Case Study Dilbert Toys

Question: Case study about Dilbert Toys. Answer: Summary The case is about Dilbert Toys (DT), a company that manufactures toys. It has been producing two different varieties of toys$Floppin Freddy$Frog and Jumpin Jill$Junebug doll in different lots. The company needs to change the set up for each lot production. They have been estimating the set-up costs by taking number of set-ups$as the cost driver. But, the newly hired accountant Bec$Williams has proposed that$number of set-up$hours should provide an improved cost estimation as the time required for making the necessary arrangements for each kind of toy manufactured by the company is different i.e. for setting up the machinery for production of one toy the time taken is less than the time taken for setting up the machinery for production of second toy. Thus the company collected monthly data for a period of 9 month and noted the number of set ups, number$of set up hours and set up costs. Using regression analysis, it was found that the R-squared value of the set up cost and number of se t up hours is greater than the R-squared value of the set up cost and number of set ups. Thus it can be concluded that the number of set-up hours is better indicator to estimate the set up costs than the number of set-ups and henceforth DT should use number of set-up hours to determine the set-up costs and it has superior understanding of the costs borne by DT. This will help them take necessary steps to reduce the cost and improve efficiency of the firm. Case Dilbert Toys (DT) is a company that manufactures toys. It has been producing two different varieties of toys$Floppin Freddy$Frog and Jumpin Jill$Junebug doll. The company has incurs a set up cost for each lot of dolls that it produces to set up the raw materials, labour and machinery required for the manufacturing of every lot. Because of this, DT has to bear set-up costs for each batch of toys that it manufactures. DT currently uses number of set-ups as the cost driver for approximating the set-up costs with each lot and computing overall production cost in the manufacture of the dolls. The new hired accountant by Dilbert Toys is Bec Williams. He thinks that since the set-up time for every lot produced by DT is not the same, it makes more sense to use number of set-up hours as the cost driver instead of number of set-ups. To understand the relation between the set up cost and number of set up hours, he collected the monthly data of the number of set ups, number of set up hours and set up costs for a period of 9 months. He has collected the following information. Month Number of set-ups Number of set-ups hours Set-up costs 1 300 1840 104600 2 410 2680 126700 3 150 1160 57480 4 480 3800 236840 5 310 3680 178880 6 460 3900 209620 7 420 2980 209620 8 300 1200 90080 9 270 3280 221040 To understand the relation between the set up costs, the number of set up hours and number of set ups, a regression analysis is used. Month Number of set-ups Set-up costs 1 300 104600 2 410 126700 3 150 57480 4 480 236840 5 310 178880 6 460 209620 7 420 209620 8 300 90080 9 270 221040 SUMMARY OUTPUT Regression Statistics Multiple R 0.682 R Square 0.465 Adjusted R Square 0.388 Standard Error 51351.141 Observations 9.000 ANOVA Df SS MS F Significance F Regression 1.00E+00 1.60E+10 1.60E+10 6.08E+00 4.31E-02 Residual 7.00E+00 1.85E+10 2.64E+09 Total 8.00E+00 3.45E+10 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 14256.330 61323.419 0.232 0.823 -130750.514 159263.175 -130750.514 159263.175 X Variable 1 421.469 170.960 2.465 0.043 17.214 825.724 17.214 825.724 Using the above data in Excel, Regression analysis was performed. The number of set ups was used as input range X and the set up cost was used as the input range Y. The output generated from the regression analysis in excel, is given below. There are 9 observations and the R-squared value for the regression analysis is 0.464 The coefficients of the intercept is 14256.33 and The coefficients of X variable 1 are 421.469. Thus the regression equation is Set up costs, Y = 421.469* (Number of set ups) + 14256.33 Y = 421.469* X + 14256.33 The F test of significance validates the R square value and the test is statistically significant. Month Number of set-ups hours Set-up costs 1 1840 104600 2 2680 126700 3 1160 57480 4 3800 236840 5 3680 178880 6 3900 209620 7 2980 209620 8 1200 90080 9 3280 221040 Using the above data in Excel, Regression analysis was performed. The number of set up hours was used as input range X and the set up cost was used as the input range Y. The output generated from the regression analysis in excel, is given below. SUMMARY OUTPUT Regression Statistics Multiple R 0.920 R Square 0.846 Adjusted R Square 0.824 Standard Error 27572.584 Observations 9.000 ANOVA Df SS MS F Significance F Regression 1.0 29163550009.7 29163550009.7 38.4 0.0 Residual 7.0 5321731679.2 760247382.7 Total 8.0 34485281688.9 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 7526.778 26191.24 0.287378 0.782144 -54405.7 69459.22 -54405.7 69459.22 X Variable 1 55.75526 9.002085 6.193594 0.000448 34.46871 77.04181 34.46871 77.04181 There are 9 observations and the R squared value of the regression analysis between the number of set up and the set up cost is 0.845 The coefficients of the intercept is 7526.77 and The coefficients of the X variable 1 are 55.755. Thus the regression equation is Set up$costs, Y = 55.75* (Number$of set ups) + 7526.77 Y$= 55.755* X + 7526.77 The F test of significance validates the R square value and the test is statistically significant. In$a linear regression, the co-efficient of the independent variable helps in understanding the effect it has on the independent variable. If the co-efficient of the independent variable is positive, then the dependent variable will increase with the increase in independent variable. The constant term gives the fixed cost that the company will incur even if no set up change takes place. In this case the variables number of set-up hours and set up costs are positively correlated. The R square value of the regression is the percentage of variation that can be predicted by the independent variables. The R squared value helps to keep an eye on the$data and determine how they are related to each other. The R squared value varies from 0$and 1. The greater the value$of R squared the more accurately the regression line can predicted the output for a particular input. In case of DT, it has been established that the R squared value for number of set-ups and set up cost is 0.464 whereas the R$squared value$for the other regression is 0.845. P value of a regression analysis helps us understand the statistical significance of the co-efficient obtained from the regression. It tells us how confident we can be about the relation between the dependent and independent variable. In this case we can be 95% confident about the results from the regression analysis in both the cases. Thus as the number$of set up hours varies from product to product, the cost suffered by the DT in setting up raw materials, labour, for manufacturing of various product fluctuates with the time consumed in setting up. Thus the company should use the number$of set up hours to determine the set up cost as a substitute of number of set ups. References Interpreting Regression Output. (n.d.). Frost Jim (2013). How to Interpret Regression Analysis Results: P-values and Coefficients. Kishore Aseem. (2010). Add a Linear Regression Trendline to an Excel Scatter Plot. Case