It’s been nearly a decade since Industry 4.0 was first discussed and most manufacturers now understand its potential to transform how we work. Most manufacturers across the world have launched pilots to validate the value of harnessing data and advanced analytics, applying autonomous solutions like predictive maintenance to optimize their operations.
However, as manufacturers strive to adopt autonomous operations faster, they face two common challenges: the highest value use cases typically require manufacturers to introduce autonomous solutions to more than one asset, line or function; and autonomous solutions leverage vast amounts of data, which is typically beyond the reach of existing manufacturing operations management (MOM) architectures.
Some manufacturers are investing in data lakes as a method for storing all manufacturing data in a single repository. But this falls short of providing the structured information needed to optimize end-to-end operations. In response, more and more CIOs and manufacturing managers are exploring how digital twins can act as a one-stop-shop for autonomous solutions by gathering data from multiple sources, unifying it and contextualizing it.