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Designing a demand forecasting model for human resources cloud data centres using fuzzy theory

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Abstract

Wireless networks have become increasingly important in the human resources industry, with cloud data centres playing a critical role in commercial information infrastructure and human resource procedures. Effective human resource management is essential for modern enterprises, so proper human resource planning is crucial for smooth and rapid growth. To achieve this, enterprises must predict the supply and demand of future human resources based on development needs and external environmental factors and formulate a human resource plan that meets their development needs. However, the success or failure of human resource planning depends on accurately forecasting human resource demand. The wireless human resource cloud data centres provide numerous advantages over traditional data centres, including flexibility, efficiency, scalability, security, and data analysis capabilities. Forecasting human resource demand through these centres remains difficult because traditional forecasting techniques can be less effective, leading to inaccurate forecasts. To address this issue, this study proposes a model that combines fuzzy categorization theory with sophisticated forecasting methods to forecast human resource demand. To enhance forecasting reliability and accuracy by efficiently utilizing the data resources of wireless human resource cloud data centres and taking into account a wide range of influencing factors. Industries that use wireless HR cloud data centres are more likely to stay ahead of the competition. It uses fuzzy classification theory and applies fuzzy classification methods to later-stage forecasting to overcome the limitations of traditional forecasting methods, which sample changes may disrupt. The study also uses the Markov chain method, which is suitable for predicting system objects with large random fluctuations. The effectiveness and feasibility of the model are demonstrated by analyzing and verifying the research object's data. This study provides a reliable and effective forecasting method for human resource demand using wireless cloud data centres, which can assist organizations in making data-driven decisions and enhancing their competitive advantage in the market.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Authors and Affiliations

  1. Hebei University of Economics and Business, Shi Jiazhang, 050061, Hebei, China

    Xiao Ming Wu & Xiao Mei Chu

  2. School of Economics and Management, Hebei University of Economics and Business, Shi Jiazhang, 050061, Hebei, China

    Zhan Xin Guo

  3. Business Department, Semyung University, Jecheon, 27136, South Korea

    Yoo Seungeok

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  1. Xiao Ming Wu
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  2. Zhan Xin Guo
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  4. Yoo Seungeok
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Correspondence to Xiao Ming Wu.

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Wu, X.M., Guo, Z.X., Chu, X.M. et al. Designing a demand forecasting model for human resources cloud data centres using fuzzy theory. Wireless Netw 29, 3417–3433 (2023). https://doi.org/10.1007/s11276-023-03423-4

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