An intelligent manufacturing framework integrates robotics, IoT monitoring, and data analytics to optimize automotive production. The research ...

Data quality issues emerge from multiple failure points from development practices to production life cycle, each compounding ...

Design for manufacturing (DfM) is evolving from traditional engineering practices into a data-intensive discipline that requires real-time integration of manufacturing capabilities, supplier ...

To establish a consistent approach to assess, manage and improve data quality across the data lifecycle, covering a wide spectrum of data types, and taking into account the blurred line between data ...

Why viewing MES not just as a monitoring tool but a data contextualizer is critical to digital transformation, as it provides meaning to disparate machine and sensor data. How integrating control and ...

While companies may share common ground when it comes to their data quality problems, data quality tools and strategies are not one-size-fits-all solutions to the problem. Each company should approach ...

Raw data only becomes useful for fine-tuning a drug manufacturing process through effective, appropriate analysis, says the team behind a new “information-centric” analytics framework. The FoReSight ...

An article recently published in Nature proposes a new way to evaluate data quality for artificial intelligence used in healthcare. Several documentation efforts and frameworks already exist to ...