Business Process Monitoring Using a Robust Max-Half-Mchart Developed with Fast S Estimator

Awang Putra Sembada R (), Muhammad Ahsan (), Sischa Wahyuning Tyas (), Muhammmad Galang Satrio Wicaksono () and Nuchaila Ainiyah ()
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Awang Putra Sembada R: Universitas Pembangunan Nasional "Veteran" Jawa Timur
Muhammad Ahsan: Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
Sischa Wahyuning Tyas: Departement of Data Science, Faculty of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya, Indonesia
Muhammmad Galang Satrio Wicaksono: Faculty of Computer Science Universitas Pembangunan Nasional “Veteran” Jawa Timur
Nuchaila Ainiyah: Data Science Study Program, Telkom University, Surabaya Campus, Surabaya, Indonesia

Priviet Social Sciences Journal, 2026, vol. 6, issue 6, 65-76

Abstract: In a business environment, ensuring production processes plays a crucial role in a company's quality and stability. One tool that can be used to monitor the quality of business processes is a control chart. Control charts are useful tools for quickly monitoring a business process. Multivariate control charts are control charts that monitor several quality variables simultaneously. This is more effective than monitoring variables individually. There are control charts that can control the mean and covariance matrix of the process simultaneously, the tool used is a simultaneous multivariate control chart. Some commonly used methods are Max-Mchart, Max-MEWMA, Max-Half-Mchart. In addition to the method, it is also important to pay attention to the data in the business process. Data in business processes can contain outliers that cause classification errors. Therefore, a strong estimator is needed combined with a control chart to be resistant to outliers. The Fast S estimator is a robust estimator that has the ability to handle data containing outliers and combined with Max-Half-Mchart, a simultaneous control chart is good at detecting shifts in the production process. The results show that the Fast S estimator can detect six more out-of-control data points than the conventional method, which only detects two. There is a significant difference in detection rates between the Robust and non-Robust methods. These results indicate that the developed method is more sensitive than the method without the Robust estimator.

Keywords: Fast S; Max-Half-Mchart; Simultaneous Control Chart; business process control (search for similar items in EconPapers)
Date: 2026
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DOI: 10.55942/pssj.v6i6.1845

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