Federated machine learning for privacy preserving, collective supply chain risk prediction

The use of Artificial Intelligence (AI) for predicting supply chain risk has gained popularity. However, proposed approaches are based on the premise that organisations act alone, rather than a collective when predicting risk, despite the interconnected nature of supply chains. This yields a problem...

Full description

Saved in:

Bibliographic Details
Published in:	International journal of production research Vol. 61; no. 23; pp. 8115 - 8132
Main Authors:	Zheng, Ge, Kong, Lingxuan, Brintrup, Alexandra
Format:	Journal Article
Language:	English
Published:	London Taylor & Francis 02.12.2023 Taylor & Francis LLC
Subjects:	Algorithms Artificial intelligence Datasets federated learning Machine learning Predictions Privacy privacy preserving Risk Supply chain Supply chains
ISSN:	0020-7543, 1366-588X
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	The use of Artificial Intelligence (AI) for predicting supply chain risk has gained popularity. However, proposed approaches are based on the premise that organisations act alone, rather than a collective when predicting risk, despite the interconnected nature of supply chains. This yields a problem: organisations that have inadequate datasets cannot predict risk. While data-sharing has been proposed to evaluate risk, in practice this does not happen due to privacy concerns. We propose a federated learning approach for collective risk prediction without the risk of data exposure. We ask: Can organisations who have inadequate datasets tap into collective knowledge? This raises a second question: Under what circumstances would collective risk prediction be beneficial? We present an empirical case study where buyers predict order delays from their shared suppliers before and after Covid-19. Results show that federated learning can indeed help supply chain members predict risk effectively, especially for buyers with limited datasets. Training data-imbalance, disruptions, and algorithm choice are significant factors in the efficacy of this approach. Interestingly, data-sharing or collective risk prediction is not always the best choice for buyers with disproportionately larger order-books. We thus call for further research on on local and collective learning paradigms in supply chains.
AbstractList	The use of Artificial Intelligence (AI) for predicting supply chain risk has gained popularity. However, proposed approaches are based on the premise that organisations act alone, rather than a collective when predicting risk, despite the interconnected nature of supply chains. This yields a problem: organisations that have inadequate datasets cannot predict risk. While data-sharing has been proposed to evaluate risk, in practice this does not happen due to privacy concerns. We propose a federated learning approach for collective risk prediction without the risk of data exposure. We ask: Can organisations who have inadequate datasets tap into collective knowledge? This raises a second question: Under what circumstances would collective risk prediction be beneficial? We present an empirical case study where buyers predict order delays from their shared suppliers before and after Covid-19. Results show that federated learning can indeed help supply chain members predict risk effectively, especially for buyers with limited datasets. Training data-imbalance, disruptions, and algorithm choice are significant factors in the efficacy of this approach. Interestingly, data-sharing or collective risk prediction is not always the best choice for buyers with disproportionately larger order-books. We thus call for further research on on local and collective learning paradigms in supply chains.
Author	Zheng, Ge Kong, Lingxuan Brintrup, Alexandra
Author_xml	– sequence: 1 givenname: Ge surname: Zheng fullname: Zheng, Ge organization: Institute for Manufacturing, University of Cambridge – sequence: 2 givenname: Lingxuan surname: Kong fullname: Kong, Lingxuan organization: Institute for Manufacturing, University of Cambridge – sequence: 3 givenname: Alexandra orcidid: 0000-0002-4189-2434 surname: Brintrup fullname: Brintrup, Alexandra email: ab702@cam.ac.uk organization: Institute for Manufacturing, University of Cambridge
BookMark	eNqFkEFLwzAUx4NMcJt-BKHg1c4kTdIML8pwKgheFMRLSNNXF-2SmXSTfXtTNi8eNAQeJL__S95vhAbOO0DolOAJwRJfYExxyVkxoZjSCSWCCSoP0JAUQuRcypcBGvZM3kNHaBTjO06LSzZEr3OoIegO6mypzcI6yFrQwVn3ljU-ZKtgN9psU4UIYZOOzzPj2xZMZzeQxfVq1W4zs9DWZcHGjx6sbbr07hgdNrqNcLKvY_Q8v3ma3eUPj7f3s-uH3DBGu5xVZVUaSN8RmE7ltKTEsJqlDdxUsgQGdVE1vCnryghaaUE4oZUoK4E1B1KM0dmu7yr4zzXETr37dXDpSUWl5CUmjE8TxXeUCT7GAI1Koy112CqCVa9R_WhUvUa115hyl79yxna6n68L2rb_pq92aeuSzaX-8qGtVae3rQ9N0M7YqIq_W3wD4e2OPg
CitedBy_id	crossref_primary_10_1080_17452007_2025_2478021 crossref_primary_10_1016_j_physa_2025_130780 crossref_primary_10_1016_j_ijpe_2024_109368 crossref_primary_10_1016_j_cie_2024_110590 crossref_primary_10_1080_00207543_2024_2432469 crossref_primary_10_1109_ACCESS_2023_3283503 crossref_primary_10_1007_s11301_025_00549_x crossref_primary_10_1080_00207543_2023_2253328 crossref_primary_10_1108_BPMJ_03_2024_0174 crossref_primary_10_1109_ACCESS_2024_3508030 crossref_primary_10_1080_00207543_2024_2448604 crossref_primary_10_1093_jcde_qwaf068 crossref_primary_10_1016_j_sca_2025_100135 crossref_primary_10_1108_MSCRA_10_2024_0041 crossref_primary_10_1108_JMTM_07_2024_0379 crossref_primary_10_3390_info16050399 crossref_primary_10_1016_j_sca_2025_100138 crossref_primary_10_1016_j_ijpe_2023_109130 crossref_primary_10_1016_j_foohum_2025_100542 crossref_primary_10_1080_00207543_2025_2458121 crossref_primary_10_1016_j_sca_2025_100130 crossref_primary_10_1080_00207543_2024_2334420 crossref_primary_10_1038_s41598_025_11510_z crossref_primary_10_3390_math11173759 crossref_primary_10_1080_00207543_2024_2367685 crossref_primary_10_1186_s40537_025_01143_4 crossref_primary_10_1016_j_neucom_2025_131337 crossref_primary_10_1007_s10479_023_05477_1 crossref_primary_10_1002_qre_3678 crossref_primary_10_1016_j_compind_2024_104132 crossref_primary_10_3390_app14114688 crossref_primary_10_1080_00207543_2025_2543964 crossref_primary_10_1007_s11431_024_2943_2 crossref_primary_10_1016_j_tre_2024_103526 crossref_primary_10_1016_j_ijpe_2023_108938 crossref_primary_10_1080_00207543_2024_2361434 crossref_primary_10_3390_s24216814 crossref_primary_10_1016_j_jclepro_2023_139069 crossref_primary_10_1016_j_omega_2024_103081 crossref_primary_10_1080_00207543_2023_2244604 crossref_primary_10_1016_j_asoc_2024_112475 crossref_primary_10_1108_JMTM_09_2023_0431 crossref_primary_10_1016_j_eij_2025_100704 crossref_primary_10_1016_j_knosys_2025_114315 crossref_primary_10_1080_00207543_2023_2270719 crossref_primary_10_1016_j_pursup_2025_101014 crossref_primary_10_1109_JSEN_2024_3465593 crossref_primary_10_1109_ACCESS_2024_3445281 crossref_primary_10_1080_00207543_2024_2447927 crossref_primary_10_4018_IRMJ_358747
Cites_doi	10.1287/mnsc.45.8.1091 10.1016/j.cie.2022.108206 10.1109/JIoT.6488907 10.1016/j.energy.2020.119712 10.5120/ijca2017915495 10.1109/TII.9424 10.1109/Access.6287639 10.1016/j.ijmedinf.2018.01.007 10.1080/00207543.2021.1840148 10.2139/ssrn.4118108 10.1080/00207543.2020.1720925 10.1108/14637150810888019 10.1038/s41591-021-01506-3 10.1504/IJISM.2022.125995 10.1016/j.protcy.2013.12.194 10.1111/jbl.2019.40.issue-1 10.1007/s41109-017-0058-8 10.1016/j.knosys.2021.106775 10.1109/BigComp51126.2021.00039 10.1007/s00521-021-06007-5 10.1108/09574091211289200 10.1108/09574090410700275 10.1093/oxfordhb/9780190066727.013.24 10.1080/00207543.2020.1750727 10.1007/s12247-019-09396-2 10.1109/JSEN.2021.3122258 10.1080/00207543.2022.2093682 10.1108/JEIM-04-2019-0104 10.1016/B978-0-12-818366-3.00005-8 10.1080/00207543.2017.1421787 10.1038/s41562-020-0896-8 10.1080/00207543.2022.2118892 10.1007/978-981-13-6661-1_6 10.1016/j.cie.2014.04.019 10.1038/s41598-020-69250-1 10.1080/00207543.2020.1712491 10.1016/j.ifacol.2022.10.084 10.1016/B978-0-323-91614-1.00022-8 10.1007/s12063-021-00194-z 10.1007/978-3-031-08871-1_9 10.1007/s10845-014-0953-0 10.1109/OAJPE.2022.3206220 10.1016/j.ijpe.2021.108279 10.1109/ICASSP.2013.6638963 10.1080/00207543.2018.1530476 10.1080/00207543.2019.1685705 10.1016/j.ijpe.2014.12.037 10.1007/978-3-030-23551-2_2
ContentType	Journal Article
Copyright	2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2023 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License http://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.
Copyright_xml	– notice: 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2023 – notice: 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License http://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.
DBID	0YH AAYXX CITATION 7SC 8FD F28 FR3 JQ2 L7M L~C L~D
DOI	10.1080/00207543.2022.2164628
DatabaseName	Taylor & Francis Open Access CrossRef Computer and Information Systems Abstracts Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Computer and Information Systems Abstracts Professional
DatabaseTitleList	Technology Research Database
Database_xml	– sequence: 1 dbid: 0YH name: Taylor & Francis Open Access url: https://www.tandfonline.com sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1366-588X
EndPage	8132
ExternalDocumentID	10_1080_00207543_2022_2164628 2164628
Genre	Research Article
GroupedDBID	-~X .7F .QJ 0BK 0R~ 0YH 29J 2DF 30N 4.4 5GY 5VS 8VB A8Z AAENE AAGDL AAHIA AAJMT AALDU AAMIU AAPUL AAQRR ABCCY ABFIM ABHAV ABJNI ABLIJ ABPAQ ABPEM ABTAI ABXUL ABXYU ACGEJ ACGFO ACGFS ACGOD ACIWK ACNCT ACTIO ADCVX ADGTB ADXPE AEGXH AEISY AEMOZ AENEX AEOZL AEPSL AEYOC AFKVX AFRVT AGDLA AGMYJ AHDZW AHQJS AIAGR AIJEM AIYEW AJWEG AKBVH AKOOK AKVCP ALMA_UNASSIGNED_HOLDINGS ALQZU AQRUH AQTUD AVBZW AWYRJ BLEHA CCCUG CE4 CS3 DGEBU DKSSO DU5 EBD EBE EBO EBR EBS EBU EMK EPL ESTFP E~A E~B GTTXZ H13 HF~ HZ~ H~9 H~P I-F IPNFZ J.P K1G KYCEM LJTGL M4Z ML~ NA5 NX~ O9- P2P PQQKQ QWB RIG RNANH ROSJB RTWRZ S-T SNACF TASJS TBQAZ TDBHL TEN TFL TFT TFW TH9 TN5 TNC TTHFI TUROJ TWF UT5 UU3 ZGOLN ZL0 ~S~ AAYXX CITATION 7SC 8FD F28 FR3 JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c442t-4b7b7ce058602989721c4d44d4e5cb87e4ed3bf5f7dbc62ba61512b67b60a5e13
IEDL.DBID	0YH
ISICitedReferencesCount	68
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000926926500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0020-7543
IngestDate	Mon Nov 10 03:01:03 EST 2025 Sat Nov 29 05:36:24 EST 2025 Tue Nov 18 21:50:38 EST 2025 Mon Oct 20 23:45:09 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	23
Language	English
License	open-access: http://creativecommons.org/licenses/by/4.0/: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c442t-4b7b7ce058602989721c4d44d4e5cb87e4ed3bf5f7dbc62ba61512b67b60a5e13
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-4189-2434
OpenAccessLink	https://www.tandfonline.com/doi/abs/10.1080/00207543.2022.2164628
PQID	2885701459
PQPubID	30924
PageCount	18
ParticipantIDs	proquest_journals_2885701459 informaworld_taylorfrancis_310_1080_00207543_2022_2164628 crossref_primary_10_1080_00207543_2022_2164628 crossref_citationtrail_10_1080_00207543_2022_2164628
PublicationCentury	2000
PublicationDate	2023-12-02
PublicationDateYYYYMMDD	2023-12-02
PublicationDate_xml	– month: 12 year: 2023 text: 2023-12-02 day: 02
PublicationDecade	2020
PublicationPlace	London
PublicationPlace_xml	– name: London
PublicationTitle	International journal of production research
PublicationYear	2023
Publisher	Taylor & Francis Taylor & Francis LLC
Publisher_xml	– name: Taylor & Francis – name: Taylor & Francis LLC
References	e_1_3_3_52_1 e_1_3_3_50_1 Taheri M. (e_1_3_3_55_1) 2021; 9 Zhang Z. (e_1_3_3_63_1) 2018; 31 e_1_3_3_18_1 e_1_3_3_39_1 e_1_3_3_14_1 Smith V. (e_1_3_3_53_1) 2017; 30 e_1_3_3_16_1 e_1_3_3_35_1 e_1_3_3_58_1 e_1_3_3_10_1 e_1_3_3_56_1 e_1_3_3_12_1 e_1_3_3_31_1 e_1_3_3_54_1 e_1_3_3_40_1 e_1_3_3_61_1 e_1_3_3_7_1 e_1_3_3_9_1 e_1_3_3_29_1 Lee H. L. (e_1_3_3_37_1) 1997; 38 e_1_3_3_25_1 e_1_3_3_48_1 e_1_3_3_27_1 e_1_3_3_46_1 e_1_3_3_3_1 e_1_3_3_21_1 e_1_3_3_44_1 e_1_3_3_5_1 e_1_3_3_23_1 e_1_3_3_42_1 e_1_3_3_65_1 e_1_3_3_30_1 e_1_3_3_51_1 e_1_3_3_17_1 e_1_3_3_19_1 e_1_3_3_13_1 e_1_3_3_38_1 e_1_3_3_59_1 e_1_3_3_15_1 e_1_3_3_36_1 e_1_3_3_57_1 e_1_3_3_34_1 e_1_3_3_11_1 e_1_3_3_32_1 e_1_3_3_41_1 e_1_3_3_62_1 e_1_3_3_60_1 Kawa D. (e_1_3_3_33_1) 2019; 6 e_1_3_3_6_1 e_1_3_3_8_1 e_1_3_3_28_1 e_1_3_3_24_1 e_1_3_3_49_1 e_1_3_3_26_1 e_1_3_3_47_1 e_1_3_3_2_1 e_1_3_3_20_1 e_1_3_3_45_1 e_1_3_3_4_1 e_1_3_3_22_1 e_1_3_3_43_1 e_1_3_3_64_1
References_xml	– ident: e_1_3_3_11_1 doi: 10.1287/mnsc.45.8.1091 – ident: e_1_3_3_22_1 doi: 10.1016/j.cie.2022.108206 – ident: e_1_3_3_36_1 doi: 10.1109/JIoT.6488907 – ident: e_1_3_3_14_1 doi: 10.1016/j.energy.2020.119712 – ident: e_1_3_3_38_1 – ident: e_1_3_3_47_1 doi: 10.5120/ijca2017915495 – ident: e_1_3_3_41_1 doi: 10.1109/TII.9424 – ident: e_1_3_3_48_1 doi: 10.1109/Access.6287639 – ident: e_1_3_3_10_1 doi: 10.1016/j.ijmedinf.2018.01.007 – ident: e_1_3_3_16_1 – volume: 38 start-page: 93 year: 1997 ident: e_1_3_3_37_1 article-title: The Bullwhip Effect in Supply Chains publication-title: Sloan Management Review – ident: e_1_3_3_19_1 doi: 10.1080/00207543.2021.1840148 – ident: e_1_3_3_50_1 doi: 10.1109/JIoT.6488907 – ident: e_1_3_3_62_1 – ident: e_1_3_3_3_1 doi: 10.2139/ssrn.4118108 – ident: e_1_3_3_58_1 doi: 10.1080/00207543.2020.1720925 – ident: e_1_3_3_24_1 doi: 10.1108/14637150810888019 – ident: e_1_3_3_18_1 doi: 10.1038/s41591-021-01506-3 – volume: 6 start-page: 1355 issue: 4 year: 2019 ident: e_1_3_3_33_1 article-title: Credit Risk Assessment From Combined Bank Records Using Federated Learning publication-title: International Research Journal of Engineering and Technology (IRJET) – ident: e_1_3_3_29_1 doi: 10.1504/IJISM.2022.125995 – ident: e_1_3_3_40_1 doi: 10.1016/j.protcy.2013.12.194 – ident: e_1_3_3_46_1 doi: 10.1111/jbl.2019.40.issue-1 – ident: e_1_3_3_9_1 doi: 10.1007/s41109-017-0058-8 – ident: e_1_3_3_64_1 doi: 10.1016/j.knosys.2021.106775 – ident: e_1_3_3_56_1 doi: 10.1109/BigComp51126.2021.00039 – ident: e_1_3_3_26_1 doi: 10.1007/s00521-021-06007-5 – volume: 31 year: 2018 ident: e_1_3_3_63_1 article-title: Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels publication-title: Advances in Neural Information Processing Systems – ident: e_1_3_3_23_1 doi: 10.1108/09574091211289200 – ident: e_1_3_3_17_1 doi: 10.1108/09574090410700275 – ident: e_1_3_3_6_1 doi: 10.1093/oxfordhb/9780190066727.013.24 – ident: e_1_3_3_30_1 doi: 10.1080/00207543.2020.1750727 – ident: e_1_3_3_49_1 doi: 10.1007/s12247-019-09396-2 – ident: e_1_3_3_61_1 – ident: e_1_3_3_65_1 doi: 10.1109/JSEN.2021.3122258 – ident: e_1_3_3_2_1 doi: 10.1080/00207543.2022.2093682 – ident: e_1_3_3_13_1 doi: 10.1108/JEIM-04-2019-0104 – ident: e_1_3_3_35_1 doi: 10.1016/B978-0-12-818366-3.00005-8 – ident: e_1_3_3_44_1 – ident: e_1_3_3_42_1 doi: 10.1080/00207543.2017.1421787 – ident: e_1_3_3_25_1 doi: 10.1038/s41562-020-0896-8 – ident: e_1_3_3_31_1 doi: 10.1080/00207543.2022.2118892 – ident: e_1_3_3_21_1 – ident: e_1_3_3_57_1 doi: 10.1007/978-981-13-6661-1_6 – ident: e_1_3_3_54_1 doi: 10.1016/j.cie.2014.04.019 – ident: e_1_3_3_52_1 doi: 10.1038/s41598-020-69250-1 – ident: e_1_3_3_43_1 – ident: e_1_3_3_20_1 doi: 10.1080/00207543.2020.1712491 – ident: e_1_3_3_34_1 doi: 10.1016/j.ifacol.2022.10.084 – ident: e_1_3_3_7_1 doi: 10.1016/B978-0-323-91614-1.00022-8 – ident: e_1_3_3_28_1 doi: 10.1080/00207543.2022.2118892 – ident: e_1_3_3_32_1 doi: 10.1007/s12063-021-00194-z – ident: e_1_3_3_27_1 doi: 10.1007/978-3-031-08871-1_9 – ident: e_1_3_3_39_1 doi: 10.1007/s10845-014-0953-0 – ident: e_1_3_3_5_1 doi: 10.1109/OAJPE.2022.3206220 – ident: e_1_3_3_59_1 doi: 10.1016/j.ijpe.2021.108279 – ident: e_1_3_3_51_1 doi: 10.1109/ICASSP.2013.6638963 – volume: 30 year: 2017 ident: e_1_3_3_53_1 article-title: Federated Multi-task Learning publication-title: Advances in Neural Information Processing Systems – ident: e_1_3_3_4_1 doi: 10.1080/00207543.2018.1530476 – ident: e_1_3_3_8_1 doi: 10.1080/00207543.2019.1685705 – ident: e_1_3_3_15_1 – ident: e_1_3_3_45_1 – volume: 9 start-page: 37 issue: 1 year: 2021 ident: e_1_3_3_55_1 article-title: A Novel Method for Medical Image Segmentation Based on Convolutional Neural Networks with Sgd Optimization publication-title: Journal of Electrical and Computer Engineering Innovations (JECEI) – ident: e_1_3_3_12_1 doi: 10.1016/j.ijpe.2014.12.037 – ident: e_1_3_3_60_1 doi: 10.1007/978-3-030-23551-2_2
SSID	ssj0000584
Score	2.634752
Snippet	The use of Artificial Intelligence (AI) for predicting supply chain risk has gained popularity. However, proposed approaches are based on the premise that...
SourceID	proquest crossref informaworld
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	8115
SubjectTerms	Algorithms Artificial intelligence Datasets federated learning Machine learning Predictions Privacy privacy preserving Risk Supply chain Supply chains
Title	Federated machine learning for privacy preserving, collective supply chain risk prediction
URI	https://www.tandfonline.com/doi/abs/10.1080/00207543.2022.2164628 https://www.proquest.com/docview/2885701459
Volume	61
WOSCitedRecordID	wos000926926500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAWR databaseName: Taylor and Francis Online Journals customDbUrl: eissn: 1366-588X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000584 issn: 0020-7543 databaseCode: TFW dateStart: 19610101 isFulltext: true titleUrlDefault: https://www.tandfonline.com providerName: Taylor & Francis
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LSwMxEA5aPejBt1itkoNHt3Sz2ddRxNJT8VCxelkySbYWdC3dWui_N5NNa4tIDwrLnnZCmEzmscx8HyHXJuaDCIB74AtToHAGntA47C6j1AcTM3JoWbKJuNtN-v30wXUTlq6tEmvovAKKsL4aL7eAct4RhxPcJtDxwFR3jDUZImSxZJNsMVOaYFdX67nz7YzDxAExtzyUmQ_x_LbMSnhaAS_94axtBGrv_8PeD8ieSz_pbWUvh2RDF0dkdwmU8Ji8tBFfwqSgir7bTktNHbXEgJp90tF4OBVyRrGDFh1NMbihaE2V56Ql0oTOqHwVw4Ji4zp-qIZ2fOKEPLbve3cdzzEweNIc28TjEEMstVFiZKHaTbkoueLm0aGEJNZcqwDyMI8VyIiBwPyIQRRD1BKh9oNTUis-Cn1GKAgh8liGIpIpj7USTKtIyRRM_pYoJuqEzxWfSQdPjiwZb5m_QDGtVJeh6jKnujppLsRGFT7HOoF0-VSzif0xklcsJlmwRrYxN4HMXfUyYwlyBPg8TM__sPQF2UEie9sowxqkNhl_6kuyLaeTYTm-sjZt3r320xdfoPN7
linkProvider	Taylor & Francis
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LSwMxEA5aBfXgW6xWzcGjW7rZ7OsoYqlYe6pYvIS8ti7oWtq10H9vJrurLSI9KOxxJ4TJZB5h5vsQujQxX3BPUEe43BQolAiHaxh2l0HsChMzEtGyZBNhrxcNBvH8LAy0VUINnRRAEdZXw-WGx-iqJQ5GuE2ko54p7whpEoDIItEqWvNNrIW2vn776dsb-1GJxNxyQKaa4vltmYX4tIBe-sNb2xDU3vmPze-i7TIBxdeFxeyhFZ3to605WMID9NwGhAmThCr8ZnstNS7JJYbYbBSPxumUyxmGHlpwNdnwCoM9Fb4TT4AodIblC08zDK3r8KNK7QDFIXps3_ZvOk7JweBIc3C5Q0UoQqmNFgML1m4KRkkVNZ_2pYhCTbXyROInoRIyIIJDhkREEIqgxX3tekeolr1n-hhhwTlPQunzQMY01IoTrQIlY2EyuEgRXke00jyTJUA58GS8MvcLx7RQHQPVsVJ1ddT8EhsVCB3LBOL5Y2W5fRpJCh4T5i2RbVQ2wMrLPmEkApYAl_rxyR-WvkAbnf5Dl3XvevenaBNo7W3bDGmgWj7-0GdoXU7zdDI-twb-CZS29pM
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LSwMxEA5aRfTgW6xWzcGjK91s9nUUdVGU0kPF4iXktbWga2lrof_eTDatLSI9KOwxE8JkMo9l5vsQOjcxX_BAUE_43BQolAiPaxh2l1HqCxMzclG3ZBNxo5G022nTdRMOXFsl1NB5CRRhfTU87p7KJx1xMMFtAh0NTHVHyCUBhCySLKMVC45lTLqVPX874zBxQMx1D2QmQzy_bTMXnubAS384axuBsq1_OPs22nTpJ74q7WUHLeliF23MgBLuoZcM8CVMCqrwu-201NhRS3SwOSfu9bsjLscYOmjB0RSdCwzWVHpOPACa0DGWr7xbYGhch4Wqa8cn9tFTdtu6vvMcA4MnzbUNPSpiEUttlBhZqHZTLkqqqPl0KEUSa6pVIPIwj5WQEREc8iMiolhEdR5qPzhAleKj0IcIC855HsuQRzKlsVacaBUpmQqTvyWK8CqiE8Uz6eDJgSXjjflTFNNSdQxUx5zqquhyKtYr8TkWCaSzt8qG9sdIXrKYsGCBbG1iAsw99QEjCXAE-DRMj_6w9Rlaa95k7PG-8XCM1oHT3vbMkBqqDPuf-gStytGwO-ifWvP-AoaI9Tc
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Federated+machine+learning+for+privacy+preserving%2C+collective+supply+chain+risk+prediction&rft.jtitle=International+journal+of+production+research&rft.au=Zheng%2C+Ge&rft.au=Kong%2C+Lingxuan&rft.au=Brintrup%2C+Alexandra&rft.date=2023-12-02&rft.pub=Taylor+%26+Francis+LLC&rft.issn=0020-7543&rft.eissn=1366-588X&rft.volume=61&rft.issue=23&rft.spage=8115&rft.epage=8132&rft_id=info:doi/10.1080%2F00207543.2022.2164628&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0020-7543&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0020-7543&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0020-7543&client=summon