A Big Data based Cost Prediction Method for Remanufacturing End-of-Life Products

Zhouyang Ding, Zhigang Jiang, Ying Liu, Yan Wang, Congbo Li

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBN

Abstract

Remanufacturing is considered as an important industrial process to restore the performance and function of End-of-Life (EOL) products to a like-new state. In order to help enterprises effectively and precisely predict the cost of remanufacturing processes, a remanufacturing cost prediction model based on big data is developed. In this paper, a cost analysis framework is established by applying big data technologies to interpret the obtained data, identify the intricate relationship of obtained sensor data and its corresponding remanufacturing processes and associated costs. Then big data mining and particle swarm optimization Back Propagation (BP) neural network algorithm are utilized to implement the cost prediction. The application of presented model is verified by a case study, and the results demonstrates that the developed model can predict the cost of the remanufacturing accurately allowing early decision making for remanufacturability of the EOL products.

Original languageEnglish
Title of host publication51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018
PublisherElsevier
Pages1362-1367
Number of pages6
DOIs
Publication statusPublished - 27 Jun 2018
Event51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018 - Stockholm, Sweden
Duration: 16 May 201818 May 2018

Publication series

NameProcedia CIRP
Volume72
ISSN (Print)2212-8271

Conference

Conference51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018
CountrySweden
CityStockholm
Period16/05/1818/05/18

Keywords

  • Big Data
  • BP Neural Network
  • Cost Prediction
  • End-of-Life Products
  • Remanufacturing

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  • Cite this

    Ding, Z., Jiang, Z., Liu, Y., Wang, Y., & Li, C. (2018). A Big Data based Cost Prediction Method for Remanufacturing End-of-Life Products. In 51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018 (pp. 1362-1367). (Procedia CIRP; Vol. 72). Elsevier. https://doi.org/10.1016/j.procir.2018.03.129