Recent developments such as the Internet-of-Things generate data and foster the exchange of it among nodes in various fields such as the supply chain management. This allows firms to improve their supply chain performance by analyzing the data. However, many firms struggle to store the data, analyze the data, and interpret the data. Therefore, we propose a concept that integrates existing IS solutions helping firms to identify and realize a higher (supply chain) performance. The concept covers supply chain analytics, big data (storage), and IT architecture. More importantly, the concept can be realized with a low-budget solution allowing even small-medium sized enterprises to apply it and realize performance gains. We contribute to theory by proposing an integrated big data and analytics concept, and a supply chain analytics solution. Future research can use our concept, e.g. to develop low budget supply chain analytic scenarios or compare performance gains between our solution and cost-intensive solutions.
Engel, Tobias; Meyer, Dany; and Vogt, Joerg-Oliver, "An integrated concept for supply chain analytics in Small-medium sized enterprises" (2018). MWAIS 2018 Proceedings. 13.