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• W2887488964 abstract "Workforce analytics involves using models that integrate internal and external data to predict future workforce and help organizations in any industry examine factors that have a prognostic effect. This paper assesses workforce modeling and prediction methods by examining their rationale, strengths, and constraints. It aims to identify enhancements for further development of workforce forecasting models and compares the capacity and reliability of different forecasting methods. Past and present modeling trends are described and critiqued based on their relevance to current requirements. Several approaches are reviewed, such as time series modeling and system dynamics simulation. Sensitivity analysis in models is assessed. The models are decomposed into three modes: supply‐based, demand‐based, and need‐based, which in some cases provide substantially different estimates of future workforce need. The chronological progression of models' development is analyzed. The articles are also classified based on the countries and the sectors that have paid great attention to workforce prediction research. Consideration of the use of workforce models and the inputs into such models is not within the scope of this paper." @default.
• W2887488964 created "2018-08-22" @default.
• W2887488964 creator A5028534030 @default.
• W2887488964 date "2018-08-17" @default.
• W2887488964 modified "2023-10-02" @default.
• W2887488964 title "Workforce forecasting models: A systematic review" @default.
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• W2887488964 doi "https://doi.org/10.1002/for.2541" @default.
• W2887488964 hasPublicationYear "2018" @default.
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