Employee attrition and absenteeism analysis using machine learning methods: Application in the manufacturing industry
- Title
- Employee attrition and absenteeism analysis using machine learning methods: Application in the manufacturing industry
- Creator
- Mansurali A.; Rajagopal M.; Subbaiah R.
- Description
- HR analytics has been envisaged as recent research trend for providing a comprehensive decision support system to the top level management in terms of employee's performance, recruitment and behaviour analysis. Globally, organizations are using technology to support and ease HR processes. Every organization should give maximum value to every available human resource, and they should minimize the attrition and absenteeism rate and ensure what are the factors that contribute towards employee attrition as well as the causes for workmen absenteeism. The ultimate objective is to correctly identify attrition and absenteeism in order to assist the company to improve retention tactics for key personnel and increase employee satisfaction. Through this chapter, a machine learning-based model is proposed to get quick results for such employee attrition and workmen absenteeism. The model is trained and tested for its accuracy. The result shows that the proposed model has high sensitivity. The managerial implications are also discussed for taking informed decisions. 2023, IGI Global. All rights reserved.
- Source
- HR Analytics in an Era of Rapid Automation, pp. 155-167.
- Date
- 2023-01-01
- Publisher
- IGI Global
- Coverage
- Mansurali A., Central University of Tamil Nadu, India; Rajagopal M., CHRIST University (Deemed), India; Subbaiah R., Jazan University, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-166848944-4; 978-166848942-0
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Mansurali A.; Rajagopal M.; Subbaiah R., “Employee attrition and absenteeism analysis using machine learning methods: Application in the manufacturing industry,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/18270.