Developing a data-driven technology roadmapping method using generative adversarial network (GAN)

• Published in: Computers in industry Vol. 145; p. 103835
• Main Authors: Kim, Sunhye, Jang, Hyejin, Yoon, Byungun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2023
• Subjects: Deep learning; Generative Adversarial Network (GAN); Subject–Action–Object (SAO) structure; Technology management; Technology Roadmapping; Deep learning; Technology Roadmapping; Technology management; Subject–Action–Object (SAO) structure; Generative Adversarial Network (GAN)
• ISSN: 0166-3615
• DOI: 10.1016/j.compind.2022.103835

The technology roadmap is used as an essential tool to establish strategies. Existing expert-based and data-based roadmapping processes consume a lot of resources, are difficult to update, and can be biased towards experts’ subjectivity. To overcome these limitations, this study proposes an automated deep learning model Generative Adversarial Network (GAN) based technology roadmapping method. The proposed framework consists of two modules. Module 1 uses the GAN model to train the Roadmap, and trains the GAN model by the knowledge of experts in the technology roadmap using the existing technology roadmap data. The defined model consists of a generator that receives the node of the technology roadmap and generates the next node, and discriminators that verify the generated node, and provide feedback. Module 2 expands the roadmap, generates candidate nodes using the trained GAN model, and merges suitable nodes into the roadmap. To select the appropriate configuration for the proposed framework, experiments were conducted in terms of data format and model layer. Also, to validate the model presented in this study, the analysis was performed by selecting the renewable energy industry as a target technology of the analysis. •This paper proposes an automated deep learning model based technology roadmapping method.•The suggested approach uses the GAN model to train the Roadmap.•We expand an existing roadmap by generating candidate nodes using the trained GAN model.•The proposed model is validated by applying it to the renewable energy industry.