Designing employee benefits to optimize turnover: A prescriptive analytics approach

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dc.coverageDOI: 10.1016/j.cie.2024.110582
dc.creatorLatorre, Paolo
dc.creatorLópez-Ospina, Héctor
dc.creatorMaldonado, Sebastián
dc.creatorGuevara, C. Angelo
dc.creatorPérez, Juan
dc.date2024
dc.date.accessioned2026-01-05T21:15:00Z
dc.date.available2026-01-05T21:15:00Z
dc.description<p>Employee turnover significantly impacts organizations, particularly those with substantial investments in training their workforce. To mitigate these effects, we propose a Prescriptive Human Resources Analytics approach that optimizes employee benefits to minimize total costs, focusing on turnover management The methodology models employee decision-making using a discrete choice model, with parameters estimated through maximum likelihood. We solve the resulting nonlinear optimization problem with a heuristic tailored to the problem's complexity. We applied this methodology to a hospital case study, which was used to enhance the transportation system as an employee benefit, considering the associated turnover costs. The results demonstrate that our approach can reduce total costs, optimize the usage level of the designed benefits, and increase employee satisfaction. This research provides a robust framework for data-driven decision-making in HR, offering practical tools for improving employee retention strategies.</p>eng
dc.identifierhttps://investigadores.uandes.cl/en/publications/cac7b561-bb46-483e-a6d7-cd063aff5c4e
dc.identifier.urihttps://repositorio.uandes.cl/handle/uandes/66380
dc.languageeng
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourcevol.197 (2024)
dc.subjectFacility location
dc.subjectHR analytics
dc.subjectNested logit
dc.subjectPrescriptive analytics
dc.subjectTurnover minimization
dc.titleDesigning employee benefits to optimize turnover: A prescriptive analytics approacheng
dc.typeArticleeng
dc.typeArtículospa
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