A Practical Approach to Replenishment Optimization: Unifying One-Shot Inventory Policy Optimization with Probabilistic Models

A Practical Approach to Replenishment Optimization: Unifying One-Shot Inventory Policy Optimization with Probabilistic Models | 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 Article A Practical Approach to Replenishment Optimization: Unifying One-Shot Inventory Policy Optimization with Probabilistic Models Alva Presbitero, Andreas Syren, Hagop Dippel, Lalita Awasthi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7453852/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Effective inventory management is crucial for businesses operating across diverse sales channels, particularly in the dynamic e-commerce landscape. Balancing the need to minimize inventory costs while ensuring sufficient stock availability to meet fluctuating demand presents a significant challenge, further compounded by the complexities of external partnerships and distributed fulfillment networks. These factors introduce uncertainties in demand, returns, and lead times, impacting overall profitability. This study presents a novel approach to replenishment optimization with a two-fold contribution. First, we aim to bridge the gap through the systematic integration of probabilistic demand forecasting with advanced inventory policy optimization techniques, bridging a critical gap between predictive modeling and practical replenishment decisions. Second, we introduce a significant extension to the classical RsQ policy framework by Jansen (1996), adapting it to the specific demands of a distributed fulfillment network and highly seasonal assortments. Our ZEOS Inventory Optimization Tool unifies one-shot inventory policy optimization with the predictive power of probabilistic gradient-boosting models (LightGBM). This tool, currently deployed in production by a leading European e-commerce company (Zalando), extends the classical RsQ policy framework to accommodate distributed fulfillment networks and highly seasonal assortments. It optimizes replenishment decisions, including initial replenishment timing and quantities as well as subsequent order quantities, while respecting constraints such as lead times, safety stock requirements, and operational rules. The integration of gradient-boosting models, which excel at capturing complex temporal dynamics, provides a more nuanced and accurate representation of future demand compared to traditional deterministic models. This research represents a pioneering effort in bridging the gap between probabilistic demand forecasting and policy optimization techniques, resulting in a practical tool that addresses the challenges of inventory management in an e-commerce setting, contributing to improved efficiency, reduced costs, and enhanced profitability. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 19 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Nov, 2025 Reviews received at journal 19 Oct, 2025 Reviews received at journal 12 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers invited by journal 06 Oct, 2025 Editor assigned by journal 06 Oct, 2025 Editor invited by journal 23 Sep, 2025 Submission checks completed at journal 20 Sep, 2025 First submitted to journal 20 Sep, 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-7453852","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":528228509,"identity":"c977fecb-c09a-4d75-97c9-da4589aa1d95","order_by":0,"name":"Alva 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