Machine Learning and Deep Learning for demand forecasting in Logistics: A systematic literature review

Machine Learning and Deep Learning for demand forecasting in Logistics: A systematic literature review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Machine Learning and Deep Learning for demand forecasting in Logistics: A systematic literature review Ivan Lizandro Pampa Pampa, Lia Betsabe Rau Rosales, Giancarlo Sánchez Atúncar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7336582/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, the increasing complexity of supply chain operations has driven a growing interest in the use of artificial intelligence, particularly deep learning, for demand forecasting. This study presents a systematic literature review (SLR) that identifies, classifies, and analyzes the most commonly used deep learning models in logistics demand planning. Using the PICO methodology, research questions were structured to guide the selection and evaluation of relevant studies. A total of 606 initial sources were retrieved from SCOPUS, and after applying strict inclusion and exclusion criteria (including year of publication, language, access type, publication status, and journal quartile), 86 articles were included in the final analysis. The results reveal significant academic interest in the topic, with a peak in publications in 2023. Models such as CNN, LSTM, GRU, and hybrid approaches demonstrate high forecasting accuracy and adaptability to complex logistics environments. However, its performance depends on factors such as data quality, operational context, and model configuration. The review highlights current trends, identifies research gaps, and suggests future courses of action focused on improving data integration and developing context-specific hybrid models. This analysis provides a solid foundation for researchers and practitioners seeking to implement deep learning techniques in logistics and supply chain demand planning. Deep Learning Machine Learning Demand Forecasting Logistics Time Series Prediction Supply Chain Management Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7336582","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501483942,"identity":"8ebc629c-889a-400e-bb13-50af1e2cbb75","order_by":0,"name":"Ivan Lizandro Pampa Pampa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYBACNghlAyYPgAg+IJYgQksaVEsCRASvFig4DKWJ0cIn3fx0c0HN+cT+GTmGhyt/HJZnY2A+eJuH4Y4cTofJHDO7PePY7cQZN3IMDp5JOGzYxsCWbM3D8MwYpxaJBLPbPGy3EzfwHEs42JBwmLGNgcdMmofhcGIDTi3p327z/DsH12LfxsD/DaSlHreWHLPbvG0HEjewNx8AaUkE2sIG0pKA2y9nym7P7Es2nnEcpCUtPbmNmc3Yco7BM0NctsjPbt92u+CbnWx/M2PzxwYba9t+9uaHN95U3JHHZQsoCphRRcBcgwM4dWDRAgF4tIyCUTAKRsFIAwBhfFfMUcUiVQAAAABJRU5ErkJggg==","orcid":"","institution":"Universidad César Vallejo","correspondingAuthor":true,"prefix":"","firstName":"Ivan","middleName":"Lizandro Pampa","lastName":"Pampa","suffix":""},{"id":501483946,"identity":"0697e894-7160-41fa-becf-96a23f823a6e","order_by":1,"name":"Lia Betsabe Rau Rosales","email":"","orcid":"","institution":"Universidad César Vallejo","correspondingAuthor":false,"prefix":"","firstName":"Lia","middleName":"Betsabe Rau","lastName":"Rosales","suffix":""},{"id":501483949,"identity":"b483d3e1-d300-4096-a702-1f2f5b04c4ca","order_by":2,"name":"Giancarlo Sánchez Atúncar","email":"","orcid":"","institution":"Universidad César Vallejo","correspondingAuthor":false,"prefix":"","firstName":"Giancarlo","middleName":"Sánchez","lastName":"Atúncar","suffix":""}],"badges":[],"createdAt":"2025-08-10 03:38:09","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7336582/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7336582/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90277910,"identity":"85795bd4-2d60-4e95-b974-4944450108e9","added_by":"auto","created_at":"2025-09-01 03:31:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1664563,"visible":true,"origin":"","legend":"","description":"","filename":"MachineLearningandDeepLearningfordemandforecastinginLogisticsAsystematicliteraturereview.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7336582/v1_covered_b125a68c-43c4-4905-b8be-6f508260ea96.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Learning and Deep Learning for demand forecasting in Logistics: A systematic literature review","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Deep Learning, Machine Learning, Demand Forecasting, Logistics, Time Series Prediction, Supply Chain Management","lastPublishedDoi":"10.21203/rs.3.rs-7336582/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7336582/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, the increasing complexity of supply chain operations has driven a growing interest in the use of artificial intelligence, particularly deep learning, for demand forecasting. This study presents a systematic literature review (SLR) that identifies, classifies, and analyzes the most commonly used deep learning models in logistics demand planning. Using the PICO methodology, research questions were structured to guide the selection and evaluation of relevant studies. A total of 606 initial sources were retrieved from SCOPUS, and after applying strict inclusion and exclusion criteria (including year of publication, language, access type, publication status, and journal quartile), 86 articles were included in the final analysis. The results reveal significant academic interest in the topic, with a peak in publications in 2023. Models such as CNN, LSTM, GRU, and hybrid approaches demonstrate high forecasting accuracy and adaptability to complex logistics environments. However, its performance depends on factors such as data quality, operational context, and model configuration. 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