Process Capability Analysis of Prediction Data of ML Algorithms

Year 2024, , 208 - 220, 31.08.2024

Tuğçe Altuntaş , Abdulkadir Atalan

https://doi.org/10.38009/ekimad.1519608

Abstract

This study integrates process capability analysis with Machine Learning (ML) methods to optimize business processes. ML, especially Random Forest (RF) and k-nearest neighbor (kNN) algorithms, has enabled the practical analysis of large data sets by using them together with process capability analysis. This integration enabled real-time monitoring and predictive analytics, enabling the proactive identification of process variations and the making of timely adjustments to maintain or increase process capability. Additionally, ML algorithms have helped optimize process parameters and identify critical factors affecting process performance, allowing for continuous improvement and achieving desired quality standards with greater efficiency. In conclusion, this study provides the basis for the synergy between process capability analysis and ML methods to enable businesses to achieve higher levels of quality control, productivity, and competitiveness in dynamic and complex production environments.

Keywords

Process Capability Analysis, Machine Learning, Random Forest

ML Algoritmalarının Tahmin Verilerine ait Süreç Yeteneği Analizi

Abstract

Bu çalışma, iş süreçlerini optimize etmek için process yetenek analizini Makine Öğrenimi (ML) yöntemleriyle entegre etmekteyi amaçlamıştır. ML, özellikle Rastgele Orman (RF) ve k-en yakın komşu (kNN) algoritmaları, süreç yetenek analizi ile birlikte kullanılarak büyük veri setlerinin pratik analizine olanak sağlamıştır. Bu entegrasyon, gerçek zamanlı izleme ve tahmine dayalı analitiği mümkün kılarak süreç değişimlerinin proaktif olarak belirlenmesine ve süreç yeterliliğini korumak veya artırmak için zamanında ayarlamalar yapılmasına olanak sağladı. Ek olarak, ML algoritmaları süreç parametrelerinin optimize edilmesine ve süreç performansını etkileyen kritik faktörlerin belirlenmesine yardımcı olarak sürekli iyileştirmeye ve istenen kalite standartlarına daha yüksek verimlilikle ulaşılmasına olanak tanıdı. Sonuç olarak bu çalışma, işletmelerin dinamik ve karmaşık üretim ortamlarında daha yüksek düzeyde kalite kontrol, üretkenlik ve rekabet gücü elde etmelerini sağlamak için süreç yetenek analizi ile ML yöntemleri arasındaki sinerjinin temelini oluşturmaktadır.

Keywords

Süreç Yeteneği Analizi, Makine Öğrenimi, Rastgele Orman, k-En Yakın Komşu

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