Enhancing Supply Chain Efficiency with AI: Predicting and Preventing Customer Backorders

Enhancing Supply Chain Efficiency with AI: Predicting and Preventing Customer Backorders | 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 Enhancing Supply Chain Efficiency with AI: Predicting and Preventing Customer Backorders Otmane Khtou, Taoufyq Elansari, Meryam El Mouhtadi, Mohammed Ouanan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8210613/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Apr, 2026 Read the published version in Discover Artificial Intelligence → Version 1 posted 14 You are reading this latest preprint version Abstract We propose a hybrid, interpretable AI module that enables autonomous backorder prevention in cyber-physical supply chains. The module integrates data-level rebalancing (SMOTE–Tomek), ensemble learners (e.g., LightGBM, XGBoost, Extra Trees), and an explanation layer combining intrinsic feature importance with permutation importance and partial-dependence/ALE analyses to infer rare backorder risks at scale while exposing the operational drivers behind each prediction. On a million-record industrial dataset, the approach outperforms competitive baselines on precision, recall, F1 and ROC-AUC, and remains transparent and deployment-ready. Explanations consistently highlight demand signals, inventory positions, and shipment delays as dominant determinants of risk, yielding policy-level levers for autonomous or human-in-the-loop replenishment agents (e.g., reallocation, expedited orders). Coupling predictive accuracy with actionable explanations, the module functions as a decision component within an autonomy loop (sense → predict → explain → act → feedback), advanc- ing learning for autonomy and improving resilience, cost-to-serve, and service reliability in networked operations. Autonomous intelligent systems Learning for autonomy Cyber-physical supply chains Backorder prediction Interpretable machine learning Permutation importance Partial-dependence/ALE Ensemble learning Decision support Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Apr, 2026 Read the published version in Discover Artificial Intelligence → Version 1 posted Editorial decision: Revision requested 23 Feb, 2026 Reviews received at journal 15 Feb, 2026 Reviews received at journal 13 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviews received at journal 31 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 11 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviewers invited by journal 09 Dec, 2025 Editor invited by journal 03 Dec, 2025 Editor assigned by journal 28 Nov, 2025 Submission checks completed at journal 28 Nov, 2025 First submitted to journal 26 Nov, 2025 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8210613","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":552522302,"identity":"e92b8ee5-4eaf-4117-afbb-7909bb7a0373","order_by":0,"name":"Otmane 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Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Autonomous intelligent systems, Learning for autonomy, Cyber-physical supply chains, Backorder prediction, Interpretable machine learning, Permutation importance, Partial-dependence/ALE, Ensemble learning, Decision support","lastPublishedDoi":"10.21203/rs.3.rs-8210613/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8210613/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe propose a hybrid, interpretable AI module that enables autonomous backorder prevention in cyber-physical supply chains. The module integrates data-level rebalancing (SMOTE–Tomek), ensemble learners (e.g., LightGBM, XGBoost, Extra Trees), and an explanation layer combining intrinsic feature importance with permutation importance and partial-dependence/ALE analyses to infer rare backorder risks at scale while exposing the operational drivers behind each prediction. On a million-record industrial dataset, the approach outperforms competitive baselines on precision, recall, F1 and ROC-AUC, and remains transparent and deployment-ready. Explanations consistently highlight demand signals, inventory positions, and shipment delays as dominant determinants of risk, yielding policy-level levers for autonomous or human-in-the-loop replenishment agents (e.g., reallocation, expedited orders). Coupling predictive accuracy with actionable explanations, the module functions as a decision component within an autonomy loop (sense → predict → explain → act → feedback), advanc- ing learning for autonomy and improving resilience, cost-to-serve, and service reliability in networked operations.\u003c/p\u003e","manuscriptTitle":"Enhancing Supply Chain Efficiency with AI: Predicting and Preventing Customer Backorders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 08:06:23","doi":"10.21203/rs.3.rs-8210613/v1","editorialEvents":[{"type":"communityComments","content":1},{"type":"decision","content":"Revision requested","date":"2026-02-23T10:45:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-15T16:43:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-13T20:26:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186732975818140432940003719204321690277","date":"2026-02-02T07:11:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-31T05:13:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48349190722448578409854294175757310522","date":"2026-01-29T23:10:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237984620389234219608074760232012394512","date":"2026-01-29T11:57:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27701944193874341670042169476846147058","date":"2025-12-11T14:01:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252920701609313568348519125265398313943","date":"2025-12-09T13:52:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-09T13:27:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-03T10:08:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-29T04:55:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-29T04:53:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Artificial Intelligence","date":"2025-11-26T08:30:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a1abb4f5-ac01-4be1-a79d-d7b37c3c9584","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:02:20+00:00","versionOfRecord":{"articleIdentity":"rs-8210613","link":"https://doi.org/10.1007/s44163-026-01237-9","journal":{"identity":"discover-artificial-intelligence","isVorOnly":false,"title":"Discover Artificial Intelligence"},"publishedOn":"2026-04-16 15:58:37","publishedOnDateReadable":"April 16th, 2026"},"versionCreatedAt":"2025-12-02 08:06:23","video":"","vorDoi":"10.1007/s44163-026-01237-9","vorDoiUrl":"https://doi.org/10.1007/s44163-026-01237-9","workflowStages":[]},"version":"v1","identity":"rs-8210613","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8210613","identity":"rs-8210613","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}