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

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

doi:10.1016/j.procir.2018.03.129

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

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

Advanced Engineering Centre

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

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 language	English
Title of host publication	51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018
Publisher	Elsevier
Pages	1362-1367
Number of pages	6
DOIs	https://doi.org/10.1016/j.procir.2018.03.129
Publication status	Published - 27 Jun 2018
Event	51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018 - Stockholm, Sweden Duration: 16 May 2018 → 18 May 2018

Publication series

Name	Procedia CIRP
Volume	72
ISSN (Print)	2212-8271

Conference

Conference	51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018
Country/Territory	Sweden
City	Stockholm
Period	16/05/18 → 18/05/18

Keywords

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

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10.1016/j.procir.2018.03.129Licence: CC BY-NC-ND

A Big Data based Cost Prediction Method for Remanufacturing End-of-LifeProductsFinal published version, 1.59 MBLicence: CC BY-NC-ND

Cite this

@inproceedings{fb365650ae954019914de62000041145,

title = "A Big Data based Cost Prediction Method for Remanufacturing End-of-Life Products",

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.",

keywords = "Big Data, BP Neural Network, Cost Prediction, End-of-Life Products, Remanufacturing",

author = "Zhouyang Ding and Zhigang Jiang and Ying Liu and Yan Wang and Congbo Li",

year = "2018",

month = jun,

day = "27",

doi = "10.1016/j.procir.2018.03.129",

language = "English",

series = "Procedia CIRP",

publisher = "Elsevier",

pages = "1362--1367",

booktitle = "51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018",

note = "51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018 ; Conference date: 16-05-2018 Through 18-05-2018",

}

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. Procedia CIRP, vol. 72, Elsevier, pp. 1362-1367, 51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018, Stockholm, Sweden, 16/05/18. https://doi.org/10.1016/j.procir.2018.03.129

A Big Data based Cost Prediction Method for Remanufacturing End-of-Life Products. / Ding, Zhouyang; Jiang, Zhigang; Liu, Ying et al.
51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018. Elsevier, 2018. p. 1362-1367 (Procedia CIRP; Vol. 72).

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

TY - GEN

T1 - A Big Data based Cost Prediction Method for Remanufacturing End-of-Life Products

AU - Ding, Zhouyang

AU - Jiang, Zhigang

AU - Liu, Ying

AU - Wang, Yan

AU - Li, Congbo

PY - 2018/6/27

Y1 - 2018/6/27

N2 - 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.

AB - 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.

KW - Big Data

KW - BP Neural Network

KW - Cost Prediction

KW - End-of-Life Products

KW - Remanufacturing

UR - https://www.scopus.com/pages/publications/85049560123

U2 - 10.1016/j.procir.2018.03.129

DO - 10.1016/j.procir.2018.03.129

M3 - Conference contribution with ISSN or ISBN

AN - SCOPUS:85049560123

T3 - Procedia CIRP

SP - 1362

EP - 1367

BT - 51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018

PB - Elsevier

T2 - 51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018

Y2 - 16 May 2018 through 18 May 2018

ER -

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

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Yan Wang

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

Abstract

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Keywords

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Yan Wang

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