Process models for the development of data-driven industrial services: insights from a systematic literature review and research agenda

Abstract This paper investigates existing process models for the development of Data-Driven Industrial Services (DDIS). These process models are increasingly important as the role of data in industrial asset management grows. This paper conducts a Systematic Literature Review (SLR) across six databases to address three research questions: Firstly, which existing process models are available for DDIS development? Secondly, do existing process models for DDIS development provide methods and roles for the support of their process models? Lastly, what further research can be derived from the current literature on process models for DDIS development? The findings reveal 17 process models, with eight of them based on the well-established CRoss Industry Standard Process for Data Mining (CRISP-DM). There is variability in how the process models are supported by methods and roles. In addition, the paper establishes a research agenda that addresses research gaps. This paper contributes to the broader understanding of service digitalization in the form of an overview of existing process models for DDIS development and a research agenda that leads towards enabling industrial companies to choose and apply the appropriate process models for DDIS.