Real-Time Digital Production: Implemented

Summary

Manufacturing worldwide has received a wake-up call with the pandemic and supply chain disruptions. Outsourcing for lower costs has created pain and is a pressure point that has negative impacts on both sales and increased profitability risk. However, the fundamentals of manufacturing and production are being reshaped by integrating manufacturing production into the entire industrial business system. A digital manufacturing architecture offers a streamlined approach to enterprise-wide clarity that allows stakeholders to adjust operations based on real-time insights, i.e. data transparency.

Industrial digitalization worldwide

The impact of open manufacturing initiatives continues to advance worldwide as countries and industries recognize the need to modernize with Industry 4.0 and other related initiatives are being adopted and accelerated. These provide models for all industrial manufacturing organizations to achieve holistic and adaptable open automation system architectures. Germany’s Industry 4.0 initiative has sparked global collaborative efforts to apply the technology to increase manufacturing competitiveness in other countries, including China, Japan, Mexico, India, Italy, Portugal, and Indonesia.

At the same time, the failure of companies, governments, and schools to invest in vocational and technical education is a major problem. In the decades after World War II, high school dropouts could walk onto factory floors across America and find decent, secure, middle-class jobs; that is no longer the case. For years, companies have not invested in meaningful internships and apprenticeships, further drying up the pipeline of skilled labor. Of course, unskilled labor continues to be eliminated by automation, but industry still needs skilled, knowledgeable people trained to work with new manufacturing technologies.

These trends are supported by the rise of real-time manufacturing business systems. Digital transformation is creating an integrated real-time system from sensor to enterprise to cloud, and this is now possible through the implementation of open standards and technology. Manufacturing and production companies are increasingly digitizing to overcome the inefficiencies of siloed systems that create overlap in processes and, more importantly, information gaps that hinder collaboration, efficiency, and ultimately growth.

Digital transformation enables companies to realize holistic manufacturing operations. This is achieved through real-time distributed manufacturing architecture (DMA).

Achieving lean, high-speed manufacturing requires that product, material, and information flows all work together in harmony. Information flow impacts the efficiency of a responsive manufacturing supply chain. Smart manufacturing systems provide optimized, fast, and reliable product and material flows. These systems must be integrated and networked so that product/process data and enterprise production information can “flow” seamlessly. The key to competitive advantage in manufacturing is not how well each system works, but how well they all work together.

Simplified hierarchies

Industrial automation is shifting from hierarchical Purdue models to more responsive architectures and achieving integrated real-time manufacturing goals. The origins of this shift were discussed in a 2012 article Titled “Simplifying Automation System Hierarchies.” Now, these architectures are increasingly being deployed to achieve more efficient and profitable production. New technology makes it possible to simplify this model to eliminate layers, improve performance, and reduce software maintenance costs.

The traditional strict hierarchy architecture is giving way to a more responsive and direct model for creating real-time, highly responsive manufacturing enterprises. Field devices can communicate information directly with applications, including historians, advanced cloud analytics, real-time maintenance monitoring, and other functions. This simplifies the implementation of these functions and eliminates Level 2 and Level 3 software costs, complexity, performance drag, and ongoing software maintenance.

Over the years, industrial automation architecture has been marked by increasing computing pushing towards end-of-field devices, leveraging distributed computing to increase performance, quality, reliability, availability, responsiveness, and reduce software maintenance costs. At every step, the limiting factor has been the cost, robustness, and reliability of the technologies. This has changed with the significant low-cost commercial, consumer, and Internet of Things (IoT) technology and communications advancements that are prevalent in everyday life.

Smartphones are everyday devices owned by many people and are a clear example of a robust and powerful computer with integrated communications and display.

The most widely used industrial automation architecture model to describe manufacturing operations management is the five-level Purdue Reference Model (PRM), which then forms the basis. ISA-95 standardThis five-tiered hierarchical architecture has served the industry well for years, being easily deployed with existing technology. The model is typically expressed as follows:

• Level 5: Business systems
• Level 4: Facility level (enterprise resource planning (ERP), material requirements planning (MRP), and manufacturing execution systems (MES))
• Level 3: Operation unit
• Level 2: Machine/process automation
• Level 1: Controller
• Level 0: Sensor/actuator.

Traditional automation systems typically reflect this architecture, with software running on general-purpose computers at Levels 2, 3, 4, and 5. Levels 2, 3, and 4 typically include database and communication interfaces that buffer and synchronize information between each level, in addition to associated human-machine interfaces (HMIs) and user interfaces. Computational costs and network bandwidth constraints dictated this configuration based on past technology. The multi-level computational model is complex and creates significant cost, ongoing configuration control, and lifecycle investment. Fortunately, this model is changing to provide a more efficient and streamlined automation system architecture.

Industrial manufacturing organizations are eliminating barriers between functional silos that create overlap and knowledge gaps in processes and that hinder collaboration, efficiency, and ultimately growth. Manufacturing companies are becoming more tightly integrated into the enterprise, reflected in the integration of systems from sensor to enterprise. The transformation to integrated, real-time, data-driven manufacturing is eliminating inefficiencies, increasing responsiveness, increasing profits, and stimulating competition.

The move to a digital manufacturing architecture (DMA) is a fundamental building block for transformation that has implications such as sensing and control devices extending from the enterprise level to the cutting edge of manufacturing and production. (Figure 1). This distributed system includes applications on embedded processors in sensors, actuators, barcode readers, cameras, and other field devices that can be controlled locally, but equally importantly, can be accessed remotely at any time for complex calculations and adjustments.

This architecture creates a closed loop, allowing for real-time transaction processing and synchronization with production. In addition to being highly integrated, efficient DMAs:

• Gain instant visibility across your entire organization
• Provide consolidated, accurate and timely data for decision making
• Adjust and optimize based on supply chain changes and customer demand.

In the new model, controllers can directly transmit information to all levels using appropriate methods and protocols. Ethernet communication has become a high-speed and widespread technology used by industrial automation protocols and business systems. More controllers support multiple Ethernet ports to directly interact with industrial and business networks in industrial facilities. Historians, analytics, real-time maintenance monitoring and other functions are now being incorporated into controllers.

This simplifies implementation of these functions and eliminates traditional architecture software costs, complexity, performance drag, and ongoing software maintenance. More powerful controllers and communications enable coordination between controllers without the need for a separate computer to coordinate them.

The most effective architecture requires orchestrating and optimizing all elements of the process for flexibility in the face of external changes such as supply chains, customer demands, costs, availability, energy and sustainability requirements. Emerging DMA technology leverages advances in distributed computing and open systems to achieve this and achieve synchronized, real-time, optimized production. (Figure 2).

Customer orders, supply chain drivers and factory operations are fed into digital twins, an ideal operating model of the facility and its processes. Real-time connections across the system create a closed loop (Figure 3) with continuous feedback, analytics, AI and machine learning to adjust and optimize operations.

Digital manufacturing architecture requirements are driving the integration of industrial cybersecurity with mainstream information technology (IT), cloud, and IoT protection technologies and methods to create more secure manufacturing environments. Key technological advances include the incorporation of firmware/hardware into controller smart sensors, actuators, and other field-edge devices.

Real-time digital manufacturing is about becoming a more efficient, holistic and competitive business. This increases reliability, quality, production, profitability, safety, flexibility, informed decision making and overall competitiveness as a business.

_{This feature was first released on: AUTOMATION 2024: 9th Annual Industrial Automation and Control Trends Report.}

Download AUTOMATION 2024: The 9th Annual Industrial Automation and Control Trends Report

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