Big Data, Artificial Intelligence, Machine Learning and Advanced Analytics are all the hype in popular media, with companies like Facebook, Google and Uber promising unprecedented breakthroughs in image recognition, natural language processing and self-service citizen analytics. But is this also the reality in Supply Chain Management and Logistics? In our presentation, we will contrast the Gartner hype cycle of emerging technologies with the current business reality in Supply Chain Management, using evidence taken from an industry survey of 200+ companies. Our comprehensive survey in Supply Chain Forecasting shows that the corporate reality still looks rather different: most companies employ human judgment paired with simple statistical algorithms from the 1960s, falling far short of adapting advanced statistics or even novelties as intelligent algorithms. This leaves a substantial gap of 50+ years between the state-of-the-art research and actual practices, in which algorithm capabilities and data have increased significantly. While the magnitude of the gap is frightening, it also indicates the potential for early adopters of Big Data and Machine Learning in Forecasting. In our talk, we will give examples how selected industry though-leaders have successfully implemented Big Data and advanced algorithms from artificial intelligence, machine learning and data science for forecasting in Supply Chain Management and Logistics, including using advanced time-series methods for FMCG Manufacturer Beiersdorf, and using Big Data Signal Repositories of causal information of temperature, prices changes and bank holidays for beer Manufacturer Anheuser-Busch InBev, Container Shipping line Hapag-Lloyd, and airfreight carrier Virgin Cargo, all successfully narrowing the gap between hype cycle and reality.
