What Does Industry 4.0 Mean for Your Control Systems?

Posted: November 7, 2018

Monitor and Control

What Does Industry 4.0 Mean for Your Control Systems?

The fourth industrial revolution is filling today’s shop floor with economically available smart devices that provide plant-level engineers critical metrics directly from assets. Imagine a device that sends data from pumps in a process manufacturing plant to display metrics with warnings when (or even before) parameters exceed critical limits. Plant level engineers love such devices that make life easier for them; what they don’t like is sharing access to those devices and the data they generate. The message is clear — storing or sending control data outside the SCADA and MES makes plant engineers uncomfortable; it's like they have a privacy fence around their control systems data.

The amount of unplanned downtime because of a PLC program executed incorrectly or a glitch in the control system supports this guarded mindset of control engineers. However, recent research reports on smart connected operations illustrate secure ways to use control data in a truly smart connected operations environment. Early adopters leveraged today’s abundance of instantly-available data, networking capabilities, and processing power to streamline operations using smart connected devices.  LNS Research recommends that manufacturing companies educate themselves on how Industry 4.0 technologies can be a significant game-changer for their control systems.

Untapped Plant-Level Data: Road to New Analytics-Centric Architecture

The traditional enterprise architecture that organizations rely on was designed for two main purposes—control production and manage assets. There has always been a firm line between enterprise data and operational data.  Companies have used the former strictly for corporate planning, while relying on the latter to control daily production.  Ultimately, there has always been a lot of untapped data that manufacturers either aren’t collecting, or they’re not using for any purpose.

Recent innovations around Industrial Internet of Things (IIoT) platforms provide ways to collect, store, and analyze all the untapped data.  This has led to new business cases, which in turn drive the need for a new architecture.  Research on this topic suggests that Big Data and analytics will give rise to a separate analytics-centric architecture that exists parallel to existing frameworks. The combined parallel architectures provide plant engineers with a more flexible yet more robust Operational Architecture to run analytics at any level throughout the organization.

A Little Trust Goes A Long Way for Prescriptive Analytics

Security is one of the biggest concerns for manufacturers when considering IIoT-enabled plant devices. Software applications built on a holistic IIoT platform have provided enough evidence to show that industrial cyber security is at the top of (nearly) every software vendor’s agenda.  Vendors have engineered secure gateways to share data and enable collaboration while addressing industrial cyber security issues.

Manufacturers are learning quickly that collecting and sharing this untapped shop floor data outside the plant is secure and allows much broader and sophisticated analytics.  IIoT applications can leverage both enterprise and plant data across a holistic platform to perform predictive, and in some cases, prescriptive analytics to address failures, suggest practical changes, and convey outcomes related to recommended changes.

Edge and Cloud Go Hand in Hand

There have been a lot of research reports written on edge devices and advantages of performing real-time analytics all the way down on the assets. But that doesn’t mean edge devices are going to replace cloud or on-premise computing. In many cases it makes more sense to send data to a centralized repository on a data center or in the Cloud.

 Performance-Dashboard-LNS-blogFor instance, it's painfully expensive and of little benefit for an edge device to perform complex analytics.  They can perform simple computations with current hardware and systems quickly—to provide local results instantly when needed. However, more sophisticated calculations have to be sent to a Cloud repository or a data center with almost unlimited computation power. Using a combination of Edge and Cloud computing enables more complex calculations and allows long-term storage of control data that used to be purged (or archived, never to be seen again) before today’s industrial revolution.

Bottom line

Today’s advanced IIoT technologies promise disruption throughout manufacturing; they enable better data collection and sophisticated analytics. Structured hierarchies will give way to a secure, decentralized architecture that leverages the abundance of data. That doesn't mean companies need to replace the entire control system. Key takeaways for manufacturers regarding industrial transformation of their control systems:

  • Industrial Transformation of control systems doesn’t mean edge devices will take over your PLCs or SCADA. Edge is great for simple real-time analytics, but can’t handle unstructured data.
  • Don’t expect or plan to “rip and replace” legacy systems. Every plant has “monument” systems that, if replaced, would cause more harm than good.
  • The best approach to an Industrial Transformation pilot or program is to add complementary software and applications to existing control systems.

Learn how industrial companies handle and present data at the plant level in the spotlight report, “Converting Data to Decisions: How Industrials Unleash Data with User Experience.

To learn how AVEVA can support your Industrial Transformation, visit : https://sw.aveva.com/digital-transformation

author
Written By
Vivek Murugesan

Vivek Murugesan is a Research Associate with LNS Research; where he conducts market data analysis and creates data models that span the breadth of LNS coverage areas including Digital Transformation and the Industrial Internet of Things (IIoT), along with Manufacturing Operations Management, Asset Performance Management, Quality Management, and Environment Health and Safety. Vivek holds a Masters of Science degree in industrial engineering from Northeastern University, and earned a Bachelor of Engineering (BE) in mechanical engineering from Anna University in Chennai, India. Prior to LNS, Vivek worked with ARRIS where he focused on supply chain analysis, and co-founded Namma Café, a social and collaborative learning environment for young professionals to develop their skills in the areas of creating and building data models, interactive dashboards, and business intelligence reports. LNS Research provides advisory and benchmarking services to help Line-of-Business and IT executives make critical decisions. Our research focuses on the Industrial Internet of Things (IIoT), Digital Transformation; and providing insights into the metrics, leadership, business processes, and technology capabilities needed for achieving Operational Excellence.

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