Revenue Optimization in Buffet Systems: A Hybrid Pricing and Ancillary Revenue Approach

Revenue Optimization in Buffet Systems: A Hybrid Pricing and Ancillary Revenue Approach | 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 Case Report Revenue Optimization in Buffet Systems: A Hybrid Pricing and Ancillary Revenue Approach sayiram g This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9424583/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 This study proposes a data-driven framework for revenue optimization in buffet-based restaurant systems by integrating multiple linear regression analysis with profit maximization modeling. A structured dataset comprising 100 observations was developed to examine the influence of pricing, alcohol attach rate, table turnover, and location on Average Revenue Per Customer (ARPC). The empirical results demonstrate that alcohol attach rate is the most significant determinant of revenue (β = 6.18, p < 0.001), exerting a substantially greater impact than conventional pricing strategies. Furthermore, simulation-based optimization reveals that maximum profitability is achieved within an optimal price range of ₹1200–₹1250. The findings underscore the critical role of high-margin ancillary revenue streams in enhancing profitability and provide a robust quantitative framework for overcoming the inherent limitations of fixed-price service models. This study contributes to the service operations and revenue management literature by offering actionable insights for strategic decision-making in hospitality systems. Pricing Strategy Alcohol Attach Rate Simulation Modeling Regression Analysis Service Operations 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. 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-9424583","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":625748792,"identity":"25aacc03-1a1b-4c88-9fe9-3abb62233028","order_by":0,"name":"sayiram g","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYJCCA0Asx8bMfPABkMHDR6wWY372tmQDkBY2Ym1KnNlzxkwCxCKoRb79jOHhipptjBtupKVVfs2xk2FjYH746AYeLQZncgwOnjl2m9ngRvKx27LbkoEOYzM2zsGnhQGopYHtNpsB0JbbktuYgVp42KTxaZHvfwPU8u82j8GNHLNiyW31hLUw3ADa0th2W0IS6H3Gj9sOE9ZicONZwcHGvtsGoECWZtx2nIeNmYBf5PuTN39s+Ha7vg0YlR9/bqu252dvfvgYr8MYOAzgTGYeMIlXOQiwP4AzGX8QVD0KRsEoGAUjEQAAY41NodmxApQAAAAASUVORK5CYII=","orcid":"","institution":"Christ University","correspondingAuthor":true,"prefix":"","firstName":"sayiram","middleName":"","lastName":"g","suffix":""}],"badges":[],"createdAt":"2026-04-15 09:41:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9424583/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9424583/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108695150,"identity":"bdbd09bb-106b-4426-8003-ce054152319b","added_by":"auto","created_at":"2026-05-07 11:42:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":722129,"visible":true,"origin":"","legend":"","description":"","filename":"RevenueOptimizationinBuffetSystemsAHybridPricingandAncillaryRevenueApproach.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9424583/v1_covered_23dfa64a-06d5-4237-8b3b-375778a5c58a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Revenue Optimization in Buffet Systems: A Hybrid Pricing and Ancillary Revenue Approach","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"Pricing Strategy, Alcohol Attach Rate, Simulation Modeling, Regression Analysis, Service Operations Management","lastPublishedDoi":"10.21203/rs.3.rs-9424583/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9424583/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study proposes a data-driven framework for revenue optimization in buffet-based restaurant systems by integrating multiple linear regression analysis with profit maximization modeling. A structured dataset comprising 100 observations was developed to examine the influence of pricing, alcohol attach rate, table turnover, and location on Average Revenue Per Customer (ARPC). The empirical results demonstrate that alcohol attach rate is the most significant determinant of revenue (β\u0026thinsp;=\u0026thinsp;6.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), exerting a substantially greater impact than conventional pricing strategies. Furthermore, simulation-based optimization reveals that maximum profitability is achieved within an optimal price range of ₹1200\u0026ndash;₹1250. The findings underscore the critical role of high-margin ancillary revenue streams in enhancing profitability and provide a robust quantitative framework for overcoming the inherent limitations of fixed-price service models. 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