LAUSR

Abstract In the context of Industry 4.0 and the Internet of Things (IoT), machine-tools producers are adopting servitization as a way to develop and sell new specialized data-oriented services already imbedded in their machines, providing analytics. However, large companies producing automation services and products offer generic turnkey IoT-based solutions, which can be implemented in any type of machinery to extract data and provide analytics. These generic solutions can be an important threat to the specialized ones, mainly those developed by SME manufacturers. In this context, this paper aims to investigate to what extent specialized solutions can be compared to generic solutions regarding perceived value. We performed an exploratory study employing a case-based method combined with TOPSIS, in which known generic solutions are compared to one specialized solution provided by a machine-tools producer SME. From the academic point of view, our results provide a better understanding of the requirements of an IoT-based analytics solution in the field. In addition, we propose an approach that can be used by other researchers for value-added analysis of companies embarking on a servitization process. Practical contributions include a method allowing manufacturer to make an informed decision about the solution to be selected for their factories. Moreover, machine-tool manufacturers and service providers will be able to better identify and evaluate their strengths and weaknesses. This project was performed in the context of a living lab for Industry 4.0 and its results are being employed to create a learning factory for research, development, education and training purposes.