Cost Accounting

Questions: a) Critically evaluate the uses of managerial accounting information for strategic decision making in various business contexts. b) Select devise and apply different types of cost allocation and explain their different roles for supporting strategic managerial decision making. c) Design and prepare budgets and explain feedback analysis and strategic control. d) Discuss various approaches to performance evaluation and control in various types of organisations and devised and evaluate simple indicators of performance. Answers: Assignment Question Dilbert Toys (DT) makes the popular Floppin Freddy Frog and Jumpin Jill Junebug doll in batches. DT incurs set-up costs for each batch 0f dolls that it produces. DT uses number of set-ups as the cost driver for set-up costs. DT has just hired Bec Williams, an accountant. Bec thinks that number of set-up hours might be better cost driver because the set-up time for each product is different. Bec collects the following data: (you should enter your student number(without letter)in the following box to get your own data to work on. you will get zero mark if you use the current numbers in the following table) Analysis Report This report is an analysis report to assess the setup costs for Dilbert Toys (DT). The two alternatives that are available to determine the setup costs are: Based on number of set ups Based on number of setup hours The data of 9 months has been used for the study and the data is as below: 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 Regression has been performed with the help of MS Excel considering setup costs as the dependent variable. Two separate models have been computed with number of setups and number of setup hours as independent variable respectively (Gelman Hill, 2007; Archdeacon, 2000). The results are as below: Setup Costs v/s Number of setups Regression Statistics Multiple R 0.68 R Square 0.46 Adjusted R Square 0.39 Standard Error 51351.14 Observations 9.00 ANOVA Df SS MS F Regression 1.00 16026703954.95 16026703954.95 6.08 Residual 7.00 18458577733.94 2636939676.28 Total 8.00 34485281688.89 Coefficients Standard Error t Stat P-value Intercept 14256.33 61323.42 0.23 0.82 Number of set-ups 421.47 170.96 2.47 0.04 From the above results the model for setup costs depending on number of setups is as below: Setup costs = 421.47*no of setups + 14256.33 The goodness fit of this model is observed to be 46% with R-square value noted be 0.48 indicating that the model is not quite reliable. The p value is noted to be at 0.04 which is lesser than 0.05 indicating the number of setups have significant impact on set up costs at 5% significance level but is not significant at 1% level (Chatterjee Hadi, 2015). Setup Costs v/s Number of setup hours Considering number of setup hours as the independent variable, the regression results are as below: Regression Statistics Multiple R 0.92 R Square 0.85 Adjusted R Square 0.82 Standard Error 27572.58 Observations 9.00 ANOVA df SS MS F Regression 1.00 29163550009.66 29163550009.66 38.36 Residual 7.00 5321731679.23 760247382.75 Total 8.00 34485281688.89 Coefficients Standard Error t Stat P-value Intercept 7526.78 26191.24 0.29 0.78 Number of set-ups hours 55.76 9.00 6.19 0.00 The model based on number of set up hours is as below: Setup costs = 55.76*no of setup hours + 7526.78 This model is reliable to the tune of 85% with R square value noted to be at 0.85. Even the p value is noted to be at 0.00 which implies that the number of set up hours have significant impact on the set up costs. Regression Lines The regression lines computed based on the values are as below. Based on the regression equations discussed above, the lines are framed as above for determining the setup costs based on number of setups and number of setup hours respectively (Weisberg, 2013). With the help of MS Excel the data has been plotted for the data with the trend line equation as determined with the help of regression. The setup costs have been plotted on y axis considering number of setups on x axis. The equation for determination is as shown in the chart and the coefficient of determination is 48%. Another plot has been made with respect to the number of setup hours and the model for determining the setup costs is as plotted above. The coefficient of determination is 85% (Gordon, 2015; Doyle, 2003). Recommendations As may be observed from the determination coefficient R2, the model to determine the setup costs on the basis on number of setups is lesser as compared to number of setup hours. This implies that the reliability of model based out of number of setup hours is high and is thus recommended for determining the setup costs for the Dilbert Toys. References Archdeacon, T.J., 2000. Correlation and Regression Analysis: A Historian's Guide. Univ of Wisconsin Press. Chatterjee, S. Hadi, A.S., 2015. Samprit Chatterjee, Ali S. Hadi. John Wiley Sons. Doyle, A.M., 2003. Regression: A Universal Experience. Greenwood Publishing Group. Gelman, A. Hill, J., 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. Gordon, R.A., 2015. Regression Analysis for the Social Sciences. 2nd ed. Routledge. Weisberg, S., 2013. Applied Linear Regression. John Wiley Sons.