Application of a hybrid genetic algorithm and neural network approach in activity-based costing

Activity-based costing (ABC) has received extensive attention since it achieves improved accuracy in estimating costs, by using multiple cost drivers to trace the cost of activities to the products associated with the resources consumed by those activities. However, it has some problems. The first problem is that ABC does not have general criteria to select relevant cost drivers. Second, ABC assumes linearity between the uses of activities and the assigned quantities of indirect cost. When cost behavior shows a nonlinear pattern, conventional ABC may distort product costs. This paper proposes hybrid artificial intelligence techniques to resolve these two problems. Genetic algorithms are used to identify optimal or near-optimal cost drivers. In addition, artificial neural networks are employed to allocate indirect costs with nonlinear behavior to the products. Empirical results show that the proposed model outperforms the conventional model.