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

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

An appropriate manufacturing strategy model for the Thai food processing industry

Thailand is well recognised for its high potential as an agricultural country and major food supplier. It provides a major source of agricultural product and agricultural manufacturing based industries. The most important sector is the food processing industry. This area is the most important sub-sector in Thai industry. The value in exports in the year 2002 was approximately 14.4% of the country’s total GDP. However, a high potential for growth still exists. Food production in Thailand is based on local agricultural raw materials, comprising of fruit and vegetables, cereal and oils, fish and livestock and provides employment to 20 million people within the agricultural and food processing sector. This thesis discusses the characteristics of the Thai food processing industry and suggests an appropriate manufacturing strategy model is needed in order for it to achieve manufacturing excellence. This study presents a classification of the Thai agricultural industry and compares one of its sectors, the agricultural manufacturing industry with another industrial sector viz. the electrical/electronic industry. The thesis investigates both industries contribution and reveals why the food processing industry is important for Thailand. After an overall analysis of the industry and its importance, the results of a survey of 350 food processing companies are compared, including characteristics of the Thai food processing industry, the focus of manufacturing strategies and the implementation of manufacturing practices. Based on this, and in order to assist the Thai food processing industry to achieve manufacturing excellence, the thesis then develops an integrated model combining GMP, HACCP, TPM and TQM, and provides guideline for their implementation