Enhancing intellectual property identification and valuation in manufacturing through digital twins
研究了数字孪生技术如何帮助制造业（尤其是汽车制造商）更有效地识别和评估知识产权资产，发现实施后识别出的知识产权数量平均增加35%，总估值翻倍以上。
This study provides a transdisciplinary and empirical examination of the efficacy of digital twin technology in enhancing intellectual property (IP) identification and valuation within the manufacturing sector. Focusing on a sample of 51 automotive manufacturers in the United Kingdom and Brazil, we address a critical gap in the existing literature by offering empirical evidence of standardised digital twins' practical benefits in IP management, a domain that has been predominantly theoretical until now. Using a mixed-methods research design, we employed 1) A standardized digital twin sub-model for representing IP assets using Asset Administration Shell principles; and 2) Questionnaires assessing current IP identification practices and perceived IP asset values before and after digital twin implementation. Utilizing the Income Approach and the Relief from Royalty Method in adherence to International Valuation Standards, our findings reveal a significant increase in both the number of identified IP assets and their overall valuation post-implementation. We empirically assess the impact of digital twins on IP practices in manufacturing by integrating engineering, legal and innovation management perspectives. Reliability and validity of the results are underpinned by a rigorous systematic methodology, including appropriate statistical analyses and thematic examinations of participant feedback. Across the 51 automotive manufacturers participating in this study, the mean number of identified IP assets rose by 35 % (from 72 to 98 assets, Z = −5.63, p < 0.001) and the mean portfolio valuation more than doubled from USD 23.2m–53.8m (Z = −5.22, p < 0.001). These results demonstrate the method's ability to surface hidden intangible value that can subsequently be monetised.