Big Data Analytics in Supply Chain Management: Practical Insights in Aerospace Supplier Collaboration

Abstract

Big Data has become a frequently used term. This expression is used to summarize the large amount of unstructured or semi-structured data, which are produced day by day by companies, their devices, machines, or products in use (Ivanov et al. 2019). Big data has been characterized in literature by 5Vs: volume, variety, velocity, veracity, and value (Fosso Wamba et al. 2015). These characteristics also define the challenges how to handle big data the right way. Analytics of such big data offers a lot of potential for new business models: In the Aerospace industry the concept of big data has been started to be adopted, e.g. based on the approach of “digital twins”, where a physical product is always accompanied by a corresponding digital counterpart object, including lifecycle information. A concrete example to be presented in the workshop is the lifecycle of e.g. material demand, incorporating demand generation, forecast and order collaboration, delivery, goods receipt and finally the invoice. Based on the SCOR model (Bolstorff et al. 2007), also return and repair processes can become relevant.