Adaptive Type-1 Fuzzy Logic-Based System for Predicting Employee Attrition
Keywords:
Type-1fuzzy logic, employee attrition, attrition prediction ratesAbstract
One of the challenges encountered by employers is the loss of skilled and competent employees. Organizations invest significant efforts in talent acquisition and development, and losing employees implies the loss of resources. This predicament requires novel pre-emptive strategies to curb high turnover rates, thereby minimizing attrition. Recent AI-based interventions have emerged as pivotal tools, offering valuable insights and predictive potential to determine the factors underlying employee attrition. Thus, adequately competent talent retention strategies can be formulated and implemented. This paper proposes a novel model based on predictive type-1 fuzzy logic that can learn the likelihood of employee attrition based on employee characteristics as well as the organizational milieu to aid human resources managers in the implementation of robust retention tactics. We validated the efficacy of our model by conducting experiments that included 72 participants at a Saudi university and analyzing the participant responses. The promising outcomes of our proposed system demonstrate the anticipatory power of estimating attrition prediction rates, underscoring the capability of the system to handle attrition. These outcomes are delivered at a lower average error rate and standard deviation, validating our model's capacity to navigate inherent uncertainties while predicting attrition.
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