Success stories show how advanced analytics software is used to create insights and improve process efficiency. See three use case examples.
Hybrid data architectures empower process manufacturers to more quickly realize the business benefits from their cloud and IIoT investments.
In a difficult hiring environment, investing in staff and fostering employee-focused cultures are essential.
Moving from reactive to predictive maintenance requires the right tools for use by subject matter experts.
You’ve escaped pilot purgatory, cleared organizational obstacles, and are now in position to implement an advanced analytics system that will significantly improve manufacturing performance.
Advanced industrial data analytics has a prominent role to pay in process control and automation. The success of process control and automation efforts depends on the skilled design and automation of process behavior understanding. Advanced analytics applications enable the integration of the process understanding with process - and equipment - related relationships, which can be gleaned from historical process data by subject matter experts (SMEs) using advanced analytics.
In the age of the industrial internet of things (IIoT) and Industry 4.0, the sheer amount and complexity of data has greatly increased. Add the emergence of artificial intelligence (AI) and machine learning (ML), and the process industries have the potential to uncover more meaningful insights than ever before.
Batch chemical processes present unique data aggregation, visualization and analytics challenges that may exceed the capabilities of traditional engineering toolsets. For a start, a chronological time stamp of data won’t suffice.
According to analysis firm Gartner, within the next five years, two manufacturing technologies will achieve a "plateau of productivity", or the stage where they drive transformational impact on business outcomes: the internet of things for manufacturing operations, and cloud computing in manufacturing operations.
Data analytics, and specifically predictive analytics, are meant to reduce the number of alarms for process improvements, trend forecasting, and predictive maintenance. However, deploying predictive analytics often leads to excessive nuisance alarms, a common problem in process manufacturing control rooms.