Remote monitoring powered by IoT and big data enable higher efficiency

Internet of things (IoT) and big data power remote monitoring, enabling a new wave of efficiency. Modernize control processes and phase out outdated control systems that limit access to data and serve as a hurdle for streamlining and optimization. See 3 steps for IoT remote monitoring.

By John Cavalenes October 30, 2014

In manufacturing, there is an increased and continuing need for businesses to drive efficiencies to remain competitive, including through remote monitoring. A major opportunity for efficiency lies in the modernization of control processes, phasing out outdated control systems that limit access to data and thereby serve as a hurdle for streamlining and optimization. By limiting access points to key data, outdated systems prevent control engineers from making real-time decisions that have the potential to improve operational efficiency and cost savings.

Remote monitoring is a common solution to lack of efficiency, allowing control engineers to access machine-to-machine data without limits of physical location. Although remote monitoring is not a new or emerging technology, the proliferation of two major technology trends—IoT and big data—has enabled new heights of remote monitoring capabilities for manufacturing. As more discrete devices become networked and connected, control engineers can more fully harness the monitoring capabilities of processes.

IoT refers to the next step beyond machine-machine communication, the interconnectivity between embedded devices connected to the existing Internet infrastructure. In the world of manufacturing, IoT allows for the collection, transfer, and aggregation of embedded machine data. That large and complex data set is referred to as big data.

With IoT, data collectors and data access have expanded greatly for control engineers. By enabling the collection of complex data sets from a multitude of embedded devices, a manufacturing system becomes more fundamentally interconnected. Devices today have more information useful to engineers, and the level of integration can result in new capability and insight, with less infrastructure. Control engineers can now collect big data and use analytics to identify trends, and predict future outcomes within the manufacturing process, in addition to responding in real time. 

3 steps for IoT remote monitoring

For control engineers undertaking remote monitoring through IoT for the first time, there are three key steps to be taken. 

Step 1: Determine what you want to monitor. 

Although the IoT can enable the collection of massive amounts of data, it’s important to determine which data is most relevant to your business and processes. Too many organizations look for the value of data as they collect it, which wastes resources and leads to an excess of data collected without a clear purpose. First, figure out what you want to monitor, why you want to monitor it, and what purpose it will serve your business. What you want to monitor should reflect the areas where you like to drive the highest level of efficiency within your manufacturing process. 

Step 2: Determine who will use the data. 

Next is determining who within your organization will be able to use data. Once data has been collected and stored, the focus of the process moves to analytics, and actionable insights for improving the efficiency of your manufacturing process. In addition to having employees with the necessary skills to interpret data, it’s important to set clear expectations. What makes sense for your business can vary widely, from a line manager on the floor using data to compare similar days and shifts to search for sources of inefficiency, or a data evangelist looking at the bigger picture of operations.

Step 3: Set up a network architecture. 

One key element commonly overlooked when preparing to collect data from embedded systems is the lack of sufficient network architecture to do so. For data collection to be fruitful for your organization, set up a network architecture that allows you to collect data securely and reliably.

A network architecture has been made easier with the onset of IoT technologies, giving more options to deliver remote monitoring. These technologies have greatly expanded access to data and ability to gather insights. Additionally, connected devices today generate more data than ever before, providing a new level of integration to diagnose further issues. 

Look ahead to larger changes

Although remote monitoring that leverages IoT and big data is valuable for creating efficiencies in the manufacturing process, ensuring buy-in within your organization is paramount before undertaking technology upgrades or data collection. Take a good look at the needs of your enterprise and what makes sense for the organization, and remember that starting with moderate improvements and improving processes through data in an area with just a few processes can be a catalyst to larger change.

One helpful exercise can be to interview employees at different stages of the manufacturing process, whether in maintenance, operations, or technical support, to understand what information would be valuable to them and allow them to do their jobs better.

If you are providing your organization’s personnel with information of value that they can harness to reduce overhead costs and streamline processes, you are guaranteed to see more buy-in from all levels.

– John Cavalenes has the title of plant solutions product management and pre-sales, Schneider Electric; edited by Mark T. Hoske, content manager, CFE Media, Control Engineering,

Key concepts

  • Remote monitoring powered by Internet of things (IoT) and big data enable higher efficiency.
  • IoT big data power remote monitoring, enabling a new wave of efficiency.
  • Modernize control processes and phase out outdated control systems that limit access to data and serve as a hurdle for streamlining and optimization.

Consider this

How can Internet of things and big data empower remote monitoring for greater efficiencies?

ONLINE extra

This online version of a November issue article links below to more information. 

See part 1 of this story below.