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

© 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...

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Main Authors: Chompu-Inwai R., Thaiupathump T.
Format: Conference Proceeding
Published: 2017
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018951833&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/40876
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-408762017-09-28T04:14:14Z Optimal cost driver selection in activity-based costing using Shuffled frog leaping algorithm Chompu-Inwai R. Thaiupathump T. © 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. 2017-09-28T04:14:14Z 2017-09-28T04:14:14Z 2017-01-01 Conference Proceeding 2-s2.0-85018951833 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018951833&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/40876
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
description © 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.
format Conference Proceeding
author Chompu-Inwai R.
Thaiupathump T.
spellingShingle Chompu-Inwai R.
Thaiupathump T.
Optimal cost driver selection in activity-based costing using Shuffled frog leaping algorithm
author_facet Chompu-Inwai R.
Thaiupathump T.
author_sort Chompu-Inwai R.
title Optimal cost driver selection in activity-based costing using Shuffled frog leaping algorithm
title_short Optimal cost driver selection in activity-based costing using Shuffled frog leaping algorithm
title_full Optimal cost driver selection in activity-based costing using Shuffled frog leaping algorithm
title_fullStr Optimal cost driver selection in activity-based costing using Shuffled frog leaping algorithm
title_full_unstemmed Optimal cost driver selection in activity-based costing using Shuffled frog leaping algorithm
title_sort optimal cost driver selection in activity-based costing using shuffled frog leaping algorithm
publishDate 2017
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018951833&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/40876
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