Predictive analytics for Human Resources through the application of Markov Chains: a case study of Cevital Food Processing Industry

Hicham Mahdjouba, Sami Mohammed Bennouna and Afaf Khouiled
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Hicham Mahdjouba: Faculty of Economic, Ain Temouchent University, Algeria
Sami Mohammed Bennouna: Faculty of Economic, Ain Temouchent University, Algeria
Afaf Khouiled: Faculty of Economics, Ouargla University, Algeria

Management Intercultural, 2022, issue 49, 43-51

Abstract: The study focuses on the application of Markov chains to forecast the human resources of Cevital Food Processing Industry. Markov chains are probabilistic models used to anticipate future trends based on the current state and probable transitions. By utilizing historical data on workforce and personnel movements, a robust predictive model was developed. The results reveal a distribution of human resources for the upcoming years, obtained by multiplying the probabilistic transition matrix with the 2019 workforce matrix. The study highlights the significance of efficient human resource planning for business success and underscores the promising use of Markov chains in this field.

Keywords: predictive analytics for Human Resources; Markov Chains; Human Resource Management; CEVITAL company (search for similar items in EconPapers)
JEL-codes: C20 J21 J24 J63 M12 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:cmj:interc:y:2022:i:49:p:43-51

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