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<p>Small and medium-sized enterprises (SMEs) face persistent challenges in adopting digital twin (DT) technologies due to limited resources and low digital maturity. In this research, the SME Smart Technology Readiness Assessment (SSTRA) method is adapted specifically for DT implementation and suggests a hierarchical taxonomy of three fundamental dimensions: real-time monitoring, predictive simulation and analytics, and system integration. The research contribution lies in extending SSTRA with DT-specific criteria, such as Predictive Modeling and data interoperability, generally absent in general readiness models. In order to rank the most important factors for DT readiness, evaluation weights were computed using the Analytic Hierarchy Process (AHP), using the inputs from 35 academic and industry experts in the field. The framework was validated through expert-led workshops and comparative benchmarking of 18 enabling technologies, scored against established best practices. A Brazilian textile SME case study had a readiness index of <i>R</i> = 1.06, which categorized it as a “Beginner” level. Strengths were found in data visualization, and key gaps were identified in simulation capability and interoperability infrastructure. The model assisted in decision-making for infrastructure improvement and phased DT roadmap implementation. Unlike more general readiness models, this is an implementable and scalable way forward for SMEs attempting digital transformation within constrained circumstances.</p>

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A technology readiness assessment approach for Digital Twin implementation in SMEs

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • Stefano de Larmelina,
  • Alessandro Lucas da Silva,
  • Lucas Antonio Risso

摘要

Small and medium-sized enterprises (SMEs) face persistent challenges in adopting digital twin (DT) technologies due to limited resources and low digital maturity. In this research, the SME Smart Technology Readiness Assessment (SSTRA) method is adapted specifically for DT implementation and suggests a hierarchical taxonomy of three fundamental dimensions: real-time monitoring, predictive simulation and analytics, and system integration. The research contribution lies in extending SSTRA with DT-specific criteria, such as Predictive Modeling and data interoperability, generally absent in general readiness models. In order to rank the most important factors for DT readiness, evaluation weights were computed using the Analytic Hierarchy Process (AHP), using the inputs from 35 academic and industry experts in the field. The framework was validated through expert-led workshops and comparative benchmarking of 18 enabling technologies, scored against established best practices. A Brazilian textile SME case study had a readiness index of R = 1.06, which categorized it as a “Beginner” level. Strengths were found in data visualization, and key gaps were identified in simulation capability and interoperability infrastructure. The model assisted in decision-making for infrastructure improvement and phased DT roadmap implementation. Unlike more general readiness models, this is an implementable and scalable way forward for SMEs attempting digital transformation within constrained circumstances.