Optimizing Multi-Tier Supply Chain Ordering with a Hybrid Liquid Neural Network and Extreme Gradient Boosting Model

Optimizing Multi-Tier Supply Chain Ordering with a Hybrid Liquid Neural Network and Extreme Gradient Boosting Model | 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 Optimizing Multi-Tier Supply Chain Ordering with a Hybrid Liquid Neural Network and Extreme Gradient Boosting Model Chunan Tong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7797137/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 Supply chain management (SCM) encounters major obstacles, such as demand variability, inventory mismatches, and increased upstream order fluctuations caused by the bullwhip effect. Conventional approaches, like simple moving averages, find it challenging to cope with ever-changing market conditions. The Vending Machine Test is a key benchmark for LLMs, simulating real-world vending machine sales prediction scenarios, but Grok-4 and many other AI models still struggle with the complex continuous time series data in SCM, making it hard to accurately predict vending machine sales that fluctuate hourly, daily or seasonally. However, new machine learning (ML) methods, including LSTM, reinforcement learning, and XGBoost, present possible solutions but are hindered by computational complexity, training inefficiencies, or limitations in time-series modeling. Liquid Neural Networks (LNN), drawing inspiration from dynamic biological systems, offer a promising alternative due to their adaptability, low computational demands, and resilience to noise, making them ideal for real-time decision-making and edge computing. Although they have been successful in areas like autonomous vehicles and medical monitoring, their potential in optimizing supply chains is still largely untapped. This study introduces a hybrid LNN + XGBoost model to refine ordering strategies in multi-tier supply chains. By utilizing LNN’s dynamic feature extraction and XGBoost’s global optimization strengths, the model seeks to reduce the bullwhip effect and boost overall profitability. The research explores how the hybrid framework’s local and global synergies meet the dual needs of adaptability and efficiency in SCM. The proposed method addresses a crucial gap in current methodologies, providing an innovative solution for dynamic and efficient supply chain management. Other Business Operations Research Bullwhip Effect Demand Fluctuation Stockout Forecast Liquid Neural Networks Extreme Gradient Boosting Full Text Additional Declarations The authors declare no competing interests. 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. 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-7797137","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":525811084,"identity":"8e036959-cbae-435f-aa25-9f5db8888a34","order_by":0,"name":"Chunan 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Park","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"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":"Bullwhip Effect, Demand Fluctuation, Stockout, Forecast, Liquid Neural Networks, Extreme Gradient Boosting","lastPublishedDoi":"10.21203/rs.3.rs-7797137/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7797137/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSupply chain management (SCM) encounters major obstacles, such as demand variability, inventory mismatches, and increased upstream order fluctuations caused by the bullwhip effect. Conventional approaches, like simple moving averages, find it challenging to cope with ever-changing market conditions. The Vending Machine Test is a key benchmark for LLMs, simulating real-world vending machine sales prediction scenarios, but Grok-4 and many other AI models still struggle with the complex continuous time series data in SCM, making it hard to accurately predict vending machine sales that fluctuate hourly, daily or seasonally. However, new machine learning (ML) methods, including LSTM, reinforcement learning, and XGBoost, present possible solutions but are hindered by computational complexity, training inefficiencies, or limitations in time-series modeling. Liquid Neural Networks (LNN), drawing inspiration from dynamic biological systems, offer a promising alternative due to their adaptability, low computational demands, and resilience to noise, making them ideal for real-time decision-making and edge computing. Although they have been successful in areas like autonomous vehicles and medical monitoring, their potential in optimizing supply chains is still largely untapped. This study introduces a hybrid LNN\u0026thinsp;+\u0026thinsp;XGBoost model to refine ordering strategies in multi-tier supply chains. By utilizing LNN\u0026rsquo;s dynamic feature extraction and XGBoost\u0026rsquo;s global optimization strengths, the model seeks to reduce the bullwhip effect and boost overall profitability. The research explores how the hybrid framework\u0026rsquo;s local and global synergies meet the dual needs of adaptability and efficiency in SCM. The proposed method addresses a crucial gap in current methodologies, providing an innovative solution for dynamic and efficient supply chain management.\u003c/p\u003e","manuscriptTitle":"Optimizing Multi-Tier Supply Chain Ordering with a Hybrid Liquid Neural Network and Extreme Gradient Boosting Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 05:00:24","doi":"10.21203/rs.3.rs-7797137/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"6726c9d7-dbf9-479d-81e2-c6923dd611ab","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55879374,"name":"Other Business"},{"id":55879375,"name":"Operations Research"}],"tags":[],"updatedAt":"2025-10-08T05:00:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 05:00:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7797137","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7797137","identity":"rs-7797137","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}