Tail-Sensitive Breakdown Modeling in Manpower Systems: AEQL Analysis and Threshold Mixture distributions

Tail-Sensitive Breakdown Modeling in Manpower Systems: AEQL Analysis and Threshold Mixture distributions | 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 Tail-Sensitive Breakdown Modeling in Manpower Systems: AEQL Analysis and Threshold Mixture distributions R Sivasamy, Setlhare, K, Fatai Adewole Adebayo, MOLEFE, W.B., and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8120646/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 paper presents a stochastic modeling framework for manpower systems sub-ject to attrition-driven breakdown. We introduce the Average Expected Quit Level (AEQL) as a diagnostic metric to quantify cumulative personnel loss prior to recruitment intervention. The breakdown threshold is modeled using a mix-ture of geometric and heavy-tailed (Zeta) distributions, capturing both frequent turnover and latent risk. Through symbolic derivations and simulation overlays, we compare estimator performance under fixed and adaptive control regimes using AEQL, Standard Deviation of Run Length (SDRL), and Probability of Correct Intervention (PCI). The results highlight the trade-offs between early intervention and operational resilience, offering insights for robust recruitment policy design in uncertain environments JEL Classification: C41 , C61 , C63 , C65 , J21 , J24 Applied Statistics Manpower Planning AEQL Breakdown Modeling Survival Analysis Heavy-Tailed Thresholdsl PCI SDRL 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-8120646","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545463439,"identity":"aa52ce12-ae4f-4f2c-bf75-e5d2f173fcff","order_by":0,"name":"R Sivasamy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACZhiDvYENTBsQr4XnALFa4EAigUgt8u08hh9/1GxL3D7zjdkDhho7BnOJBPxaDA7zGEvzHLudOOd2jrkBw7FkBssZhLQwsyVIM7DdTpwhnWMmwcB2gMHgNgEt8s1syT9//ANqkTwD1PKPCC0Mh5mPSfC2AbVI8JhJMLYRocUAqMWat++28QyetHKDxL5kHsv5Dwg4rP9g880f327LzmA/vO3Bh292cuY8Bwg4DAocG0Ak0Ek8xKkHAnuiVY6CUTAKRsHIAwDd6EA753oLMQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3158-928X","institution":"University of Botswana","correspondingAuthor":true,"prefix":"","firstName":"R","middleName":"","lastName":"Sivasamy","suffix":""},{"id":545463593,"identity":"b683909a-1751-4323-a069-0967ff2a5d55","order_by":1,"name":"Setlhare, K","email":"","orcid":"","institution":"University of Botswana","correspondingAuthor":false,"prefix":"","firstName":"K","middleName":"","lastName":"Setlhare","suffix":""},{"id":545463890,"identity":"586a4c6e-8f20-47c8-9991-5c46b9cbee9b","order_by":2,"name":"Fatai Adewole Adebayo","email":"","orcid":"","institution":"University of Botswana","correspondingAuthor":false,"prefix":"","firstName":"Fatai","middleName":"Adewole","lastName":"Adebayo","suffix":""},{"id":545463891,"identity":"0a7f064e-f6d8-4b80-896b-da83a0709859","order_by":3,"name":"MOLEFE, W.B.","email":"","orcid":"","institution":"University of Botswana","correspondingAuthor":false,"prefix":"","firstName":"W.B.","middleName":"","lastName":"MOLEFE","suffix":""},{"id":545463892,"identity":"0d0a1e47-9c79-4c75-9d7b-a68da7c30f90","order_by":4,"name":"MOSEKI, K.K. (","email":"","orcid":"","institution":"University of Botswana","correspondingAuthor":false,"prefix":"","firstName":"K.K.","middleName":"(","lastName":"MOSEKI","suffix":""}],"badges":[],"createdAt":"2025-11-15 08:42:52","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-8120646/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8120646/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96248591,"identity":"c3544850-6617-4aec-be6f-98b0aca96dd5","added_by":"auto","created_at":"2025-11-19 07:28:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":511052,"visible":true,"origin":"","legend":"","description":"","filename":"ManpowerOPSEARCH.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8120646/v1_covered_8811fc89-a48c-44d5-828c-b68680220f5b.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cem\u003eTail-Sensitive Breakdown Modeling in Manpower\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSystems: AEQL Analysis and Threshold Mixture distributions\u003c/em\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Botswana","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":"Manpower Planning, AEQL, Breakdown Modeling, Survival Analysis, Heavy-Tailed Thresholdsl PCI, SDRL","lastPublishedDoi":"10.21203/rs.3.rs-8120646/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8120646/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents a stochastic modeling framework for manpower systems sub-ject to attrition-driven breakdown. We introduce the Average Expected Quit Level (AEQL) as a diagnostic metric to quantify cumulative personnel loss prior to recruitment intervention. The breakdown threshold is modeled using a mix-ture of geometric and heavy-tailed (Zeta) distributions, capturing both frequent turnover and latent risk. Through symbolic derivations and simulation overlays, we compare estimator performance under fixed and adaptive control regimes using AEQL, Standard Deviation of Run Length (SDRL), and Probability of Correct Intervention (PCI). The results highlight the trade-offs between early intervention and operational resilience, offering insights for robust recruitment policy design in uncertain environments\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification: \u003c/strong\u003eC41 , C61 , C63 , C65 , J21 , J24\u003c/p\u003e","manuscriptTitle":"Tail-Sensitive Breakdown Modeling in Manpower\nSystems: AEQL Analysis and Threshold Mixture distributions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 03:56:01","doi":"10.21203/rs.3.rs-8120646/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":"461cb173-2a00-44d8-9714-77ae52699af4","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58037258,"name":"Applied Statistics"}],"tags":[],"updatedAt":"2025-11-18T03:56:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 03:56:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8120646","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8120646","identity":"rs-8120646","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}