A Probabilistic-Driven Approach for Early Design Quality Risk and Crux Identification Using Non-Markovian Stochastic Petri Nets

A Probabilistic-Driven Approach for Early Design Quality Risk and Crux Identification Using Non-Markovian Stochastic Petri Nets | 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 A Probabilistic-Driven Approach for Early Design Quality Risk and Crux Identification Using Non-Markovian Stochastic Petri Nets Van-Dung Truong, William Brace, Gabriele Benzoni, Antonio Cammi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7101379/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Nov, 2025 Read the published version in Research in Engineering Design → Version 1 posted 9 You are reading this latest preprint version Abstract Stochastic Petri Net (SPN) is a powerful and widely used approach for modelling, deadlock detection and safety analysis of stochastic processes in complex stochastic systems. However, applying this method is rarely seen in formal Risk Analysis, especially for quality risks, which refer to the system’s capability and performance to fulfil its objectives on time. This paper investigates the potential of applying Risk Analysis in the early conceptual design stages by modelling the design problem using non-Markovian SPN as a formal analytical tool. The use of SPN offers several advantages, such as high flexibility and strong compatibility with statistical methods such as performance analysis with semi-Markov models, as well as sensitivity analysis and uncertainty analysis. The proposed method enables designers to address quality risks quantitatively and focus explicitly on the design crux problem - the key quality issue directly affecting the design's success. Preferred high-level solutions obtained throughout the Risk Analysis are evaluated using Monte Carlo simulations as initial decision-making insights. A case study of the concept development of a remote maintenance system for the In-Bioshield area of the DEMO fusion power plant is presented to demonstrate the method’s applicability. Initial results showed potential in identifying quality risks, addressing key factors contributing to the design problem, and finding optimal design specifications in the early stages. conceptual design quality risks stochastic Petri Net non-markovian stochastic Petri Net sensitivity analysis uncertainty analysis semi-markov processes performance analysis risk analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Nov, 2025 Read the published version in Research in Engineering Design → Version 1 posted Editorial decision: Revision requested 19 Sep, 2025 Reviews received at journal 13 Aug, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviews received at journal 30 Jul, 2025 Reviewers agreed at journal 24 Jul, 2025 Reviewers invited by journal 23 Jul, 2025 Editor assigned by journal 19 Jul, 2025 Submission checks completed at journal 16 Jul, 2025 First submitted to journal 11 Jul, 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7101379","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490341456,"identity":"5eddfc95-4eb4-433c-9fda-50f9a1fdd5be","order_by":0,"name":"Van-Dung 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