SCADA has long been a staple of industrial automation. These systems grant operators insight into industrial processes, equipment, alarms and even operational conditions. However, the modern industrial environment is a sea of data, and traditional monitoring alone will hardly be sufficient to use this information to its fullest. The intriguing question is not if SCADA will continue to be a relevant part of operations but rather how SCADA data itself can be turned into smarter data.
SCADA Built to Monitor
A typical SCADA system captures data from industrial machines, presents this data to an operator through a series of screens and displays, and allows the operator to monitor and control the process. It helps an operator understand if there are anomalies, for instance, or if pressure levels are not within specified ranges. Still, a SCADA system simply reports raw data that requires some degree of human interpretation.
An operator or engineer will monitor the system to look for relationships between different datasets, patterns and anomalies.
They also need to analyze the data to determine if the anomaly requires a human response. As the industrial world continues to embrace connectivity, human interpretation becomes an increasing challenge.
IoT Is Generating More Data
Industrial Internet of Things (IIoT)is bringing increasing numbers of sensors and machines online. From the temperature and pressure of machines to their overall status or energy consumption, machines produce an enormous amount of data. This data represents exciting new possibilities but also raises a new issue: how do you turn all that raw information into something useful?
Collecting more data points does not automatically lead to smarter decisions.
More data requires context and interpretation.
AI Lends Intelligence to the Mix
AI becomes a natural fit in modern industrial environments and can serve as a complement to SCADA, as well as IIoT technology. Instead of merely viewing a single data point, AI can analyze relationships across multiple streams of data to detect patterns that are not immediately evident to human observers. A fluctuation in machine performance, for example, could be analyzed in combination with production data and historic operating conditions. The goal is not to fully automate all operational decisions but rather to assist human beings in better understanding extremely complex operational environments.
From Reactive to Predictive Measures
Monitoring of many traditional industrial facilities has been reactive. An alarm is generated when an operational threshold has been breached, at which point a machine is shut down, a part is inspected, and a corrective action is then performed. Predictive analytics offers another approach entirely.
By applying analytics to historical and current data, systems powered by AI can identify patterns indicating that a problem is developing and give operators the chance to intervene before a full shutdown occurs.
While this technology does not remove the necessity of talented engineers and operators, it can give them more information to make smarter, more proactive operational and maintenance planning.
Facing Data Fragmentation
One of the greatest hindrances to achieving truly smart industrial operations is data fragmentation. Modern factories typically have: SCADA systems to monitor operational processes and machinery. MES platforms to manage manufacturing processes and track progress. ERP systems to manage business activities and inventory. IoT devices that collect data from sensors.
Workforce systems that track and manage operator activities. A factory that produces millions of data points per day may be collecting SCADA data and manufacturing data, IoT device data and data about its workforce, but this fragmented data is of little use unless it can be brought together.
An anomaly occurring with one piece of machinery might make far more sense when viewed alongside production scheduling, work orders or inventory information. For this reason, Integration is rapidly becoming just as important as data analytics in making smart decisions.
Intelligence for the Shop Floor in Real Time
An industrial plant rarely has the luxury of waiting. Bottlenecks appear and disappear within minutes. Machines can fail causing production interruptions.
Supply shortages may prevent workflow.
Real-time analytics can detect these changes as they are occurring and present the information to the user. Edge computing can be also employed here. Edge devices offer processing near the source of the data – at the machine, for example – giving the system the ability to make faster decisions and gain insights more rapidly. The combination of IIoT, AI and edge computing enables a faster approach to understanding any operational environment.
The AIoT Connects Intelligence with Operations
AIoT-an abbreviation for the convergence of AI and IoT-unites connectivity from devices, AI for insights and, in industrial applications, the physical environment of the enterprise and real-time processing from edge devices. This brings together the individual capabilities to deliver an additional layer of intelligence. It is not necessarily a matter of replacing SCADA, MES or ERP– the focus is more on connecting and gaining greater intelligence from the data that these systems already produce.
CompentraAI's Value Proposition
The merging of industrial systems with IoT technologies and AI gives rise to new possibilities and applications for connected manufacturing. CompentraAI, for example, offers an AIoT-based platform that focuses on workforce intelligence, visibility into the work-in-progress, inventory management and visibility, traceability and real-time shop-floor intelligence. More information about the company platform can be found athttps://compentraai.com/. The crucial takeaway here is that becoming an intelligent enterprise does not require scrapping systems that are already in place; it starts with finding smarter, more valuable ways to leverage the existing information.
Beyond SCADA
The future of industry isn't a question of choosing between SCADA and AI, but rather of realizing how the two technologies can enhance each other to create more connected and intelligent manufacturing environments. The traditional operational workflow: Sensors-->SCADA-->Dashboard-->Manual interpretation. The emerging operational workflow: Sensors-->SCADA/IIoT-->Edge & AI-->Analysis & Insight-->Human Decision.
While SCADA provides crucial visibility, IIoT offers connectivity.AI can perform analysis and provide crucial, predictive insights.
And as always, humans play an essential role in applying context, experience, and judgment to make the ultimate decisions. The combination represents the next step forward for smart manufacturing.
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