Smart Manufacturing

The key business objectives achieved with the Smart industry are increased productivity, enablement of agile and flexible manufacturing to produce highly specialized and personalized products at scale, and efficiency.

These objectives are achieved by making manufacturing operations more predictive using data from processes and equipment, connecting business to manufacturing operations, and making manufacturing equipment run autonomously in response to supply chain requirements.  

From an automation perspective, Smart Industry is enabled by three key technologies: 

  • Industrial Internet of Things (IIOT) 
  • Artificial Intelligence (AI) 
  • Digital Twins 

IIoT 

Contrary to common misconception, IIoT is not just about sensors—new or existing. The Industrial Internet is defined as the “Internet of things, machines, computers, and people, enabling intelligent industrial operations using advanced data analytics for transformational business outcomes” (IIC The Industrial Internet of Things Volume G8: Vocabulary IIC:PUB: G8:V2.1:PB:20180822)

An Industrial Internet of Things (IIOT) system is defined as a “system that connects and integrates industrial control systems with enterprise systems, business processes, and analytics” (IIC The Industrial Internet of Things Volume G8: Vocabulary IIC:PUB: G8:V2.1:PB:20180822).

The term “analytics” in this context includes online predictive analytics built using Machine Learning algorithms. 

The illuminatorTM part of the Quartic PlatformTM is an IIoT System. 

The automation achieved in the last few decades with sensors, actuators and automation systems like PLC’s, DCS, SCADA, MES, motion control systems, motor controllers, and drives (often referred to collectively as OT systems) has continued to increase the amount of manufacturing data available. New, inexpensive, and wireless sensors being added will only increase this data. 

Data from OT systems, CMMS systems, and business systems built on hierarchical architectures such as ISA95, reside in silos of data, making it difficult to deploy predictive analytics, AI, and machine learning needed for smart manufacturing. 

“An important element in the industrial internet is the application of analytics on the data gathered from the industrial assets and control systems to gain insights on their operations.

To enable analytics on these asset data, many of the system’s functional components require a concerted effort in data management. Therefore, data management is also considered a crosscutting function” (IIC IIRA Industrial Internet Reference Architecture).

In an IIoT architecture, connectivity is therefore defined as a “crosscutting function”. Transforming existing data architectures into this highly agile, contextual data architecture is needed to build a smart industry infrastructure. 

The illuminatorTM data pipeline is an example of such a modern data bus for IIoT architecture. 

Intelligence created from this data with AI-powered intelligence engines like eXponenceTM can make all aspects of manufacturing operations predictive, agile, and flexible, leading to a new state of automation that eventually makes autonomous operations possible.  

Typical application areas for process manufacturing that impact business with smart manufacturing include:

Smart manufacturing is enabled by AI-powered smart assets.

To build a smart manufacturing operation, users can start by making their legacy assets smart. 

Author

  • Rajiv Anand

    Rajiv is the CEO & Co-founder of Quartic.ai. Rajiv brings 30 years of industrial automation experience implementing process control and asset health solutions using Emerson Process Automation platforms for power, mining, pharmaceutical and chemical industries. Rajiv held key engineering, management and leadership positions with Emerson and their impact partners. Prior to starting Quartic.ai, Rajiv spent a year researching Industrial AI and Machine Learning and advising technology companies and customers on digital manufacturing strategies.

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