{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T04:54:57Z","timestamp":1774155297635,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":17,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819907403","type":"print"},{"value":"9789819907410","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-0741-0_25","type":"book-chapter","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T09:04:34Z","timestamp":1680253474000},"page":"343-356","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Machine Learning Techniques for Predicting Risks of Late Delivery"],"prefix":"10.1007","author":[{"given":"Ravikanth","family":"Lolla","sequence":"first","affiliation":[]},{"given":"Matthew","family":"Harper","sequence":"additional","affiliation":[]},{"given":"Jan","family":"Lunn","sequence":"additional","affiliation":[]},{"given":"Jamila","family":"Mustafina","sequence":"additional","affiliation":[]},{"given":"Jolnar","family":"Assi","sequence":"additional","affiliation":[]},{"given":"Chong Kim","family":"Loy","sequence":"additional","affiliation":[]},{"given":"Dhiya","family":"Al-Jumeily OBE","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,1]]},"reference":[{"issue":"7","key":"25_CR1","doi-asserted-by":"publisher","first-page":"2179","DOI":"10.1080\/00207543.2018.1530476","volume":"57","author":"G Baryannis","year":"2019","unstructured":"Baryannis, G., Validi, S., Dani, S., Antoniou, G.: Supply chain risk management and artificial intelligence: state of the art and future research directions. Int. J. Prod. Res. 57(7), 2179\u20132202 (2019)","journal-title":"Int. J. Prod. Res."},{"key":"25_CR2","doi-asserted-by":"publisher","first-page":"107599","DOI":"10.1016\/j.ijpe.2019.107599","volume":"226","author":"R Dubey","year":"2020","unstructured":"Dubey, R., et al.: Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. Int. J. Prod. Econ. 226, 107599 (2020)","journal-title":"Int. J. Prod. Econ."},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Gunasekaran, A., et al.: Big data and predictive analytics for supply chain and organizational performance. J. Bus. Res. 70, 308\u2013317 (2017). http:\/\/www.springer.com\/lncs. Accessed 21 Nov 2016","DOI":"10.1016\/j.jbusres.2016.08.004"},{"key":"25_CR4","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.cor.2017.07.004","volume":"98","author":"T Nguyen","year":"2018","unstructured":"Nguyen, T., Zhou, L., Spiegler, V., Ieromonachou, P., Lin, Y.: Big data analytics in SCM: A state-of-the-art literature review. Comput. Oper. Res. 98, 254\u2013264 (2018)","journal-title":"Comput. Oper. Res."},{"key":"25_CR5","unstructured":"Goldman, S.: Post-pandemic e-commerce: The unstoppable growth of online shopping. The future of customer engagement and experience (2021)"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Weingarten, J., Spinler, S.: Shortening delivery times by predicting customers\u2019 online purchases: a case study in the fashion industry. Inf. Syst. Manage. 384, 287\u2013308 (2021). https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/10580530.2020.1814459","DOI":"10.1080\/10580530.2020.1814459"},{"key":"25_CR7","doi-asserted-by":"publisher","first-page":"104559","DOI":"10.1016\/j.resconrec.2019.104559","volume":"153","author":"S Bag","year":"2020","unstructured":"Bag, S., Wood, L.C., Xu, L., Dhamija, P., Kayikci, Y.: Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Res. Conserv. Recycl. 153, 104559 (2020)","journal-title":"Res. Conserv. Recycl."},{"key":"25_CR8","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1186\/s40537-020-00345-2","volume":"71","author":"S Islam","year":"2020","unstructured":"Islam, S., Amin, S.H.: Prediction of probable backorder scenarios in the supply chain using distributed RF and GB ML techniques. J. Big Data 71, 65 (2020)","journal-title":"J. Big Data"},{"key":"25_CR9","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1080\/2573234X.2020.1776164","volume":"31","author":"S John","year":"2020","unstructured":"John, S., Shah, B.J., Kartha, P.: Refund fraud analytics for an online retail purchases. J. Bus. Anal. 31, 56\u201366 (2020)","journal-title":"J. Bus. Anal."},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Malviya, L., Chittora, P., Chakrabarti, P., Vyas, R.S. and Poddar, S.: Backorder prediction in the supply chain using machine learning. Mater. Today: Proc. (2021)","DOI":"10.1016\/j.matpr.2020.11.558"},{"issue":"11","key":"25_CR11","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1016\/j.ifacol.2018.08.472","volume":"51","author":"D Gyulai","year":"2018","unstructured":"Gyulai, D., Pfeiffer, A., Nick, G., Gallina, V., Sihn, W., Monostori, L.: Lead time prediction in a flow-shop environment with analytical and machine learning approaches. IFAC-PapersOnLine 51(11), 1029\u20131034 (2018)","journal-title":"IFAC-PapersOnLine"},{"key":"25_CR12","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.ijpe.2019.01.032","volume":"211","author":"Y Zhu","year":"2019","unstructured":"Zhu, Y., Zhou, L., Xie, C., Wang, G.-J., Nguyen, T.V.: Forecasting SMEs\u2019 credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. Int. J. Prod. Econ. 211, 22\u201333 (2019)","journal-title":"Int. J. Prod. Econ."},{"issue":"4","key":"25_CR13","first-page":"1","volume":"66","author":"M Paw\u0142owski","year":"2021","unstructured":"Paw\u0142owski, M.: Machine learning based product classification for eCommerce. J. Comput. Inf. Syst. 66(4), 1\u201310 (2021)","journal-title":"J. Comput. Inf. Syst."},{"key":"25_CR14","unstructured":"Constante, F.: DataCo smart supply chain for big data analysis. Mendeley (2019). https:\/\/data.mendeley.com\/datasets\/8gx2fvg2k6\/5"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Bergstra, J., Yamins, D., Cox, D.D.: Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms. In: Proceedings of the 12th Python in Science Conference, p. 20. Citeseer (2013)","DOI":"10.25080\/Majora-8b375195-003"},{"key":"25_CR16","unstructured":"Nogueira, F.: Bayesian optimization: open source constrained global optimization tool for Python (2014). https:\/\/github.com\/fmfn\/BayesianOptimization"},{"key":"25_CR17","doi-asserted-by":"publisher","unstructured":"Velmurugan, M., Ouyang, C., Moreira, C., Sindhgatta, R.: Evaluating fidelity of explainable methods for predictive process analytics. In: Nurcan, S., Korthaus, A. (eds.) Intelligent Information Systems. CAiSE 2021. Lecture Notes in Business Information Processing, vol. 424, pp.64\u201372. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-79108-7_8","DOI":"10.1007\/978-3-030-79108-7_8"}],"container-title":["Lecture Notes on Data Engineering and Communications Technologies","Data Science and Emerging Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-0741-0_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T20:51:56Z","timestamp":1685479916000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-0741-0_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819907403","9789819907410"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-0741-0_25","relation":{},"ISSN":["2367-4512","2367-4520"],"issn-type":[{"value":"2367-4512","type":"print"},{"value":"2367-4520","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DaSET","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The International Conference on Data Science and Emerging Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"daset2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icdaset.com","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}