File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1080/0951192X.2021.1901315
- Scopus: eid_2-s2.0-85103635909
- WOS: WOS:000636870200001
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: A hybrid machine learning method for procurement risk assessment of non-ferrous metals for manufacturing firms
Title | A hybrid machine learning method for procurement risk assessment of non-ferrous metals for manufacturing firms |
---|---|
Authors | |
Keywords | Material procurement risk assessment machine learning GARCH LSTM |
Issue Date | 2021 |
Publisher | Taylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/0951192X.asp |
Citation | International Journal of Computer Integrated Manufacturing, 2021, Epub 2021-04-05 How to Cite? |
Abstract | With the growing complexity of manufacturing systems nowadays, the effective assessment of important risk factors inherent in the manufacturing process is crucial for the stability and reliability of such complex systems. Thus, this article proposes a data-driven approach using the state-of-art machine learning techniques to assess and forecast the procurement risks of non-ferrous metals associated with complex manufacturing systems. A variety of state-of-art machine learning models including ANN, LSTM, BLSTM, GARCH, as well as their combinations which compose the proposed hybrid models, are deployed and analyzed. The testing results show that the proposed hybrid machine learning method can forecast the price uncertainty in procurement and effectively evaluate the procurement risk in a precautious manner. Moreover, it is shown that the hybrid model that combines GARCH, ANN, and LSTM significantly improves the forecasting results. Besides, the optimal choice of the network configurations in the hybrid model is also analyzed via a series of sensitivity analyses. This research can serve as a useful reference for the effective assessment and control of procurement risk for manufacturing firms. |
Persistent Identifier | http://hdl.handle.net/10722/298764 |
ISSN | 2021 Impact Factor: 4.420 2020 SCImago Journal Rankings: 0.884 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ni, J | - |
dc.contributor.author | Hu, Y | - |
dc.contributor.author | Zhong, RY | - |
dc.date.accessioned | 2021-04-12T03:03:03Z | - |
dc.date.available | 2021-04-12T03:03:03Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | International Journal of Computer Integrated Manufacturing, 2021, Epub 2021-04-05 | - |
dc.identifier.issn | 0951-192X | - |
dc.identifier.uri | http://hdl.handle.net/10722/298764 | - |
dc.description.abstract | With the growing complexity of manufacturing systems nowadays, the effective assessment of important risk factors inherent in the manufacturing process is crucial for the stability and reliability of such complex systems. Thus, this article proposes a data-driven approach using the state-of-art machine learning techniques to assess and forecast the procurement risks of non-ferrous metals associated with complex manufacturing systems. A variety of state-of-art machine learning models including ANN, LSTM, BLSTM, GARCH, as well as their combinations which compose the proposed hybrid models, are deployed and analyzed. The testing results show that the proposed hybrid machine learning method can forecast the price uncertainty in procurement and effectively evaluate the procurement risk in a precautious manner. Moreover, it is shown that the hybrid model that combines GARCH, ANN, and LSTM significantly improves the forecasting results. Besides, the optimal choice of the network configurations in the hybrid model is also analyzed via a series of sensitivity analyses. This research can serve as a useful reference for the effective assessment and control of procurement risk for manufacturing firms. | - |
dc.language | eng | - |
dc.publisher | Taylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/0951192X.asp | - |
dc.relation.ispartof | International Journal of Computer Integrated Manufacturing | - |
dc.rights | This is an Accepted Manuscript of an article published by Taylor & Francis in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/[Article DOI]. | - |
dc.subject | Material procurement | - |
dc.subject | risk assessment | - |
dc.subject | machine learning | - |
dc.subject | GARCH | - |
dc.subject | LSTM | - |
dc.title | A hybrid machine learning method for procurement risk assessment of non-ferrous metals for manufacturing firms | - |
dc.type | Article | - |
dc.identifier.email | Zhong, RY: zhongzry@hku.hk | - |
dc.identifier.authority | Zhong, RY=rp02116 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/0951192X.2021.1901315 | - |
dc.identifier.scopus | eid_2-s2.0-85103635909 | - |
dc.identifier.hkuros | 322149 | - |
dc.identifier.volume | Epub 2021-04-05 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 15 | - |
dc.identifier.isi | WOS:000636870200001 | - |
dc.publisher.place | United Kingdom | - |