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dc.creatorTaco Arias, Marlon Stalinen
dc.date2026-06-30
dc.date.accessioned2026-08-03T16:43:31Z
dc.date.available2026-08-03T16:43:31Z
dc.identifierhttps://imjeta.org/index.php/IMJETA/article/view/Automating-Data-Envelopment-Analysis-in-Python
dc.identifier10.67294/j87y4t65
dc.identifier.urihttp://bdigital2.ula.ve:8080/xmlui/654321/26933
dc.descriptionThis study evaluated whether a reproducible Python workflow can strengthen Data Envelopment Analysis in industrial efficiency studies when compared with spreadsheet-based tools such as XlDEA/XIDEA. A methodological, documentary, and computational comparative design was applied. The study examined two implementation environments: a reproducible Python workflow and spreadsheet-based analysis tools. Data were collected through a structured comparison matrix that assessed methodological coverage, automation and scalability, reproducibility and auditability, and second-stage inferential robustness. The analytical procedure reviewed input-oriented CCR estimation, bootstrap inference, Tobit modeling on inefficiency, truncated regression with double bootstrap, and automated report generation. The main result indicates that Python provides a more scalable and auditable architecture for repeated analysis, especially when monthly data, multiple decision-making units, and standardized outputs are required. However, spreadsheet tools remain useful for exploratory applications because they offer greater initial accessibility for non-programming users. The study concludes that Python is preferable for production-grade efficiency analysis, while truncated regression with double bootstrap should guide future second-stage inference when contextual determinants of efficiency are analyzed.en
dc.formatapplication/pdf
dc.languageen
dc.publisherGrupo Afronta C.A.en
dc.relationhttps://imjeta.org/index.php/IMJETA/article/view/Automating-Data-Envelopment-Analysis-in-Python/20
dc.rightsCopyright (c) 2026 Marlon Stalin Taco Arias (Author)en
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0en
dc.sourceInternational Multidisciplinary Journal of Emerging Technologies and Applications; Vol. 1 No. 3 (2026); 79-94en
dc.source3135-6214
dc.subjectData Envelopment Analysisen
dc.subjectPython Workflowen
dc.subjectTechnical Efficiencyen
dc.subjectBootstrap Inferenceen
dc.subjectTruncated Regressionen
dc.titleAutomating Data Envelopment Analysis in Python: Functional Comparison with XlDEA/XIDEA and Methodological Assessment of Second-Stage Inferenceen
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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