Evaluating Heuristics Used When Designing Product Costing Systems

Abstract

The academic and practitioner literature justifies firms' use of product costs in product pricing and capacity planning decisions as heuristics to address an otherwise intractable problem. However, product costs are the output of a cost reporting system, which itself is the outcome of heuristic design choices. In particular, because of informational limitations, when designing cost systems firms use simple rules of thumb to group resources into cost pools and to select drivers used to allocate the pooled costs to products. Using simulations, we examine how popular choices in costing system design influence the error in reported costs. Taking information needs into account, we offer alternative ways to translate the vague guidance in the literature to implementable methods. Specifically, we compare size-based rules for forming cost pools with more informationally demanding correlation-based rules and develop a blended method that performs well in terms of accuracy. In addition, our analysis suggests that significant gains can be made from using a composite driver rather than selecting a driver based on the consumption pattern for the largest resource only, especially when combined with correlation-based rules to group resources. We vary properties of the underlying cost structure (such as the skewness in resource costs, the traceability of resources to products, the sharing of resources across products, and the variance in resource consumption patterns) to address the generalizability of our findings and to show when different heuristics might be preferred. This paper was accepted by Stefan Reichelstein, accounting.