Optimal cost driver selection in activity-based costing using Shuffled frog leaping algorithm

• Main Authors: Rungchat Chompu-Inwai, Trasapong Thaiupathump
• Format: Conference Proceeding
• Published: 2018
• Subjects: Decision Sciences; Engineering; Agricultural and Biological Sciences
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018951833&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/46509
• Institution: Chiang Mai University

© IEOM Society International. Activity-based costing (ABC) system more precisely allocates the overhead costs to cost objects (products, services or customers) than traditional costing systems. In ABC, resources are consumed by the activities, and multiple cost drivers are used to allocate the costs of activities to the products. The selection of activity and cost drivers is then highly significant. Using too few cost drivers may result in low level of accuracy in allocating the overhead costs. On the other hands, a high accuracy normally requires a large number of cost drivers which would be very time-consuming and expensive in data collection, processing, and reporting. Therefore, the trade-off between the product cost accuracy and the ABC complexity is crucial. Using appropriate number of cost drivers is required to achieve a satisfactory level of information cost and accuracy, as well as to make the ABC system simpler to implement. The cost-drivers optimization (CDO) problem focuses on selecting the representative cost drivers by considering the trade-off between the information-gathering costs and the benefits of precise costing. Recently, many approaches have been applied to solve the CDO problem. In this paper, Shuffled Frog Leaping Algorithm (SFLA), a meta-heuristic method for finding optimal solutions, is applied in selecting optimal representative cost drivers. The objective function of the algorithm is the cost saving from the information gathering cost of eliminated cost drivers minus the loss of accuracy cost. With computational results, SFLA can effectively find the optimal cost driver combination that has the optimal objective function value. Convergence performances of the best and average objective function value are presented.