  We live in a world of exploding complexity driven by technical evolution as
well as highly volatile socio-economic environments. Managing complexity is a
key issue in everyday decision making such as providing safe, sustainable, and
efficient industrial control solutions as well as solving today's global grand
challenges such as the climate change. However, the level of complexity has
well reached our cognitive capability to take informed decisions. Digital
Twins, tightly integrating the real and the digital world, are a key enabler to
support decision making for complex systems. They allow informing operational
as well as strategic decisions upfront through accepted virtual predictions and
optimizations of their real-world counter parts. Here we focus on real-time
Digital Twins for online prediction and optimization of highly dynamic
industrial assets and processes. They offer significant opportunities in the
context of the industrial Internet of Things for novel and more effective
control and optimization concepts. Thereby, they meet the Internet of Things
needs for novel technologies to overcome today's limitations in terms of data
availability in industrial contexts. Integrating today's seemingly
complementary technologies of model-based and data-based, as well as edge-based
and cloud-based approaches has the potential to re-imagine industrial process
performance optimization solutions.
