Research objectives and hypothesis/research questions:
In this study, there are two theorized foundations: First-the adopting of Digital Twin technology has a positive impact on the financial performance of the case subject company. Second-the differences in financial performance, measured by operating profit and revenues, before and after the adoption of Digital Twin technology are statistically significant.

Research methods:
The use of dummy variable regression analysis and independent T test to validate the impact of digital twin on financial performance.

Main results:
The empirical results of this study demonstrate that the adoption of Digital Twin (DT) technology has had a substantial and statistically significant impact on Huawei’s financial performance. Both operating profit and total revenues increased markedly in the years following DT implementation in 2015, with regression and T-test analyses confirming the robustness of these trends. These results indicate a financial improvement that closely follows the predicted advantages to be achieved through the digital twin approach, such as increased operational efficiency, preventive maintenance, and data-driven decision-making. These findings are in agreement with existing literature emphasizing the economic and strategic importance of DT technologies. For instance, improvements in forecasting and operational cost savings due to DT adoption were cited by Bassey, Opoku-Boateng, Antwi et al. (2024) and Saini, Sharma, Mathur et al. (2022) in the case of the energy and building industries. Likewise, Bányai and Kovács (2023) reported significant improvements in ROI and NPV due to RFID-based digital twins in manufacturing contexts, mirroring the pattern seen with Huawei’s rising profits and revenues. Again, Korepin, Mohamed, Zhaksylyk et al. (2024) and Svadkovsky (2024) documented strong revenue growth in high-tech sectors after the DT integration, giving more supporting evidence to the generalization of our results to digital-intensive industries.

Implications for theory and practice:
Because of the constraints, future research would concentrate on multi-firm, multi-industry datasets to validate and generalize findings across diverse contexts. Sector-specific drivers, barriers, and enabling conditions for successful DT would also merit attention. A further combining of financial metrics with qualitative assessments could contrive to a more complete view regarding how digital twins affect firm behavior as well as long-term competitiveness.