Developing an Algorithmic HR Governance Model: Mitigating Supervisor Bias and Structural Stagnation Using Fuzzy Logic and Signal Processing Analogy

ABSTRACT

Traditional performance evaluations suffer from systemic manager bias and structural stagnation. This study develops an interdisciplinary human resource (HR) governance framework to resolve these flaws. Drawing an analogy to electrical engineering, supervisor bias represents harmonic signal noise. To extract the true output signal, we design a 40/60 weighting mechanism using a Mamdani Fuzzy Inference System and automated enterprise data. The framework operates like a full‐wave bridge rectifier circuit. Empirically deployed over 12 months across two manufacturing plants (1800 and 1200 employees), the model successfully stabilized distorted evaluations. High‐performer retention rates rose up to 88.0%. Factory line productivity increased by 13.0% in overall equipment effectiveness (OEE). This study demonstrates how automated mathematical governance protects human capital and optimizes industrial workflows.