Adaptive Statistical Process Control by Managing Uncertainty in Process Monitoring

Year 2025, Volume: 5 Issue: 2, 11 - 32, 01.10.2025

Ufuk Cebeci , Uğur Şimşir

Abstract

Statistical Process Control (SPC) is a widely used methodology for monitoring and improving process stability and quality. However, traditional SPC techniques rely on crisp control limits, which may be insufficient when dealing with uncertainty, variability, or imprecise data in real-world environments. This study introduces a fuzzy logic-based framework to enhance SPC by incorporating flexible and adaptive control mechanisms. In the proposed approach, process parameters such as mean, standard deviation, defect rate, and cycle time are transformed into fuzzy linguistic variables. A fuzzy inference system (FIS) is then designed to evaluate process conditions using expert-defined rules, providing an interpretative and continuous assessment of process stability. Unlike traditional control charts, which classify a process as "in control" or "out of control," the fuzzy SPC approach allows intermediate states such as "partially stable" or "at risk," thereby enabling proactive intervention before severe deviations occur. The results demonstrate that fuzzy SPC provides greater robustness in handling uncertain data and offers a more realistic and actionable decision support system for quality management.

Keywords

process monitoring , fuzzy inference system , process control

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