Predicting employee absenteeism: A framework and its validation

The issue of employee absenteeism presents a continual obstacle to the effectiveness of organizations, especially within sectors that rely heavily on labor. This research introduces an innovative probabilistic framework that employs Bayesian Belief Networks (BBNs) to forecast the risk of absenteeism as well as the absenteeism rate, tackling the intricate and uncertain relationships among various contributing factors. By employing thorough factor analysis and drawing on expert insights, we systematically identify and organize essential variables spanning personal, organizational, familial, health-related, and external domains. The model integrates the insights of experts and addresses the challenge of limited data by employing a functional interpolation technique to develop conditional probability tables. Two models based on Bayesian Belief Networks have been developed: one assesses the probability of an employee being absent on a specific day, while the other predicts absenteeism rates on a monthly basis. The Monte Carlo simulation serves as a valuable tool for addressing the uncertainty inherent in essential input variables. The validation process, which includes scenario, sensitivity, and diagnostic analyses, highlights the strength and relevance of the models. This study presents a practical and scalable resource for decision-makers to evaluate and address absenteeism, particularly in contexts where data may be limited. • Develops two models to predict absenteeism risk and absenteeism rate. •Applies Bayesian Belief Networks to model uncertainty. •Introduces a structured expert elicitation approach. •Enables customization using factor analysis. •Validated through real-world manufacturing case study.