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

Panel B : Mean Squared Percentage Errors Department Simple Method * Single Driver Regression Multiple Drivers Regression Sheet Metal 666.48 1157.58 357.23 Machine Shop 15074.90 26104.84 4468.52 Brush & Steel Wool 7308.71 4818.04 2576.89 Paint Shop 7053.32 44463.26 39217.46 Component Assembly 8707.42 14657.80 7705.59 Welding 1089.13 10149.26 3477.02 Final Assembly 46946.06 28694.67 24251.18 • The simple method used at our research site estimates indirect production labor costs for each production department by multiplying the daily production labor costs by the ratio of its total indirect production labor costs to its total direct production labor costs in the preceding year. The documented violation of the fundamental assumptions underlying the existing labor- based cost accounting system suggests that many of the cost estimates based on that system may be distorted. We explore this issue by evaluating how these different methods perform in providing cost predictions useful for daily departmental production planning and budgeting. For this purpose, we re-estimated both the single and the multiple cost driver models 1 and 2 using daily data for only the first four years. We then obtained a prediction for the daily indirect production labor costs for the holdout year five for each production department based on its actual activity levels and the parameters estimates based on the first four years' data. We also predicted daily indirect production labor costs using the simple method described earlier that is currently in place at our research site. For this purpose, we multiply the daily direct production labor costs for each production department by the ratio of its total indirect production labor costs to its total direct production labor costs in the preceding year. Finally, we calculated mean absolute and squared percentage deviations for each department based on the daily cost prediction errors. Table 9 presents a comparison of the prediction errors using the three methods. The multiple cost driver model results in the lowest mean percentage absolute and squared deviations for five of the seven departments, while the single cost driver regression model performs the worst in all but one department. More interestingly, we find that the simple method used by the company predicts daily costs almost as well as our multiple cost driver regression model. This finding can be interpreted in two different ways. First, we may infer that the simple method of forecasting indirect production labor costs as a proportion of direct labor costs performs well even when multiple factors drive these indirect costs because direct labor costs are highly correlated with these other drivers. Alternatively, we may infer that managers assign resources to indirect production labor activities in the observed manner because the existing accounting system budgets resources in proportion to direct labor costs. It is, of course, impossible to discriminate between these two alternative inferences at our research site because the same accounting system has continued to be used throughout our sample period. Although, our findings seem to indicate that the traditional costing system performs as well as a sophisticated costing system for prediction/planning purpose, our earlier finding on cost distortions indicate that a costing system based on the hierarchy of cost drivers may be more useful for pricing, product mix and perhaps other decisions such as outsourcing. These mix results seem consistent with studies that find that only about 20% to 30% percent of firms adopt more elaborate costing systems (Innes et al. 2000; Schoute, 2011). The findings also echo the results of Ittner et al. (2002) who document that the extensive use of ABC by firms has no significant association with return on assets and that benefits may be contingent on firm characteristics. V. C oncluding R emarks Labro (2015) recently noted that compared to research on management controls, there is little research on information to support decision making, even though this is highly relevant to business practice and teaching. In the present study, we use time-series data from seven production departments of a manufacturing company to test the assumption that indirect production labor costs are not associated with other batch-related and product-sustaining activity cost drivers such as number of setups and number of distinct parts. We also test the assumption that indirect production labor costs are proportional to direct labor costs. The assumption that indirect production labor costs are proportional to a single unit-related cost driver, such as direct labor cost, is common in most traditional cost accounting systems. Our results document a strong relation between indirect production labor costs 13 Global Journal of Management and Business Research Volume XXI Issue II Version I Year 2021 ( ) D © 2021 Global Journals Cost Hierarchy: Evidence and Implications