Open Access

Soo-Yon Kim, Stefanie Berninger, Max Kocher, Martin Perau, Sandra Geisler

• Decision making in modern supply chain management relies heavily on data-driven decision support. Companies show a growing interest in building insights not only on data from within the company’s own boundaries, but also from collaborators and other actors in the market. While the topic of data and information sharing has been the focus of previous works, there has been a lack of studies focusing on practical implementations in the supply chain domain. Our aim is to conduct a technical feasibility study of data sharing in supply chain management. We analyze the requirements for cross-company data sharing in supply chains, and discuss existing technologies that enable such collaboration. We apply a distributed analytics framework that has already been implemented in the healthcare domain to a simulated use case of key performance indicator (KPI) exchange between supply chain actors. We find that the application is able to compute and exchange KPIs from the simulated companies’ datasets without requiring centralization of the databases. Furthermore, we find that the framework supports integration of data quality assessment and privacy preservation mechanisms. The application thus yields promising results with regard to technical feasibility. Factors that may facilitate scalability are discussed as directions for future research.