Exploring the Development of Research, Technology and Business of Machine Tool Domain in New-Generation Information Technology Environment Based on Machine Learning

Is There a Smart Sustainability Transition in Manufacturing? Tracking Externalities in Machine Tools over Three Decades

Only one third of studies on the Industry 4.0–sustainability link have been conducted in manufacturing, despite its centrality to “ensuring sustainable consumption and production patterns” (UN Sustainable Development Goal nr. 12). The European Ecodesign Directive singled out machine tools as key to the sustainability transition, not least due to their high energy usage and their increasingly becoming enmeshed in cyber-physical production systems. This paper aims to find out whether the digital transformation underway in machine tools is sustainable as well as to identify its central technological pathways. Externalities in machine tools are tracked over three decades (1990–2018) by means of a multi-method setting: (1) mapping the Technological Innovation System (TIS) of machine tools; (2) co-occurrence analysis of transnational patent families, in order to reduce geographical and market distortions (Questel’s FAMPAT); and (3) analysis of the incidence of digital and sustainable technologies in machine tools patent applications (WIPO PATENTSCOPE). A smart sustainability transition is currently not hampered by a lack of smart technologies but rather by the sluggish introduction of sustainable machine tools. Cyber-physical and robot machine tools have been found to be central pathways to a smart sustainability transition. Implications for harnessing externalities reach beyond the machine tools industry.

Technology opportunity discovery by structuring user needs based on natural language processing and machine learning

Discovering technology opportunities from the opinion of users can promote successful technological development by satisfying the needs of users. However, although previous approaches using opinion mining only have classified various needs of users into positive or negative categories, they cannot derive the main reasons for their opinion. To solve this problem, this research proposes an approach to exploring technology opportunity by structuring user needs with a concept of opinion trigger of objects and functions of the technology-based products. To discover technology opportunity, first, an opinion trigger is identified from review data using Naïve Base classifier and natural language processing. Second, the opinion triggers and patent keywords that have a similar meaning in context are clustered to discover the needs of the user and need-related technology. Then, the sentimental values of needs are calculated through graph-based semi-supervised learning. Finally, the needs of the user are classified in resolving the problem of vacant technology to discover technology opportunity. Then, an R&D strategy of each opportunity is suggested based on opinion triggers, patent keywords, and their property. Based on the concept of opinion trigger-based methodology, a case study is conducted on automobile—related reviews, extracting the customer needs and presenting important R&D projects such as an extracted need (cargo transportation) and its R&D strategy (resolving contradiction). The proposed approach can analyze the needs of user at a functional level to discover new technology opportunities.