To evaluate the effects of customers’ participation levels in various business activities on pricing in service-oriented manufacturing, the indices of pricing are proposed through extracting the influential factors in the four stages (i.e., design, manufacturing, production and services) from the whole value chain to comprehensively reflect customers’ demands. A new pricing model based on these indices is formulated by Support Vector Machine (SVM). It can predict a more accurate product price regarding the products’ similarity by the values of the influential factors that are determined in terms of business activities participated by customers. Finally, a case study from a molding company in China is conducted to verify the effectiveness of this pricing methodology. The results indicate that the model by SVM fares better in comparison with that by Back Propagation Neural Networks in small scale samples, especially in the performances of generalization and robustness. The outcomes also testify that this price prediction methodology can increase the accuracy of a product’s price as well as the customer’s satisfaction.

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