ANNs in ABC multi-driver optimization based on thailand automotive industry

• Main Authors: Noppadol Amdee, Kawin Sonthipermpoon, Chaitamlong Pongpattanasili, Kreangsak Tamee, Chonnanath Kritworakarn
• Format: Journal
• Published: 2018
• Subjects: Engineering
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84969130588&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/55752
• Institution: Chiang Mai University

© 2016, Chulalongkorn University 1. All right reserved. 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.