Statistical Inference on a Flexible Loss-based Capability Index for Normal Processes

Statistical Inference on a Flexible Loss-based Capability Index for Normal Processes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Statistical Inference on a Flexible Loss-based Capability Index for Normal Processes Abbas Parchami, Hamideh Iranmanesh, Mehdi Jabbari Nooghabi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5211298/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Feb, 2026 Read the published version in Soft Computing → Version 1 posted 4 You are reading this latest preprint version Abstract This article presents a new index for evaluating the manufacturing processes based on a non-symmetric and flexible loss function. The proposed index offers flexibility in capturing varied process deviations and aligns with practical manufacturing requirements. A combination of the squared error and absolute error loss functions can be considered as the model for the loss function. The Monte Carlo simulation procedure is discussed and investigated for the proposed loss-based index in three significant statistical problems, including: (1) the point estimation of the loss-based process capability index, (2) the construction of confidence intervals for the capability index, and (3) testing the capability on the basis of the non-symmetric loss function. To demonstrate the practicality and implementation of the proposed simulation methodology, illustrative examples are derived from a pipe manufacturing industry. These results exemplify the utility of the loss-based process capability index in addressing real-world challenges, such as assessing process capability and ensuring quality compliance. Loss-based index Confidence interval Testing hypotheses Simulation Procedure Full Text Supplementary Files svjour3.cls Cite Share Download PDF Status: Published Journal Publication published 17 Feb, 2026 Read the published version in Soft Computing → Version 1 posted Reviewers agreed at journal 14 Apr, 2025 Reviewers invited by journal 07 Apr, 2025 Editor assigned by journal 07 Apr, 2025 First submitted to journal 07 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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