Modeling and prediction for optimal Human Resources Management. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Abbracciavento, Francesco; Formentin, Simone; Gualandi, Emanuela; Nanni, Rita; Paoli, Andrea; Savaresi, Sergio M.
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 16996
• Page End: 17001
• Publication Date: 2020
• Subjects: statistical analysis -- big data -- economics; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1250
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Human resources management is key for the retention and development of quality staff in modern companies. With the advent of big data and the recent boost in computing power, modeling and predictive analytics have shown their potential to increase HR-related performance, thus making the companies more competitive on the market via data-driven solutions. In this work, we develop a predictive model of the annual hourly cost per employee in big maintenance companies, which is usable for sales, marketing and HR purposes. With experimental real data, we show that such a model outperforms the typically employed solutions, by also allowing for an adaptive implementation using monthly updates.