Data Analytics Meets Manufacturing

As stated in the National Institute of Standards and Technology (NIST) at the U.S. Department of Commerce, big data encompasses extensive datasets for analysis, storage, and manipulation. The rate of data aggregation continues to grow at seemingly lightning speed. Such an explosion creates opportunities to formulate new information that’s essential to overcoming challenges and sparking the realization of yet untapped value. Advances in analytical technologies allow manufacturers to process copious amounts of data that can result in improved efficiency and throughput. Big data thereby includes the processes for identifying relevant and actionable information within the high volumes of data sets.

With software advancements and decreases in the cost of sensor technologies, manufacturers have continued to benefit from collecting more and more data. As a result, big data analytics solutions have supported meaningful gains at production facilities across the process industries.

What Happens When Data Analytics Meets Manufacturing?

 Ways in which data analytics empower manufacturers to improve their competitiveness include the following:

  • Improving operational efficiency: Analytics allow manufacturing companies to make knowledgeable and timely assessments based on the most recent and relevant information. For decades analytics solutions have been employed in the power and other segments of the process industries to avoid the high cost of unplanned downtime. Elsewhere such solutions have been used to boost the performance of processes and optimize the transformation of raw production inputs into marketable outputs. Tying disparate data together equips manufacturers with more comprehensive insight to make effective decisions.  Even modest improvements in production control systems, as an example, have been shown to boost production capacity, enhance quality, and reduce energy consumption.


  • Saving on costs: Data mining can lower a company’s expenses by homing in on vital information quicker than by performing similar analysis manually. For instance, algorithms – whether basic or advanced – can be used in a continuous manner to proactively identify patterns in a process that would otherwise be invisible to the human eye. Whether fluctuations in noise, error, or other process data characteristics, it is nearly impossible for production staff to recognize subtle changes that can have a dramatic impact in performance. From the standpoint of economic savings, analytics can be used to assess asset performance and thereby avoid mechanical issues that hamper production and that ultimately lead to catastrophic failure. Analytics can also be used to speed up a process and thereby increase throughput. Insights from analytics solutions can help process engineers to understand where to focus their efforts for smoother overall performance, and to assure that costs are effectively contained.


  • Identifying process risks:  Process manufacturing plants can be significantly slowed when needed equipment fails. Thus, applying data analytics to oversee key production assets is of critical importance. Analytical tools have been applied successfully in the process industries to monitor the health of large rotating equipment. While the number of such assets is limited, data analytics have also been used to assess the performance characteristics of more abundant assets such as pumps, motors, and even valves and dampers. Timely awareness of changes in the health of these assets allows staff to adjust production and avoid the risk of unplanned downtime. Data analytics provides the benefit of reducing risk through its ability to both consume and assess enormous amounts of data and to provide advance warning of declining health.

What does this mean?

As data analytics is applied increasingly across the process manufacturing landscape, operations can be better understood and production efficiency and throughput can be improved. Analytical tools are enabling production staff to uncover new insights that were previously unavailable and to make meaningful improvements to plant performance

To learn more about data analytics, contact the experts at Control Station or investigate how PlantESP is being utilized today to improve plant performance.

These resources offer related content:


Using the Oscillation Metric to Uncover Tuning Issues

  The Oscillation Metric Can Identify a PID Controller that Requires Tuning, But More Often Than Not it Provides an Indication of Loop Interaction or Mechanical Issues. There’s a wealth of information available in most every data historian. The data can be used to evaluate...

What is Industry 4.0?

Those in the manufacturing industry have seen a rash of new terminology introduced over the last several years. Among those terms: The Industrial Internet of Things (IIoT) and Industry 4.0. While these terms may stimulate thoughts of progress and innovation, their true definition remains uncertain...

What is the Process Time Constant? How Do I Calculate the Time Constant of A Process?

Timing Is Everything, Especially When It Comes to Control Loop Tuning and the Process Time Constant   Party hosts often cringe when guests arrive early or late. Seemingly subtle shifts in timing can throw off planned details that are of importance to the host and...

Still looking for more?

Now that you’ve gotten the basics, connect with our team to learn how our people, processes and technologies can help you optimize.