-when one increases, the other decreases.

Required

1. Analyze the findings in Table 2 and assess how, if at all, order size and complexity affect setup time and run time. What other findings in Table 2 are of particular interest?

2. How can your analysis in requirement 1 help PPI become more competitive?

Correlations with absolute value > .4 are statistically significant at p .5 are statistically significant at p

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Table Plant Data for PPI Per Unit Machine Number Order Size Order Complexity Setup Time 0.0023 0.0001 0.0063 0.0158 0.0022 0.0023 0.0042 0.0026 0.0030 0.0111 0.0037 0.0080 0.0045 0.0022 0.0017 0.0041 0.0054 0.0045 0.0050 Run Time 0.0290 0.0434 0.0419 0.0400 0.0355 0.0405 0.0400 0.0413 0.0411 0.0376 0.0430 0.0345 0.0407 0.0299 0.0433 0.0417 0.0340 0.0425 0.0467 1,220 489 180 2,160 1,377 420 540 360 1,080 300 2,400 81 1,350 120 120 2,260 240 60 Table 2 Correlation Results for PPI's Plant Data Number Order Size ComplexitySetup Time Run Time Number Order size -0.07005 Complexity-0.01714 Setup time -0.06786 Runtime -0.14071 -0.01052 -0.73279 0.08663 0.11588 -0.01959