Using Artificial Neural Networks for Detemining Optimal Cost Drivers in Activity-Based Costing

Despite inattention to the application of Artificial Neural Networks (ANNs) in Activity-Based Costing (ABC) until now, employing ANNs in this field may have numerous advantages. Considering the capability of ANNs for optimizing both linear and nonlinear problems, they are good candidates for solving two problems of optimal cost driver determination in ABC. As for the first one is related to the Cost Driver Optimization (CDO) and for the second one i.e., Cost Estimation Relationship (CER) problem. .Hence, in this paper, a new procedure for nonlinear cost allocation and determining the optimal cost driver employing ANNs is presented. The proposed procedure is implemented in Export Development Bank of Iran(EDBI). The results of the implementation of the proposed procedure have been compared with the results of using conventional method from 1999 to 2008. In addition, the durability of the selected optimal cost drivers within this period has been discussed.

Keywords:

Activity-Based Costing , Artificial Neural Networks , EDBI , Cost Driver