Employee turnover forecasting for human resource management based on time series analysis

an article by Xiaojuan Zhu, William Seaver, Rapinder Sawhney, Shuguang Ji, Bruce Holt, Gurudatt Bhaskar Sanil and Girish Upreti (University of Tennessee, Knoxville) published in Journal of Applied Statistics Volume 44 Issue 8 (2017)

Abstract

In some organizations, the hiring lead time is often long due to responding to human resource requirements associated with technical and security constrains. Thus, the human resource departments in these organizations are pretty interested in forecasting employee turnover since a good prediction of employee turnover could help the organizations to minimize the costs and impacts from the turnover on the operational capabilities and the budget.

This study aims to enhance the ability to forecast employee turnover with or without considering the impact of economic indicators. Various time series modelling techniques were used to identify optimal models for effective employee turnover prediction.

More than 11-years of monthly turnover data were used to build and validate the proposed models.

Compared with other models, a dynamic regression model with additive trend, seasonality, interventions, and a very important economic indicator effectively predicted the turnover with training R² = 0.77 and holdout R² = 0.59.

The forecasting performance of optimal models confirms that time series modelling approach has the ability to predict employee turnover for the specific scenario observed in our analysis.

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