Classification decision model for a Hong Kong clothing manufacturing plant locations using an artificial neural network

Abstract

Clothing manufacturers’ direct investment and joint ventures in developing regions have seen to grow rapidly in the past few decades. Manufacturers face difficulties during the decision-making process in the selection of a plant location due to vague and subjective considerations. Selecting a plant location relies mostly on subjective intuition and assessment as variables to be considered in the decision making process. But these variables cannot always be represented in terms of objective value, such as country risk and community facilities. Though several optimization techniques have been developed to assist decision makers in searching for the optimal sites, it is difficult to rank the sites which display a small difference of scores. Classification is thus more reasonable and realistic. This paper investigates two recent types of classification techniques, namely unsupervised and supervised artificial neural networks, on the site selection problem of clothing manufacturing plants. The limitations of adaptive resonance theory in unsupervised artificial neural networks will be demonstrated. A comparison of the performance of the three types of supervised artificial neural networks – including back propagation, learning vector quantization and probabilistic neural network – is used and the proposed classification decision model will be presented. The experimental results indicate that the supervised artificial neural network is a proven and effective classifier in which a probabilistic neural network performs better than the others in this site selection problem.