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Moving from reactive to predictive maintenance requires the right tools for use by subject matter experts.

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.

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.

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.

The production of greenhouse gases, inefficient water and energy usage, and significant harmful emissions have earned the manufacturing industry a less-than-sterling reputation for its impact on the environment.

The pharmaceutical industry is in the midst of an evolution, spurred by significant reductions in time-to-market requirements, most notably as demonstrated with COVID-19 vaccines.

Digital transformation is enabled by technology, but many end users don’t see these initiatives as proprietary intellectual property, but as information to be shared among peers. This openness helps advance efforts throughout the process industries by creating a new feedback channel for organizations as they continue their digital transformation journeys.

Scaling complex analytics across production assets improves production and operations metrics.

A common misconception in large-scale process manufacturing is that minimizing downtime is the key to increasing profitability. This mindset fails to address the fact that some downtime can improve capacity, increasing production and revenue.