Performance Modeling for Isolated Industrial Operations: A Python Framework for Data Envelopment Analysis

Performance Modeling for Isolated Industrial Operations: A Python Framework for Data Envelopment Analysis | 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 Performance Modeling for Isolated Industrial Operations: A Python Framework for Data Envelopment Analysis Oghenero Orevaoghene Inana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7930805/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 Performance benchmarking in industrial systems is often limited by sparse data, confidentiality constraints, and the lack of comparable peer facilities. These challenges restrict the applicability of conventional data envelopment analysis (DEA), which is dependent on multiple decision-making units to define an efficiency frontier. This study introduces the PERNAGE Analytics System (PASy), a Python-based framework designed to extend DEA to single-facility or data-scarce environments. PASy integrates expert-driven weighting through the Analytic Hierarchy Process (AHP), synthetic data generation using domain-specific multipliers, and bounded DEA modeling to produce geometric efficiency scores. To improve interpretability, the results are translated into quartile-based performance bands rather than traditional scalar indices. The application to a representative industrial facility demonstrates that PASy can generate realistic synthetic comparators and identify polarized efficiency patterns, with units clustering around high and low performance levels. These results highlight both latent inefficiencies and achievable operational frontiers, reinforcing the diagnostic and managerial value of the framework. In general, PASy provides a transparent, reproducible, and scalable approach to efficiency evaluation in complex production settings where conventional peer-based benchmarking is infeasible. \vspace{1em}\noindent\textbf{Keywords:} Data Envelopment Analysis (DEA); Performance Benchmarking; Python; Synthetic Data; Efficiency; Industrial Operations Data Envelopment Analysis (DEA) Performance Benchmarking Python Synthetic Data Efficiency Industrial Operations Full Text Additional Declarations Competing interest reported. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors, and the author is currently employed as a contract field worker with Heritage Energy Operational Services Limited, HEOSL. The PASy (PERNAGE Analytics System) framework described in this paper was developed independently by the author as part of his consulting and research work under Adlore Business Enterprise Limited, a firm in which the author is a cofounder. The author affirms that HEOSL had no role in the conception, design, analysis, or preparation of this research, and no financial or professional interests of either organization have influenced the content of this manuscript. 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. 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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-7930805","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":549321161,"identity":"467a0f1b-6696-4235-a7a1-67d49ee5ac85","order_by":0,"name":"Oghenero Orevaoghene Inana","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABH0lEQVRIiWNgGAWjYBAC9gYQycbAIAFkSTwAsg2A+AA+LTwg2QMgLUCWRAJpWoDq4VrwAh7pw8cefyizsZec+fjhjYSKe3LmEukPDxdU1NkzSLdfwKqFLy3d4MC5tMTZ0mnGFglnio0tZ+QYHJ5x5nBig8yZAmxa7Hl4zCQOth1OkJNOMJNIbEtI3HAjh+Ewb9sBoDtzErDaAtHy315O8vg3kJb6DTfSHxzm/Qd0GH4tBxhnS/CAbUkwuJFgcJi3gZmxQSL9AHYtbGkSZ84lJ87sySkG+iXBcGfPG4PDPMcOJ7ZJ5GAPMR7mYxIVZXb2EsePb7zxoSJB3pw9/fFnnpo6e36J9AdY9eAGbAw8BOMIA7CTassoGAWjYBQMTwAA+dhizXVtMnEAAAAASUVORK5CYII=","orcid":"","institution":"Adlore Business Enterprise Limited","correspondingAuthor":true,"prefix":"","firstName":"Oghenero","middleName":"Orevaoghene","lastName":"Inana","suffix":""}],"badges":[],"createdAt":"2025-10-23 09:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7930805/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7930805/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96591148,"identity":"61b5657c-556d-49a6-bee3-51a763ee8d46","added_by":"auto","created_at":"2025-11-24 06:29:33","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4751,"visible":true,"origin":"","legend":"","description":"","filename":"9603440074f84462953d113589de09ad.json","url":"https://assets-eu.researchsquare.com/files/rs-7930805/v1/0390b7c7cfd12d78b94855a9.json"},{"id":96591147,"identity":"d7a1fb0c-29db-4eed-bae9-651d31341461","added_by":"auto","created_at":"2025-11-24 06:29:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":255321,"visible":true,"origin":"","legend":"","description":"","filename":"PerformanceModelingforIsolatedIndustrialOperations2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7930805/v1/440449e9077dbe5cf26918d0.pdf"},{"id":96591146,"identity":"3259b4b6-9aab-4c3a-8fb2-4bd6fbfa6e00","added_by":"auto","created_at":"2025-11-24 06:29:33","extension":"cls","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55857,"visible":true,"origin":"","legend":"","description":"","filename":"snjnl.cls","url":"https://assets-eu.researchsquare.com/files/rs-7930805/v1/be8d5cf61ad9bc4cdd20e9b6.cls"},{"id":96591149,"identity":"2f025e55-85b4-4cf7-a65f-a5cfd8a3c226","added_by":"auto","created_at":"2025-11-24 06:29:33","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122112,"visible":true,"origin":"","legend":"","description":"","filename":"9603440074f84462953d113589de09ad1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7930805/v1/1497274d970906e061a85fa8.xml"},{"id":98431137,"identity":"39b069f2-61cc-4dad-9ef9-2a51588a1917","added_by":"auto","created_at":"2025-12-17 16:47:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":358756,"visible":true,"origin":"","legend":"","description":"","filename":"PerformanceModelingforIsolatedIndustrialOperations2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7930805/v1_covered_9bb894b9-f6a2-40f2-893e-6a89b8d8a22f.pdf"}],"financialInterests":"Competing interest reported. 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