Ittner, C. D., D. F. Larcker and T. Randall. 1997. The activity-based cost hierarchy, production policies and firm profitability. Journal of Management Accounting Research (9): 143-162.
Summary by Anita Reed
Ph.D. Program in Accounting
University of South Florida, Spring 2002
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The authors are interested in exploring the extent to which common measures of manufacturing activity actually correspond to the cost hierarchy classifications established by Cooper and Kaplanís work in activity based costing, as well as examining how the association among the cost hierarchy levels are affected by the production policies of an organization and the effects of the measures on revenues.
OVERVIEW OF THE ACTIVITY-BASED HIERARCHY
Previous work by Cooper and Kaplan proposes a cost hierarchy framework that maintains that costs are driven by, and are variable with respect to, activities that occur at four levels:
Unit Level: Activities performed in proportion to the volume of units produced.
Batch Level: Activities performed in proportion to the number of production or support batches, independent of the number of unites produced in the batch.
Product sustaining Level: Activities performed to support a product portfolio, independent of the number of units produced or production batches.
Facility sustaining Level: Activities performed to maintain a facility, independent of the number of units produced, production batches and products carried by the facility.
Little research has been conducted to examine the relationship between the various levels and the production decisions of organizations regarding product mix and production level. In addition, little research has been done to examine the revenue implications of production decisions.
The lack of research and consistent evidence regarding the relationships among the levels of the hierarchy and the effects on revenue lead to three research questions:
1. To what extent do common measures of manufacturing activity correspond to the cost hierarchy classifications?
2. How are the associations among the cost hierarchy classifications affected by the firmís production policies?
3. To what extent do the cost hierarchy classifications explain variations in costs, revenues and profits?
The sample for the study was a manufacturer of outdoor packs, producing four related product lines at a single facility located in the U.S. The manufacturer uses a make-to-order system with little finished goods inventory, thus the costs and revenues for each month are closely matched. Longitudinal data was collected from the monthly cost and revenue data for the 41-month period from July 1992 to November 1995.
Production cost pools consist of assembly, cutting, direct materials, kitting, quality control and sewing. Non-production cost pools consist of accounting, benefits, depreciation, general and administrative activities, marketing, planning, procurement and shipping. Cost and revenue data are adjusted to control for inflation and labor is deflated for raises given over the data-collection period. The manufacturer tracks 14 operational measures related to factors that potentially drive costs. These are shown on Table 2, pg.152.
Longitudinal data collected from a single organization. Principle components analysis is used to address research question 1. Regression analysis is used to address research questions 2 and 3.
Research question 1 was addressed by using principle components analysis on the 14 operational measures from Table 2 to determine the extent to which these measures correspond to the first three hierarchy dimensions: unit level, batch level and product sustaining level (facility sustaining level is not relevant as only one location). An oblique rotation was used to allow correlation of the resulting factors. Three factors emerged from the analysis, with the model retaining 80% of the total variance. The three emergent factors corresponded with the three hierarchy dimensions. Table 3 provides details of the loading of the operational measures on the underlying factors. High cross-loadings of various measures indicated interrelations among the three underlying factors. The Pearson correlations show a negative correlation between the Batch and Product factors, indicating that these two factors are interrelated and the measures associated with each affect operations associated with multiple levels of the cost hierarchy.
Research question 2 was addressed using regression analysis. Independent variables were factor scores from the principal components analysis and dependent variables were the monthly expenditures reported in each of the production and non-production cost pools. The coefficients on UNIT are positive and statistically significant in all production models, indicating production volume is a significant predictor of costs. The BATCH and PRODUCT variables indicated support for the relation between manufacturing costs and cost drivers corresponding to the cost hierarchy classifications, but are not consistent across all cost drivers. Table 4 provides the details of the regression models, coefficients, and R2s.
Research question 3 was also addressed using regression analysis. Independent variables are the revenue and profits reported by month. The dependent variables are the principle component scores for UNIT, BATCH, and PRODUCT and an additional variable $/ORDER to proxy for the average purchase price of product mix for each month. Table 5 provides the details of the regression models, coefficients and R2s. The analysis indicates that all variables are significant predictors of revenue except for BATCH, and that only BATCH and $/ORDER are significant with respect to profits. This indicates that the cost hierarchy classifications provide explanatory power with regard to revenue changes in the organization. The results of the profit regression indicate that the firmís revenue gains from higher sales volumes and broader product lines were offset by increased costs, indicating that on average the firm offered greater product variety than optimal and that profits came from sales of the more expensive product lines.
The analysis of principle components indicates the operational measures used by the organization correspond to the cost hierarchy classifications, though inconsistent with some prior findings. Additional findings of relationships between particular cost drivers and the organizationís production and inventory policies indicate that the cost driver analysis should be tailored to the production environment. Unit-related measures have the greatest explanatory power, as indicated by the regression models developed to address research questions 2 and 3. This may be impacted by the difficulty in detecting cost behavior changes using relatively short periods of longitudinal data.
Single organization, auto-correlation and serial correlation of longitudinal data, correlated omitted variables, and short time period of date collection.
FUTURE RESEARCH OPPORTUNITIES
The authors identify several areas for additional study:
Examining the interdependencies among the various cost hierarchy levels when making production decisions.
Development of more appropriate proxies for the various cost hierarchy levels in different production environments.
Comparison of "structural" cost drivers to "executional" cost drivers to determine which may be the most useful for production decisions.