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Pricing Method for Service-Oriented Manufacturing With Support Vector Machine

[+] Author Affiliations
Qiunan Meng, Jian Lou

Dalian University of Technology, Dalian, China

Xun Xu, Shiqiang Yu

University of Auckland, Auckland, New Zealand

Paper No. MSEC2017-2752, pp. V003T04A033; 8 pages
doi:10.1115/MSEC2017-2752
From:
  • ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing
  • Volume 3: Manufacturing Equipment and Systems
  • Los Angeles, California, USA, June 4–8, 2017
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5074-9
  • Copyright © 2017 by ASME

abstract

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.

Copyright © 2017 by ASME

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