This paper aims to propose a new framework for the extension to the stage for establishing a technology roadmap. Previous research mainly relied on the expert interview method, and there was also a patent analysis study based on topic modeling, but only to grasp technology trends. The purpose of this study is to identify opportunities and areas for technology development through patent data in establishing technology strategies. In addition, to enable the implementation of the technology strategy, it is necessary to detect the change in technology and search for the technology that is expected to have a practical development effect. A technology strategy is essential for digitization, and identifying opportunities and threats of technology development through technology trend exploration is important for technology strategy. For players in the logistics industry, digitization has become an unavoidable situation to achieve survival and sustainable competitiveness. The rapid advancement of digital technologies has fundamentally changed the competitive dynamics of the logistics industry. In a concluding step, the application of the developed concept is validated based on a selected use case.
Subsequently, a morphology for the systematic development of ML-based BMs is generated using the insights gained. At first, characteristics as well as general and specific requirements for ML-based BMs in manufacturing are elaborated. Therefore, this paper aims to develop a framework for the holistic generation of ML-based BMs in manufacturing. This can be ascribed to a lack of knowledge about the necessary elements for ML applications’ sustainable implementation and operation. Despite the emerging data-related possibilities, especially small and medium sized enterprises (SMEs) struggle with identifying reasonable use cases for ML in their own company. Through the increasing availability of data in the context of digitalization as well as continuously more powerful and cost-effective possibilities for data processing, the amount of economically viable scenarios for implementing ML-based business models (BMs) in production rises. Analyzing data with the help of Machine Learning (ML) promises to raise significant potentials in all relevant target dimensions and different application fields of industrial production.
0 kommentar(er)
