Author(s): Pierre Dupont

Abstract: The growing adoption of Digital Twin (DT) technology has opened new possibilities for redefining asset valuation methodologies in the context of smart asset management. This research explores the integration of DT frameworks into the valuation process, aiming to enhance financial decision-making through real-time operational intelligence. A hybrid methodological approach was employed, combining quantitative simulations, predictive modeling, and expert validation across three asset classes—manufacturing equipment, building infrastructure, and energy grid systems. The proposed DT-integrated valuation model incorporates live performance data such as degradation rates, energy efficiency, and downtime probability into traditional discounted cash flow (DCF) frameworks. Statistical analyses using regression and Monte Carlo simulations revealed significant improvements in valuation accuracy, with Mean Absolute Percentage Error (MAPE) reductions ranging from 5-8 percentage points and R² increases of up to 0.23. Forecast uncertainty decreased by approximately 40%, confirming the DT model’s superior predictive stability. Sensitivity assessments identified energy efficiency, degradation rate, and downtime probability as dominant determinants of asset value. The findings indicate that DT integration enables dynamic valuation updates, improved transparency, and a more resilient response to operational variability. This research thus introduces a novel paradigm that unifies engineering performance with financial valuation, providing actionable insights for industries transitioning toward data-driven asset management under the Industry 4.0 framework. The study concludes that DT-based valuation models can serve as strategic decision tools, fostering adaptive, real-time, and evidence-based asset management systems that outperform conventional static valuation methods.

Pages: 46-50 | Views: 22 | Downloads: 14

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