Digitally enabled supply chain integration through business and process analytics

Supply chain integration (SCI) is the degree to which a manufacturer strategically collaborates with its supply chain partners and collaboratively manages intra- and inter-organizational processes to gain superior operational performance. Grounded on system theory, contingency theory, and knowledge based view this paper identifies requirements for successful SCI. In alignment with the findings of a qualitative case study by expert interviews and participant observations the study demonstrates that a lack of organizational compatibility, a lack of supply chain planning, and a lack of information sharing are the main barriers for successful SCI, which can be compensated by the positive aspects of an increase of organizational IT capability focused on intelligent systems, which play a key role for successful SCI. By an additional quantitative empirical study, we show how these systems, more specifically artificial intelligence (AI) empowered process mining (PM), could compensate for the identified deficiencies. Subsequently we implement a graph convolutional network (GCN) in order to predict the next process activity and its corresponding timestamp in the product development process supporting SCI. Based on the qualitative and quantitative results we discuss implications both for theory and practice.