IDEAS home Printed from https://ideas.repec.org/a/cmj/interc/y2022i49p43-51.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Hicham MAHDJOUBA & Sami Mohammed BENNOUNA & Afaf KHOUILED, 2022. "Predictive analytics for Human Resources through the application of Markov Chains: a case study of Cevital Food Processing Industry," Management Intercultural, Romanian Foundation for Business Intelligence, Editorial Department, issue 49, pages 43-51, December.
  • Handle: RePEc:cmj:interc:y:2022:i:49:p:43-51
    as

    Download full text from publisher

    File URL: http://seaopenresearch.eu/Journals/articles/MI_49_1.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    predictive analytics for Human Resources; Markov Chains; Human Resource Management; CEVITAL company;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs
    • M12 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Personnel Management; Executives; Executive Compensation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cmj:interc:y:2022:i:49:p:43-51. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Serghie Dan (email available below). General contact details of provider: https://seaopenresearch.eu/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.