Framework for Problem-Oriented Identification of New Technologies

In the context of accelerated technological evolution, industries are under pressure to identify and adopt new technologies in order to maintain competitiveness. Conventional keyword-based technology scouting techniques are frequently inadequate, particularly in the context of complex industrial challenges that necessitate integrated solutions encompassing multiple complementary technologies. This paper introduces a flexible and systematic framework for problem-oriented technology identification that integrates both manual and natural language processing (NLP)-based approaches. The framework is comprised of four principal stages. The four stages of the framework are as follows: Problem Identification, Problem Abstraction, Identification of Technologies, and Creation of Technology Bundles. By transforming specific industrial problems into abstract representations, the framework facilitates a comprehensive search for relevant technologies across various domains. The incorporation of NLP methodologies enables the processing of extensive unstructured data sets, thereby enhancing the capacity to identify technologies based on semantic similarity rather than mere keyword matches. Although the framework has not yet been validated, it has been demonstrated in identifying coating and drying technologies in battery cell manufacturing. Its potential lies in overcoming the limitations of traditional methods by enabling the identification of both individual technologies and their combinations to address complex industry challenges effectively. Future research will focus on validating the framework through real-world applications and developing software tools to automate the process. In conclusion, the proposed framework offers a comprehensive approach to problem-oriented technology identification.