Global Journal of Management and Business Research, D: Accounting and Auditing, Volume 21 Issue 2

In Final Assembly, the number of distinct parts is small, reflecting only the number of products, rather than all the parts and components. The number of setups is low indicating the long production runs in that department. The department also has the fewest number of workers and the lowest proportion of indirect to direct labor reflecting the high level of labor intensity of its production process. For each department the standard deviations and distribution deciles (not shown here) of all variables reveal considerable variation in the daily data within each year. Their distributions exhibit little skewness, with the median values (not shown here) very close to the means. Table 2 reports, by department, Pearson correlations between ILCOST, DLCOST, NUMSETUPS and NUMPARTS in the upper triangles and Spearman correlations in the lower triangles. Except where noted, all of the correlations are significant at the 1% level suggesting that omitting cost drivers may result in biased coefficients. ILCOST is significantly correlated with each of the other three variables. The magnitude of the Pearson correlation of ILCOST with DLCOST is the lowest for the three departments in Plant A, but the highest in three of the four departments in Plant B, reflecting once again the differences in the job shop versus assembly line type settings in the two plants. Because different parts usually require separate setups, NUMPARTS and NUMSETUPS are highly correlated in all departments except the Paint Shop and the Final Assembly departments. The magnitudes of all correlation coefficients differ considerably across departments and plants, reflecting differences in process characteristics. Pearson correlation coefficients range between 0.65 and 0.93 in the Sheet Metal and Machine Shop departments, and between 0.48 and 0.60 in the Brush & Steel Wool department. All except two of the coefficients range from only 0.02 to 0.43 for three of the four departments in Plant B, but they are between 0.50 and 0.84 in the Welding department. Table 2: Panel a Pearson and Spearman Correlations for Production Departments in Plant A (Daily Data) Sheet Metal Variable ILCOST DLCOST NUMSETUPS NUMPARTS ILCOST 1.00 0.65 0.68 0.68 DLCOST 0.59 1.00 0.73 0.78 NUMSETUPS 0.68 0.56 1.00 0.93 NUMPARTS 0.63 0.62 0.93 1.00 Machine Shop Variable ILCOST DLCOST NUMSETUPS NUMPARTS ILCOST 1.00 0.76 0.88 0.83 DLCOST 0.57 1.00 0.82 0.78 NUMSETUPS 0.80 0.68 1.00 0.93 NUMPARTS 0.76 0.68 0.94 1.00 Brush & Steel Wool Variable ILCOST DLCOST NUMSETUPS NUMPARTS ILCOST 1.00 0.48 0.54 0.56 DLCOST 0.53 1.00 0.55 0.60 NUMSETUPS 0.57 0.50 1.00 0.70 NUMPARTS 0.58 0.56 0.71 1.00 Please see Table 1 for the definition of variables. Pearson correlations are above the diagonal, Spearman correlations are below the diagonal. All correlations are significant at the 1% level. Table 2: Panel B Pearson and Spearman Correlations for Production Departments in Plant B (Daily Data) Paint Shop Variable ILCOST DLCOST NUMSETUPS NUMPARTS ILCOST 1.00 0.37 0.20 0.25 DLCOST 0.34 1.00 0.41 0.43 NUMSETUPS 0.21 0.35 1.00 0.30 NUMPARTS 0.27 0.38 0.29 1.00 5 Global Journal of Management and Business Research Volume XXI Issue II Version I Year 2021 ( ) D © 2021 Global Journals Cost Hierarchy: Evidence and Implications