ANNs in ABC Multi-driver Optimization Based on Thailand Automotive Industry

Authors

  • Noppadol Amdee Naresuan University
  • Kawin Sonthipermpoon Naresuan University
  • Chaitamlong Pongpattanasili Naresuan University
  • Kreangsak Tamee Naresuan University
  • Chonnanath Kritworakarn Chiang Mai University

DOI:

https://doi.org/10.4186/ej.2016.20.2.73

Keywords:

Activity Based Costing (ABC), Single Drivers Activity Based Costing (SDABC), Multiple Drivers Activity Based Costing (MDABC), Artificial Neural Networks (ANNs), Thailand Automotive Industry.

Abstract

The purpose of this research was to develop a method for Activity Based Costing (ABC) that provided accurate product production costs. ABC using Single Driver Activity Based Costing (SDABC) can result in distortion of the cost. A more accurate ABC cost calculation based on multiple cost drivers (CDs) in each activity has been devised and proven by considering the various cost drivers using the correlation coefficient or R2. The application of artificial neural networks (ANNs) to choose the CDs is Multiple Drivers Activity Based Costing (MDABC). The ANNs choose the CDs by algorithms including Multilayer Perceptron and Back-propagation. The transfer function for hidden layers is the Log-Sigmoid Function and for the output layer is the Pure Linear transfer function. The results have demonstrated that using MDABC results in more accurate cost calculations than when using SDABC. The study found that both of the extended ABC method, SDABC and MDABC provide more accurate actual cost of production, and both are applicable to products with low turnover or those in a state of loss condition. However, MDABC is better used in situations which include a variety of production activities, while the SDABC method is best used in situations of the factory operations not being very complex. Overall, the resolution, or accuracy, of the calculated production costs is better using the MDABC method, but is more complicated in its use and operation. Computer-based ANNs overcome this problem of complexity.

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Author Biographies

Noppadol Amdee

Department of Industrial Engineering, Naresuan University, Phitsanulok, Thailand
Department of Manufacturing Technology Faculty of Science and Technology Muban Chombueng Rajabhat University 46 Moo.3 Chombueng Subdistrict, Chombueng District, Ratchaburi Province 70150, Thailand

Kawin Sonthipermpoon

Department of Industrial Engineering, Naresuan University, Phitsanulok, Thailand

Chaitamlong Pongpattanasili

Department of Industrial Engineering, Naresuan University, Phitsanulok, Thailand

Kreangsak Tamee

Department of Computer Science and Information Technology, Naresuan University, Phitsanulok, Thailand

Chonnanath Kritworakarn

Department of Industrial Engineering, Chiang Mai University, Chiang Mai, Thailand

Published

Vol 20 No 2, May 18, 2016

How to Cite

[1]
N. Amdee, K. Sonthipermpoon, C. Pongpattanasili, K. Tamee, and C. Kritworakarn, “ANNs in ABC Multi-driver Optimization Based on Thailand Automotive Industry”, Eng. J., vol. 20, no. 2, pp. 73-87, May 2016.

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