Scholarlink

<p>Digital twins (DTs) have become key enablers of industrial digitalization by supporting real-time monitoring, predictive capabilities, and data-driven decision-making across manufacturing systems. Despite rapid technological advances, the literature still lacks structured and replicable approaches to assess DT quality and support acquisition and selection processes in industrial contexts. Existing studies tend to address isolated aspects (e.g., data accuracy, interoperability, cybersecurity), limiting comparability and reproducibility across applications. This study proposes a structured and decision-oriented DT quality evaluation framework for acquisition and selection contexts, defined as the first stage of a broader DT lifecycle validation perspective. The proposed approach organizes heterogeneous quality criteria into a coherent evaluation structure tailored to pre-deployment analysis in industrial environments. A hybrid multi-criteria decision-making (MCDM) approach combining the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), implemented as TOPSIS-2N (two-step normalization), is applied to compute criteria weights and rank four generalized industrial DT platform categories (A₁–A₄). The framework comprises six core dimensions: accuracy of digital representation, interoperability, operational integration, data integrity and security, accessibility, and cost-effectiveness. The results show that combining hierarchical weighting with ideal-solution ranking provides a transparent and consistent prioritization of DT quality characteristics. In addition, the proposed approach enhances traceability between expert judgments, criteria weighting, and ranking outcomes, supporting industrial stakeholders in comparing and selecting DT solutions. The proposed framework addresses a relevant gap by providing a structured, decision-oriented, and reproducible approach to DT quality evaluation in industrial acquisition contexts.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A structured framework for defining and prioritizing quality criteria for industrial digital twins

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月13日

查看全文
  • Frankysia Faria Maciel,
  • Marcelo Rudek

摘要

Digital twins (DTs) have become key enablers of industrial digitalization by supporting real-time monitoring, predictive capabilities, and data-driven decision-making across manufacturing systems. Despite rapid technological advances, the literature still lacks structured and replicable approaches to assess DT quality and support acquisition and selection processes in industrial contexts. Existing studies tend to address isolated aspects (e.g., data accuracy, interoperability, cybersecurity), limiting comparability and reproducibility across applications. This study proposes a structured and decision-oriented DT quality evaluation framework for acquisition and selection contexts, defined as the first stage of a broader DT lifecycle validation perspective. The proposed approach organizes heterogeneous quality criteria into a coherent evaluation structure tailored to pre-deployment analysis in industrial environments. A hybrid multi-criteria decision-making (MCDM) approach combining the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), implemented as TOPSIS-2N (two-step normalization), is applied to compute criteria weights and rank four generalized industrial DT platform categories (A₁–A₄). The framework comprises six core dimensions: accuracy of digital representation, interoperability, operational integration, data integrity and security, accessibility, and cost-effectiveness. The results show that combining hierarchical weighting with ideal-solution ranking provides a transparent and consistent prioritization of DT quality characteristics. In addition, the proposed approach enhances traceability between expert judgments, criteria weighting, and ranking outcomes, supporting industrial stakeholders in comparing and selecting DT solutions. The proposed framework addresses a relevant gap by providing a structured, decision-oriented, and reproducible approach to DT quality evaluation in industrial acquisition contexts.