Adaptive Statistical Process Control by Managing Uncertainty in Process Monitoring

Ufuk Cebeci; Uğur Şimşir

Adaptive Statistical Process Control by Managing Uncertainty in Process Monitoring

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

References

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Details

Primary Language

English

Subjects

Fuzzy Computation

Journal Section

Research Article

Authors

Ufuk Cebeci ^*
0000-0003-4367-6206
Türkiye

Uğur Şimşir
0000-0002-0948-6364
Türkiye

Publication Date

October 1, 2025

Submission Date

August 26, 2025

Acceptance Date

September 12, 2025

Published in Issue

Year 2025 Volume: 5 Number: 2

IZ

https://izlik.org/JA32LN45DX

Cite

RIS / Bibtex

APA

Cebeci, U., & Şimşir, U. (2025). Adaptive Statistical Process Control by Managing Uncertainty in Process Monitoring. Artificial Intelligence Theory and Applications, 5(2), 11-32. https://izlik.org/JA32LN45DX

AMA

1.Cebeci U, Şimşir U. Adaptive Statistical Process Control by Managing Uncertainty in Process Monitoring. AITA. 2025;5(2):11-32. https://izlik.org/JA32LN45DX

Chicago

Cebeci, Ufuk, and Uğur Şimşir. 2025. “Adaptive Statistical Process Control by Managing Uncertainty in Process Monitoring”. Artificial Intelligence Theory and Applications 5 (2): 11-32. https://izlik.org/JA32LN45DX.

EndNote

Cebeci U, Şimşir U (October 1, 2025) Adaptive Statistical Process Control by Managing Uncertainty in Process Monitoring. Artificial Intelligence Theory and Applications 5 2 11–32.

IEEE

[1]U. Cebeci and U. Şimşir, “Adaptive Statistical Process Control by Managing Uncertainty in Process Monitoring”, AITA, vol. 5, no. 2, pp. 11–32, Oct. 2025, [Online]. Available: https://izlik.org/JA32LN45DX

ISNAD

Cebeci, Ufuk - Şimşir, Uğur. “Adaptive Statistical Process Control by Managing Uncertainty in Process Monitoring”. Artificial Intelligence Theory and Applications 5/2 (October 1, 2025): 11-32. https://izlik.org/JA32LN45DX.

JAMA

1.Cebeci U, Şimşir U. Adaptive Statistical Process Control by Managing Uncertainty in Process Monitoring. AITA. 2025;5:11–32.

MLA

Cebeci, Ufuk, and Uğur Şimşir. “Adaptive Statistical Process Control by Managing Uncertainty in Process Monitoring”. Artificial Intelligence Theory and Applications, vol. 5, no. 2, Oct. 2025, pp. 11-32, https://izlik.org/JA32LN45DX.

Vancouver

1.Ufuk Cebeci, Uğur Şimşir. Adaptive Statistical Process Control by Managing Uncertainty in Process Monitoring. AITA [Internet]. 2025 Oct. 1;5(2):11-32. Available from: https://izlik.org/JA32LN45DX