{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T10:43:47Z","timestamp":1782816227633,"version":"3.54.5"},"reference-count":25,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2018,1,30]],"date-time":"2018-01-30T00:00:00Z","timestamp":1517270400000},"content-version":"vor","delay-in-days":29,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:p>Manufacturing companies often lack visibility of the procurement interdependencies between the suppliers within their supply network. However, knowledge of these interdependencies is useful to plan for potential operational disruptions. In this paper, we develop the Supply Network Link Predictor (SNLP) method to infer supplier interdependencies using the manufacturer\u2019s incomplete knowledge of the network. SNLP uses topological data to extract relational features from the known network to train a classifier for predicting potential links. Using a test case from the automotive industry, four features are extracted: (i) number of existing supplier links, (ii) overlaps between supplier product portfolios, (iii) product outsourcing associations, and (iv) likelihood of buyers purchasing from two suppliers together. Na\u00efve Bayes and Logistic Regression are then employed to predict whether these features can help predict interdependencies between two suppliers. Our results show that these features can indeed be used to predict interdependencies in the network and that predictive accuracy is maximised by (i) and (iii). The findings give rise to the exciting possibility of using data analytics for improving supply chain visibility. We then proceed to discuss to what extent such approaches can be adopted and their limitations, highlighting next steps for future work in this area.<\/jats:p>","DOI":"10.1155\/2018\/9104387","type":"journal-article","created":{"date-parts":[[2018,1,30]],"date-time":"2018-01-30T23:32:07Z","timestamp":1517355127000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Predicting Hidden Links in Supply Networks"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4189-2434","authenticated-orcid":false,"given":"A.","family":"Brintrup","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P.","family":"Wichmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P.","family":"Woodall","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D.","family":"McFarlane","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E.","family":"Nicks","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"W.","family":"Krechel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,1,30]]},"reference":[{"key":"e_1_2_7_1_2","doi-asserted-by":"crossref","unstructured":"TangZ. E. GoetschalckxM. andMcGinnisL. Modeling-based design of strategic supply chain networks for aircraft manufacturing Proceedings of the 11th Annual Conference on Systems Engineering Research CSER 2013 March 2013 USA 611\u2013620 2-s2.0-84898736842 https:\/\/doi.org\/10.1016\/j.procs.2013.01.064.","DOI":"10.1016\/j.procs.2013.01.064"},{"key":"e_1_2_7_2_2","unstructured":"LinD. An information-theoretic definition of similarity Proceedings of the 15th Int. Conf. on Machine Learning 1998 Madison Wisconsin USA."},{"key":"e_1_2_7_3_2","first-page":"3","volume-title":"Emerging Artificial Intelligence Applications in Computer Engineering","author":"Kotsiantis S. B.","year":"2007"},{"key":"e_1_2_7_4_2","unstructured":"Garcia-GasullaD. Link prediction in large-scale directed graphs PhD thesis Universitat Politecnica de Catalunya 2015 Barcelona."},{"key":"e_1_2_7_5_2","doi-asserted-by":"crossref","unstructured":"Liben-NowellD.andKleinbergJ. The link prediction problem for social networks Proceedings of the 12th ACM International Conference on Information and Knowledge Management (CIKM \u203203) November 2003 ACM 556\u2013559 https:\/\/doi.org\/10.1145\/956863.956972 2-s2.0-18744398132.","DOI":"10.1145\/956958.956972"},{"key":"e_1_2_7_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF02289026"},{"key":"e_1_2_7_7_2","doi-asserted-by":"publisher","DOI":"10.1103\/physreve.73.026120"},{"key":"e_1_2_7_8_2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.80.046122"},{"key":"e_1_2_7_9_2","doi-asserted-by":"publisher","DOI":"10.1209\/0295-5075\/96\/48007"},{"key":"e_1_2_7_10_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature06830"},{"key":"e_1_2_7_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/mis.2004.49"},{"key":"e_1_2_7_12_2","doi-asserted-by":"publisher","DOI":"10.1108\/01443571311307343"},{"key":"e_1_2_7_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12159-015-0128-1"},{"key":"e_1_2_7_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2015.2425137"},{"key":"e_1_2_7_15_2","unstructured":"FriedmanN. GetoorL. KollerD. andPfefferA. Learning Probabilistic relational models Proceedings of the 16th Int. Joint Conference on Artificial Intelligence 1999 Stockholm Sweden."},{"key":"e_1_2_7_16_2","doi-asserted-by":"crossref","unstructured":"YuK. ChuW. YuS. TrespV. andXuZ. Stochastic Relational Models for Discriminative Link Prediction Proceedings of Neural Information Processing Systems 2006 Cambridge MA.","DOI":"10.7551\/mitpress\/7503.003.0199"},{"key":"e_1_2_7_17_2","doi-asserted-by":"publisher","DOI":"10.1002\/sam.11198"},{"key":"e_1_2_7_18_2","article-title":"Link prediction using supervised learning","author":"Al-Hassan M.","year":"2006","journal-title":"In SDM Workshop on Al-Link Analysis, Counter-terrorism and Security"},{"key":"e_1_2_7_19_2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"e_1_2_7_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007734"},{"key":"e_1_2_7_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2008.2007853"},{"key":"e_1_2_7_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.187"},{"key":"e_1_2_7_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2005.10.010"},{"key":"e_1_2_7_24_2","unstructured":"NickelM. TrespV. andKriegelH. A Three-Way Model for Collective Learning on Multi-Relational Data Proc. of the 28th Int. Conf. on Machine Learning 2011 Bellevue WA USA."},{"key":"e_1_2_7_25_2","doi-asserted-by":"crossref","unstructured":"Garcia-GasullaD. CortesU. AyguadeE. andLabartaJ. Evaluating Link Prediction on Large Graphs Proceedings of 18th Int. Conf. of the Catalan Association for Artificial Intelligence 2015 90\u201399.","DOI":"10.3233\/978-1-61499-578-4-90"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2018\/9104387.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2018\/9104387.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2018\/9104387","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T05:09:14Z","timestamp":1751260154000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2018\/9104387"}},"subtitle":[],"editor":[{"given":"Pietro","family":"De Lellis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2018,1]]},"references-count":25,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,1]]}},"alternative-id":["10.1155\/2018\/9104387"],"URL":"https:\/\/doi.org\/10.1155\/2018\/9104387","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1]]},"assertion":[{"value":"2017-05-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2017-12-20","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-01-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"9104387"}}