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The Industrial Internet of Things: Are you prepared for the revolution?

Think back 18 years ago. That’s when the movie “Smart House” was released. The idea that a house have self-locking doors and talking appliances seemed too good to ever be true. Today, technology has come so far that “smart house” is a common term used in the home building industry and Samsung just announced their “talking fridge.” What many people don’t realize is all the sensors, equipment, devices and software are interconnected, working together to monitor and study data like never before. A concept that seems like sci-fi not that long ago has created the Internet of Things (IoT) that we’re readily using – and growing – today.

When applied to the manufacturing industry, the IoT is referred to as the Industrial Internet of Things (IIoT). These types of deployments are largely focused on the integration of closed environment systems within a plant or corporate infrastructure. Traditionally, factory data was not readily visible or accessible to those making key business decisions. IIoT systems can provide valuable insights that help drive faster, real-time business decisions for process manufacturers. It has been estimated that manufacturing companies worldwide will spend $500 billion a year on IIoT Technology. Further, the value generated by IIoT will reach $15 trillion per year by 2030.

A New Paradigm
As companies begin to invest in IIoT deployments, the old paradigms of process manufacturing will be challenged. IIoT will flatten plant infrastructure as represented in the ISA 95 model. As plant data grows in volume and requires greater context and visualization, the Manufacturing Execution System (MES) layer will be more important, becoming the middleware responsible for connecting the plant floor to more business-oriented Enterprise Resource Planning (ERP) platforms.

The focus then turns away from merely improving the efficiency of process manufacturing and its associated control loops to deploying devices and systems capable of collecting, analyzing and trending real-time data. Middleware protocols such as OPC-UA gain a larger role in this new manufacturing environment establishing a common platform for accessing production values from Programmable Logic Controllers (PLCs).

What Are the Major Components?
International Society of Automation (ISA) describes the following as the major components of IIOT:

  • Intelligent Assets – At the core of an IIOT deployment, this is data available from PLCs, sensors, and other field devices. Although intelligent sensors have been more than capable of collecting copious amounts of data for decades, industrial platforms are just now catching up with intelligent ways to harness, analyze and display this data in a useful manner.
  • Data Infrastructure – Ethernet-based process supervisory and control systems have all but replaced most of the proprietary networks of yesteryear. Plus, wireless infrastructure technology has reached maturity, if not essentially ubiquitous deployment. Virtualization and cloud storage are also being embraced to help hold the massive amounts of data that’s generated and collected.
  • Analytics – If intelligent assets represent the eyes and ears of the IIoT system, process analytics represents the brain power that turns the data into actionable knowledge. Context is king — without it,  data and analytics are a jumble of puzzle pieces with no real effectiveness. Systems must process data and provide context for human use.
  • People – This is a key component that is sometimes overlooked. Automation and algorithms can only take us so far. In the end, smart, talented people are needed to decipher the impact on the  business and the manufacturing processes. Data must be visualized and parsed properly so that it can be easily understood and digested.

What Are the Benefits?

  • Predictive maintenance – Today, most plants function in a reactionary manner, as they have for the last 100 years. When equipment failures happen, plant personnel respond and fix it. Implementing a distributed sensor such as vibration, temperature and stress in manufacturing equipment and then pairing that sensor with a data analytic system can help predict failure before it happens, decreasing downtime. Integrating into to the ERP layer can help plants stage predictive spare parts to increase the time it takes to get key equipment back in operation.
  • Data visibility and cross-functional analysis – Sharing data with stakeholders from different departments helps ensure big data analytics are turned into actionable information. By presenting actionable information to the right person in an easily interpreted manner, such as graphs, gauges or charts, overall buy-in and improvements are realized. This gives engineers and business analysts alike real-time tools for troubleshooting and continuous improvement. They can take a deep dive into their processes to identify previously unseen patterns and insights, in the process eliminating variability and process flaws that may lead to defects or other quality issues.
  • Automated control – The IIoT connects sensors to analytics and other systems to automatically improve performance, safety, reliability, and energy efficiency. This information delivers valuable information to  personnel who can take corrective action. It can also power predictive process control functions to reduce errors and produce more consistent quality.

What Are the Key Challenges?

  • Deploying IIoT in an industrial process manufacturing environment requires forethought. Companies must answer key questions about what information should be collected, how should it be stored, what  is best analyzed, etc. The end goal must be properly defined.
  • Manufacturers must also clearly articulate the key operational requirements and the capabilities of deployed technology. Leaders must possess a deep comprehension of their current process systems and equipment.
  • Data integrity and security are of major concerns for companies that must protect their intellectual property in the new industrial environment. Ethernet based process control networks that have ties to business systems must perform detailed risk and threat assessments prior to deployment. Intelligent devices that have individual IP addresses and embedded servers must also be protected from intrusion while still leveraging their data collection capabilities. Once data is collected, it must be securely stored and protected locally or in the cloud.
  • Industries such as pharma, biotech, and food/beverage have an added burden of adhering to regulatory standards such as CFR Part 11. This prescribes the necessity of electronic signatures and the “Validation of systems to ensure accuracy, reliability, consistent intended performance, and the ability to discern invalid or altered records.” This adds additional time, cost and resources to an IIOT deployment.

IIoT is an inevitable step in the evolution of process manufacturing. It is up to individual manufacturers to determine their own ROI for implementation. More real-time data can be leveraged to increase plant and process productivity, but this must be balanced with the increased risk to data integrity and security. The need for qualified automation engineers, IT system analysts and integrators will only increase as industry leaders grapple to deploy various solutions and platforms. Those who do not take IIOT systems seriously now will be left behind by the competition.