Applying shuffled frog leaping algorithm to find optimal cost driver combination weights in abc cost driver replacement

Abstract

Activity-based costing (ABC) is a well-known approach to allocate overhead costs to cost objects with higher accuracy than traditional costing approaches. High accuracy in allocating the overhead costs commonly requires a large number of cost drivers which is very time-consuming and expensive in data-related costs, such as collection, processing, and reporting. In contrast, using too few cost drivers may cause a low level of cost accuracy. The trade-off between the complexity of the ABC method and the product cost accuracy must be carefully considered. The cost-drivers optimization problem focusing on finding an appropriate group of representative cost drivers by considering the balance between the benefits against costs is significant and challenging. A cost driver is normally replaced by only one other cost driver; however, a cost driver may also be replaced by a proper combination of other cost drivers. With the same ABC system complexity, this approach yields a more accurate cost allocation. In this paper, Shuffled Frog Leaping Algorithm (SFLA), a meta-heuristic optimization approach, is applied to find the selected cost drivers and the weights of the combinations of selected cost drivers to be used in replacing the eliminated cost drivers.