Resilient Digital Twins
Wil M. P. van der Aalst, Oliver Hinz, Christof Weinhardt
Abstract
Currently, we can witness contradictory expectations when it comes to IT. On the one hand, there is the belief that Artificial Intelligence (AI) and Machine Learning (ML) will solve most problems because of the abundance of data and sophisticated algorithms In this editorial, we use AI/ML to refer to machine intelligence, i.e., mixtures of Artificial Intelligence and Machine Learning. AI/ML can deal amazingly well with unstructured data (text, images, and video) as long as there are enough training data. On the other hand, the COVID-19 pandemic and rapid climate changes (e.g., the floods in Germany in July 2021) show that AI/ML cannot deal with disruptions. When there is a sudden dramatic change, predictive models will fail, no matter how much data was there before. Consider, for example, the impact of the COVID-19 pandemic on supply chains. Especially at the beginning of the global outbreak of COVID-19 in March 2020, supply chains failed because of the unpredicted demand for certain products (e.g., masks and toilet paper) combined with simultaneous restrictions for travel, work, and business.