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

ln DLCOST 0.16 (6.58) ** 0.38 (10.31) ** 0.25 (5.19) ** 0.91 (23.78) ** 0.60 (12.62) ** 0.51 (11.84) ** 0.65 (7.27) ** ln NUMSETUPS 0.74 (16.08) ** 0.57 (10.56) ** 0.43 (14.12) ** 0.01 (0.07) 0.28 (7.17) ** 0.31 (7.04) ** 0.17 (7.01) ** ln NUMPARTS 0.11 (2.02) * 0.35 (4.09) ** 0.46 (6.18) ** 0.17 (4.76) ** 0.29 (4.01) ** 0.34 (4.70) ** 0.60 (5.61) ** ILCOST = Indirect Labor Cost DLCOST = Direct Labor Cost NUMSETUPS = Number of Setups NUMPARTS = Number of Distinct Parts * indicates significant at the 5% level. ** indicates significant at the 1% level. Estimation results for the ARMA models appear in Tables 7 and 8. In the multiple cost driver model, we find that NUMSETUPS is significant at the 5% level for six of the seven departments, and NUMPARTS is significant for all seven departments, thus supporting our earlier inference about the significance of these cost drivers. IV. M anagerial I mplications The findings of this study are useful to managers at our research site. The results document that indirect production labor costs are driven by number of setups and number of parts, in addition to the direct labor cost based measure of production volume, and thus the findings provide a more detailed understanding of how these costs arise. More importantly, these results support their cost control efforts by providing specific estimates of the monetary impact of the number of daily setups and parts produced on indirect production labor costs that can be used to evaluate and justify cost- benefit aspects of programs to reduce these aspects of production complexity. Ittner and Larcker (2001) assert that studies on costs need to determine whether an improved understanding of cost drivers may lead to better decision making by managers. To examine the existence of potential costing errors, we estimated the cost distortion or difference between the traditional labor based cost system and a cost model based on multiple cost drivers. Recall that our statistical analysis is based on daily indirect production labor costs and not average product costs for a year. To estimate product costs, therefore, we need to translate our daily cost estimates to average product costs. For this purpose, we first estimated the cost of each batch of parts on the day it was manufactured by inserting the actual values of the number of setups and parts produced on that day in our multiple cost driver model. We then calculated the average indirect production labor costs for each part as a weighted average of daily unit indirect production labor costs for that part based on all the batches manufactured in a year. We compared these average costs with the unit indirect production labor costs calculated using the existing method of estimating indirect production labor costs as a percentage of direct labor costs alone where the percentage factor in each department is the ratio of the total indirect production labor costs to the total direct production labor costs in the preceding year. We calculated the percentage cost difference as [(estimate based on existing method) - (estimate based on multiple cost driver model)] / [estimate based on multiple cost driver model] (Banker et al., 1990). We find that low volume parts tend to be under - costed and high volume parts tend be over- costed. Percentage cost difference for a part is significantly positively correlated (r=0.43, p=0.0001) with its annual production volume, consistent with the literature on the behavior of overhead costs. Table 9: Errors in Cost Predictions Panel A: Mean Absolute Percentage Errors Department Simple Method * Single Driver Regression Multiple Drivers Regression Sheet Metal 20.36 25.16 14.20 Machine Shop 47.93 55.05 33.79 Brush & Steel Wool 39.34 46.39 39.30 Paint Shop 39.53 55.00 50.73 Component Assembly 55.32 59.58 48.90 Welding 23.54 32.01 25.96 Final Assembly 86.95 72.45 69.90 © 2021 Global Journals 2 Global Journal of Management and Business Research Volume XXI Issue II Version I Year 2021 ( ) D 1 Cost Hierarchy: Evidence and Implications