Cargando…

Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming

In this paper, we propose a comprehensive analytics framework that can serve as a decision support tool for HR recruiters in real-world settings in order to improve hiring and placement decisions. The proposed framework follows two main phases: a local prediction scheme for recruitments' succes...

Descripción completa

Detalles Bibliográficos
Autores principales: Pessach, Dana, Singer, Gonen, Avrahami, Dan, Chalutz Ben-Gal, Hila, Shmueli, Erez, Ben-Gal, Irad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7252110/
https://www.ncbi.nlm.nih.gov/pubmed/32501316
http://dx.doi.org/10.1016/j.dss.2020.113290
_version_ 1783539092309409792
author Pessach, Dana
Singer, Gonen
Avrahami, Dan
Chalutz Ben-Gal, Hila
Shmueli, Erez
Ben-Gal, Irad
author_facet Pessach, Dana
Singer, Gonen
Avrahami, Dan
Chalutz Ben-Gal, Hila
Shmueli, Erez
Ben-Gal, Irad
author_sort Pessach, Dana
collection PubMed
description In this paper, we propose a comprehensive analytics framework that can serve as a decision support tool for HR recruiters in real-world settings in order to improve hiring and placement decisions. The proposed framework follows two main phases: a local prediction scheme for recruitments' success at the level of a single job placement, and a mathematical model that provides a global recruitment optimization scheme for the organization, taking into account multilevel considerations. In the first phase, a key property of the proposed prediction approach is the interpretability of the machine learning (ML) model, which in this case is obtained by applying the Variable-Order Bayesian Network (VOBN) model to the recruitment data. Specifically, we used a uniquely large dataset that contains recruitment records of hundreds of thousands of employees over a decade and represents a wide range of heterogeneous populations. Our analysis shows that the VOBN model can provide both high accuracy and interpretability insights to HR professionals. Moreover, we show that using the interpretable VOBN can lead to unexpected and sometimes counter-intuitive insights that might otherwise be overlooked by recruiters who rely on conventional methods. We demonstrate that it is feasible to predict the successful placement of a candidate in a specific position at a pre-hire stage and utilize predictions to devise a global optimization model. Our results show that in comparison to actual recruitment decisions, the devised framework is capable of providing a balanced recruitment plan while improving both diversity and recruitment success rates, despite the inherent trade-off between the two.
format Online
Article
Text
id pubmed-7252110
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Elsevier B.V.
record_format MEDLINE/PubMed
spelling pubmed-72521102020-05-28 Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming Pessach, Dana Singer, Gonen Avrahami, Dan Chalutz Ben-Gal, Hila Shmueli, Erez Ben-Gal, Irad Decis Support Syst Article In this paper, we propose a comprehensive analytics framework that can serve as a decision support tool for HR recruiters in real-world settings in order to improve hiring and placement decisions. The proposed framework follows two main phases: a local prediction scheme for recruitments' success at the level of a single job placement, and a mathematical model that provides a global recruitment optimization scheme for the organization, taking into account multilevel considerations. In the first phase, a key property of the proposed prediction approach is the interpretability of the machine learning (ML) model, which in this case is obtained by applying the Variable-Order Bayesian Network (VOBN) model to the recruitment data. Specifically, we used a uniquely large dataset that contains recruitment records of hundreds of thousands of employees over a decade and represents a wide range of heterogeneous populations. Our analysis shows that the VOBN model can provide both high accuracy and interpretability insights to HR professionals. Moreover, we show that using the interpretable VOBN can lead to unexpected and sometimes counter-intuitive insights that might otherwise be overlooked by recruiters who rely on conventional methods. We demonstrate that it is feasible to predict the successful placement of a candidate in a specific position at a pre-hire stage and utilize predictions to devise a global optimization model. Our results show that in comparison to actual recruitment decisions, the devised framework is capable of providing a balanced recruitment plan while improving both diversity and recruitment success rates, despite the inherent trade-off between the two. Elsevier B.V. 2020-07 2020-04-03 /pmc/articles/PMC7252110/ /pubmed/32501316 http://dx.doi.org/10.1016/j.dss.2020.113290 Text en © 2020 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Pessach, Dana
Singer, Gonen
Avrahami, Dan
Chalutz Ben-Gal, Hila
Shmueli, Erez
Ben-Gal, Irad
Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming
title Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming
title_full Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming
title_fullStr Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming
title_full_unstemmed Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming
title_short Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming
title_sort employees recruitment: a prescriptive analytics approach via machine learning and mathematical programming
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7252110/
https://www.ncbi.nlm.nih.gov/pubmed/32501316
http://dx.doi.org/10.1016/j.dss.2020.113290
work_keys_str_mv AT pessachdana employeesrecruitmentaprescriptiveanalyticsapproachviamachinelearningandmathematicalprogramming
AT singergonen employeesrecruitmentaprescriptiveanalyticsapproachviamachinelearningandmathematicalprogramming
AT avrahamidan employeesrecruitmentaprescriptiveanalyticsapproachviamachinelearningandmathematicalprogramming
AT chalutzbengalhila employeesrecruitmentaprescriptiveanalyticsapproachviamachinelearningandmathematicalprogramming
AT shmuelierez employeesrecruitmentaprescriptiveanalyticsapproachviamachinelearningandmathematicalprogramming
AT bengalirad employeesrecruitmentaprescriptiveanalyticsapproachviamachinelearningandmathematicalprogramming