Supply Chain Management in the Digital Economy: Case Studies of Deep Learning Technology Applications
Supply chain management (SCM) is pivotal in orchestrating the flow of goods and services from suppliers to consumers, fundamentally shaping business operations worldwide. However, traditional SCM faces significant limitations, such as inefficiencies in handling complex data structures and adapting t...
Gespeichert in:
| Veröffentlicht in: | Journal of global information management 2024-01, Vol.32 (1), p.1-27 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 27 |
|---|---|
| container_issue | 1 |
| container_start_page | 1 |
| container_title | Journal of global information management |
| container_volume | 32 |
| creator | Huang, Anzhong Zhuang, Jianming Ren, Yuheng Rao, Yun Tsai, Sangbing |
| description | Supply chain management (SCM) is pivotal in orchestrating the flow of goods and services from suppliers to consumers, fundamentally shaping business operations worldwide. However, traditional SCM faces significant limitations, such as inefficiencies in handling complex data structures and adapting to rapid market changes, which undermine operational effectiveness. The application of deep learning technologies in SCM is increasingly recognized as crucial, offering powerful tools for real-time visibility, predictive analytics, and enhanced decision-making capabilities. We propose a VAE-GNN-DRL network model that integrates Variational Autoencoder (VAE), Graph Neural Network (GNN), and Deep Reinforcement Learning (DRL) to address these challenges by efficiently processing and analyzing complex supply chain data. |
| doi_str_mv | 10.4018/JGIM.361589 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_gale_infotracacademiconefile_A820985711</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A820985711</galeid><sourcerecordid>A820985711</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2289-43702763c5c221d35908483699fbe1b30bb6e3623f58e46215434f7b47dd275a3</originalsourceid><addsrcrecordid>eNptkU1r3DAQhk1poWmaU_-AoMfijaSxbKu37SbNBxtySHoWsjz2KtiSK3kP_vdRuoUlsOggjXjmHYYny74xuiooqy_vb-4eVlAyUcsP2RkTAHklpfiY3rTkeQWV-Jx9ifGFUgq0YGcZPu2naVjIZqetIw_a6R5HdDNJ1bxDcmV7O-uBXBvv_Lj8JBsdkTzN-9ZiJL4jV4gT2aIOzrqePKPZOT_4fiHrlGuNnq138Wv2qdNDxIv_93n25_f18-Y23z7e3G3W29xwXsu8gIryqgQjUs1aEJLWRQ2llF2DrAHaNCVCyaETNRYlZ6KAoquaompbXgkN59n3Q-4U_N89xlm9-H1waaQCBlwAZ1QeqV4PqKzr_By0GW00al1zKmtRMZao_ATVo8OgB--ws-n7Hb86wafT4mjNyYYfhwYTfIwBOzUFO-qwKEbVm071plMddCb614FOQo5bDYv6Z04dzaXRat6diuDwCg9gpOg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3132532109</pqid></control><display><type>article</type><title>Supply Chain Management in the Digital Economy: Case Studies of Deep Learning Technology Applications</title><source>Alma/SFX Local Collection</source><creator>Huang, Anzhong ; Zhuang, Jianming ; Ren, Yuheng ; Rao, Yun ; Tsai, Sangbing</creator><creatorcontrib>Huang, Anzhong ; Zhuang, Jianming ; Ren, Yuheng ; Rao, Yun ; Tsai, Sangbing</creatorcontrib><description>Supply chain management (SCM) is pivotal in orchestrating the flow of goods and services from suppliers to consumers, fundamentally shaping business operations worldwide. However, traditional SCM faces significant limitations, such as inefficiencies in handling complex data structures and adapting to rapid market changes, which undermine operational effectiveness. The application of deep learning technologies in SCM is increasingly recognized as crucial, offering powerful tools for real-time visibility, predictive analytics, and enhanced decision-making capabilities. We propose a VAE-GNN-DRL network model that integrates Variational Autoencoder (VAE), Graph Neural Network (GNN), and Deep Reinforcement Learning (DRL) to address these challenges by efficiently processing and analyzing complex supply chain data.</description><identifier>ISSN: 1062-7375</identifier><identifier>EISSN: 1533-7995</identifier><identifier>DOI: 10.4018/JGIM.361589</identifier><language>eng</language><publisher>Hershey: IGI Global</publisher><subject>Accuracy ; Business operations ; Case studies ; Consumers ; Data structures ; Decision making ; Deep learning ; Digital economy ; Economic aspects ; Efficiency ; Graph neural networks ; Information management ; Internet of Things ; Inventory ; Inventory management ; Logistics ; Machine learning ; Neural networks ; Predictive analytics ; Real time ; Suppliers ; Supply chain management ; Supply chains</subject><ispartof>Journal of global information management, 2024-01, Vol.32 (1), p.1-27</ispartof><rights>COPYRIGHT 2024 IGI Global</rights><rights>2024. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2289-43702763c5c221d35908483699fbe1b30bb6e3623f58e46215434f7b47dd275a3</cites><orcidid>0009-0002-9679-9947 ; 0009-0009-9605-4233 ; 0000-0001-6988-5829</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Huang, Anzhong</creatorcontrib><creatorcontrib>Zhuang, Jianming</creatorcontrib><creatorcontrib>Ren, Yuheng</creatorcontrib><creatorcontrib>Rao, Yun</creatorcontrib><creatorcontrib>Tsai, Sangbing</creatorcontrib><title>Supply Chain Management in the Digital Economy: Case Studies of Deep Learning Technology Applications</title><title>Journal of global information management</title><description>Supply chain management (SCM) is pivotal in orchestrating the flow of goods and services from suppliers to consumers, fundamentally shaping business operations worldwide. However, traditional SCM faces significant limitations, such as inefficiencies in handling complex data structures and adapting to rapid market changes, which undermine operational effectiveness. The application of deep learning technologies in SCM is increasingly recognized as crucial, offering powerful tools for real-time visibility, predictive analytics, and enhanced decision-making capabilities. We propose a VAE-GNN-DRL network model that integrates Variational Autoencoder (VAE), Graph Neural Network (GNN), and Deep Reinforcement Learning (DRL) to address these challenges by efficiently processing and analyzing complex supply chain data.</description><subject>Accuracy</subject><subject>Business operations</subject><subject>Case studies</subject><subject>Consumers</subject><subject>Data structures</subject><subject>Decision making</subject><subject>Deep learning</subject><subject>Digital economy</subject><subject>Economic aspects</subject><subject>Efficiency</subject><subject>Graph neural networks</subject><subject>Information management</subject><subject>Internet of Things</subject><subject>Inventory</subject><subject>Inventory management</subject><subject>Logistics</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Predictive analytics</subject><subject>Real time</subject><subject>Suppliers</subject><subject>Supply chain management</subject><subject>Supply chains</subject><issn>1062-7375</issn><issn>1533-7995</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNptkU1r3DAQhk1poWmaU_-AoMfijaSxbKu37SbNBxtySHoWsjz2KtiSK3kP_vdRuoUlsOggjXjmHYYny74xuiooqy_vb-4eVlAyUcsP2RkTAHklpfiY3rTkeQWV-Jx9ifGFUgq0YGcZPu2naVjIZqetIw_a6R5HdDNJ1bxDcmV7O-uBXBvv_Lj8JBsdkTzN-9ZiJL4jV4gT2aIOzrqePKPZOT_4fiHrlGuNnq138Wv2qdNDxIv_93n25_f18-Y23z7e3G3W29xwXsu8gIryqgQjUs1aEJLWRQ2llF2DrAHaNCVCyaETNRYlZ6KAoquaompbXgkN59n3Q-4U_N89xlm9-H1waaQCBlwAZ1QeqV4PqKzr_By0GW00al1zKmtRMZao_ATVo8OgB--ws-n7Hb86wafT4mjNyYYfhwYTfIwBOzUFO-qwKEbVm071plMddCb614FOQo5bDYv6Z04dzaXRat6diuDwCg9gpOg</recordid><startdate>20240101</startdate><enddate>20240101</enddate><creator>Huang, Anzhong</creator><creator>Zhuang, Jianming</creator><creator>Ren, Yuheng</creator><creator>Rao, Yun</creator><creator>Tsai, Sangbing</creator><general>IGI Global</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X5</scope><scope>7XB</scope><scope>87Z</scope><scope>88K</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CNYFK</scope><scope>DWQXO</scope><scope>E3H</scope><scope>F2A</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M1O</scope><scope>M2T</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope><orcidid>https://orcid.org/0009-0002-9679-9947</orcidid><orcidid>https://orcid.org/0009-0009-9605-4233</orcidid><orcidid>https://orcid.org/0000-0001-6988-5829</orcidid></search><sort><creationdate>20240101</creationdate><title>Supply Chain Management in the Digital Economy: Case Studies of Deep Learning Technology Applications</title><author>Huang, Anzhong ; Zhuang, Jianming ; Ren, Yuheng ; Rao, Yun ; Tsai, Sangbing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2289-43702763c5c221d35908483699fbe1b30bb6e3623f58e46215434f7b47dd275a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Business operations</topic><topic>Case studies</topic><topic>Consumers</topic><topic>Data structures</topic><topic>Decision making</topic><topic>Deep learning</topic><topic>Digital economy</topic><topic>Economic aspects</topic><topic>Efficiency</topic><topic>Graph neural networks</topic><topic>Information management</topic><topic>Internet of Things</topic><topic>Inventory</topic><topic>Inventory management</topic><topic>Logistics</topic><topic>Machine learning</topic><topic>Neural networks</topic><topic>Predictive analytics</topic><topic>Real time</topic><topic>Suppliers</topic><topic>Supply chain management</topic><topic>Supply chains</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Huang, Anzhong</creatorcontrib><creatorcontrib>Zhuang, Jianming</creatorcontrib><creatorcontrib>Ren, Yuheng</creatorcontrib><creatorcontrib>Rao, Yun</creatorcontrib><creatorcontrib>Tsai, Sangbing</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Proquest Entrepreneurship</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection</collection><collection>Telecommunications (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Social Science Premium Collection</collection><collection>Advanced Technologies & Aerospace Database (1962 - current)</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Library & Information Science Collection</collection><collection>ProQuest Central</collection><collection>Library & Information Sciences Abstracts (LISA)</collection><collection>Library & Information Science Abstracts (LISA)</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Library Science Database</collection><collection>Telecommunications Database</collection><collection>Engineering Database</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection><collection>ProQuest Central Basic</collection><jtitle>Journal of global information management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Huang, Anzhong</au><au>Zhuang, Jianming</au><au>Ren, Yuheng</au><au>Rao, Yun</au><au>Tsai, Sangbing</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Supply Chain Management in the Digital Economy: Case Studies of Deep Learning Technology Applications</atitle><jtitle>Journal of global information management</jtitle><date>2024-01-01</date><risdate>2024</risdate><volume>32</volume><issue>1</issue><spage>1</spage><epage>27</epage><pages>1-27</pages><issn>1062-7375</issn><eissn>1533-7995</eissn><abstract>Supply chain management (SCM) is pivotal in orchestrating the flow of goods and services from suppliers to consumers, fundamentally shaping business operations worldwide. However, traditional SCM faces significant limitations, such as inefficiencies in handling complex data structures and adapting to rapid market changes, which undermine operational effectiveness. The application of deep learning technologies in SCM is increasingly recognized as crucial, offering powerful tools for real-time visibility, predictive analytics, and enhanced decision-making capabilities. We propose a VAE-GNN-DRL network model that integrates Variational Autoencoder (VAE), Graph Neural Network (GNN), and Deep Reinforcement Learning (DRL) to address these challenges by efficiently processing and analyzing complex supply chain data.</abstract><cop>Hershey</cop><pub>IGI Global</pub><doi>10.4018/JGIM.361589</doi><tpages>27</tpages><orcidid>https://orcid.org/0009-0002-9679-9947</orcidid><orcidid>https://orcid.org/0009-0009-9605-4233</orcidid><orcidid>https://orcid.org/0000-0001-6988-5829</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1062-7375 |
| ispartof | Journal of global information management, 2024-01, Vol.32 (1), p.1-27 |
| issn | 1062-7375 1533-7995 |
| language | eng |
| recordid | cdi_gale_infotracacademiconefile_A820985711 |
| source | Alma/SFX Local Collection |
| subjects | Accuracy Business operations Case studies Consumers Data structures Decision making Deep learning Digital economy Economic aspects Efficiency Graph neural networks Information management Internet of Things Inventory Inventory management Logistics Machine learning Neural networks Predictive analytics Real time Suppliers Supply chain management Supply chains |
| title | Supply Chain Management in the Digital Economy: Case Studies of Deep Learning Technology Applications |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-04T18%3A00%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Supply%20Chain%20Management%20in%20the%20Digital%20Economy:%20Case%20Studies%20of%20Deep%20Learning%20Technology%20Applications&rft.jtitle=Journal%20of%20global%20information%20management&rft.au=Huang,%20Anzhong&rft.date=2024-01-01&rft.volume=32&rft.issue=1&rft.spage=1&rft.epage=27&rft.pages=1-27&rft.issn=1062-7375&rft.eissn=1533-7995&rft_id=info:doi/10.4018/JGIM.361589&rft_dat=%3Cgale_proqu%3EA820985711%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3132532109&rft_id=info:pmid/&rft_galeid=A820985711&rfr_iscdi=true |