Abstract
Implementing target cost control at the design stage can better reduce the cost. However, for automakers in the Chinese market, no adequate attention is paid to the target cost control during design stage for various reasons. Among these reasons, the lack of an effective cost control tool is a substantial one. In this study, a target cost control method is proposed and artificial intelligence is employed for new product cost reduction. At the early design stage, Back Propagation (BP) neural network is introduced to estimate and evaluate the target cost of different designs. Consequently, a cost saving design can be chosen. The target cost can be mainly achieved through procurement cost control. A procurement model is designed for balancing procurement cost reduction and supplier satisfaction. To search the optimal solution for this model, genetic algorithm is introduced. A case study of the proposed method in a Chinese automobile company is also discussed .
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Ju, B., Xi, L., Zhou, X. (2009). New Product Design Based Target Cost Control with BP Neural Network and Genetic Algorithm - A Case Study in Chinese Automobile Industry. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2009. Lecture Notes in Computer Science, vol 5821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04843-2_13
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DOI: https://doi.org/10.1007/978-3-642-04843-2_13
Publisher Name: Springer, Berlin, Heidelberg
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