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Digitally Native Manufacturing: An Innovative Concept in Manufacturing Automation

Digitally native plants are new-generation operating plants that are self-evaluating, self-learning and self-optimizing. These autonomous plants will run at the highest overall equipment effectiveness and lowest operational risks and have operational flexibility to be profitable in a dynamic market environment. They will be built on a scalable digital platform that enables direct ‘edge to cloud to edge’ communication architecture where feasible, potentially flattening the conventional Purdue Model. The Purdue Model is a framework for industrial control systems security that helps protect OT from malware and other attacks. It is based on the idea that separating IT from industrial control systems and industrial infrastructure can help keep these environments secure.6

Disruption of Purdue Model–based architecture will lead to a fundamental shift in the way the life cycle of a plant and its automation will be managed. Manufacturing automation providers of digital technology, control systems, sensors, valves, IT hardware and software and advanced applications will have to think differently to deliver a digitally native plant. The operators will need to upskill their workforce and redesign the organisation and work processes to fully leverage the potential. The autonomous plants will challenge existing industry guidelines and best practices around safety and reliability and will need plant operators to influence governing bodies to ensure alignment with the new technology and organisational capabilities. This is a fundamental re-imagining of various models and process manufacturing.

Throughout the life cycle of a plant, digitally native plants can achieve the following objectives:

Manufacturing plant: design phase
i. Minimise total cost of ownership over the life of the plant by tightly integrating digital technologies (for example, advanced simulation, digital twin) from design to operations to optimisation
Building the manufacturing plant
i. Reduce commissioning, start-up time and time to full-capacity operation by instituting digital-ready instrumentation and control requirements from design, automating procedures and software-defined control to future-proof OT systems
Stabilisation and successful operations
i. Run flexible operations to adjust to market demands at lowest costs and higher efficiency with real-time automated and AI-optimised control
ii. Significantly reduce plant personnel requirements with autonomous and streamlined plant workflows from operations, maintenance and compliance, using advanced tools with human-in-the-loop automation
iii. Minimise risk to digital and capital assets through real-time visibility to cyber threats and opportunities
iv. Overdrive ESG goals with real-time sensing and analytics of process and emissions and closed-loop corrective actions for driving sustainability goals
Organisation and operating model
i. Maximise the effectiveness of the workforce with ‘connected everywhere’ industrial workers armed with sensors, VR/AR and digital handheld devices that provide guided instructions and on-demand training over high-speed cellular or wireless networks that are always connected
ii. Upskill and reskill the workforce through AI-driven knowledge platforms, anywhere anytime ‘experiential training’ and frictionless access to remote experts

Digitally native plants are built with a strong edge-to-cloud strategy. Cloud provides the advantages of faster software deployment, lower maintenance cost through shared infrastructures and automated updates, unlimited and flexible computing and storage scalability and lower carbon footprint through shared resources and efficiencies of scale. The smart edge-to-cloud architecture delivers optimised performance, scale, latency and security based on the application requirements.

Unlike the past, adding sensors (sensorisation) and connecting the machine to the edge network can now be done through a plug-and-play model. This is a new-age concept that encompasses all the latest technologies blended together for a greenfield manufacturing facility. It has great potential to bring a paradigm shift in the way the industry designs, builds, operates and improves manufacturing plants in the future.

练习题

Which description best captures a digitally native manufacturing plant?

A. A plant that relies mainly on manual inspection and fixed production schedules

B. A plant that is self-evaluating, self-learning, self-optimizing, and operationally flexible

C. A plant that avoids cloud services to preserve the traditional Purdue Model unchanged

D. A plant that improves only during the design phase and not during operations

In the source material, what is the main security idea behind the Purdue Model?

A. Combining all IT and OT networks into one flat cloud network

B. Separating IT from industrial control systems and industrial infrastructure to help protect OT

C. Eliminating sensors from manufacturing plants to reduce cyber exposure

D. Replacing all human operators with autonomous agents

Which option correctly matches a plant life-cycle phase with a digitally native plant objective?

A. Design phase: minimize total cost of ownership by integrating digital technologies such as simulation and digital twins

B. Building phase: delay commissioning so that operators can manually validate every control loop

C. Operations phase: reduce market responsiveness by avoiding AI-optimized control

D. Organization phase: remove all training because connected devices replace workforce skills

Which statements are described advantages or design goals of edge-to-cloud strategy in digitally native plants? Select all that apply.

A. Faster software deployment through cloud-enabled delivery

B. Lower maintenance cost through shared infrastructure and automated updates

C. Unlimited and flexible computing and storage scalability

D. Optimizing performance, scale, latency, and security based on application requirements

E. Permanently eliminating the need for cybersecurity visibility

Which workforce and organization changes are associated with digitally native plants? Select all that apply.

A. Operators need to upskill the workforce and redesign organizational work processes

B. Connected industrial workers may use sensors, VR/AR, and handheld devices for guided instructions and training

C. AI-driven knowledge platforms can support upskilling, reskilling, experiential training, and access to remote experts

D. Human-in-the-loop automation can streamline workflows in operations, maintenance, and compliance

E. Workers should be disconnected from high-speed networks to avoid guided training

Disrupting Purdue Model-based architecture may fundamentally change how a plant and its automation are managed across the plant life cycle.

A digitally native plant uses real-time sensing and analytics only to improve production speed; the source does not connect them to ESG or sustainability goals.

Adding sensors and connecting a machine to the edge network can now be done through a ___ model.

Explain how digitally native plants connect AI-optimized operations with earlier concepts of machine learning and distributed autonomous systems.

Describe two ways digitally native manufacturing can create a paradigm shift across plant design, building, operations, and future transformation.

A manufacturer wants a plant that can adjust production to market demand while improving its own control decisions over time. Which combination of ideas best supports this goal?

A. A digitally native plant using real-time automated and AI-optimized control, supported by machine learning that analyzes data and learns to make informed decisions.

B. A plant that separates all IT and OT systems permanently and avoids using data-driven decision tools.

C. A plant that relies only on manual operators because autonomous systems cannot pursue objectives without direct supervision.

D. A plant that focuses only on faster software deployment in the cloud and does not change operations or control.

Which statements correctly connect digitally native manufacturing with earlier ideas about intelligent process automation and human-machine collaboration?

A. Autonomous and streamlined workflows in digitally native plants relate to IPA because IPA removes repetitive, replicable, and routine tasks.

B. Human-in-the-loop automation in digitally native plants is consistent with the idea that effective solutions require humans and machines working together.

C. Connected workers using guided instructions and on-demand training conflict with workforce upskilling and reskilling.

D. IPA can improve manufacturing by simplifying interactions and speeding processes, which aligns with reducing commissioning and start-up time through automated procedures.

E. Digitally native plants eliminate the need for any human guidance because intelligent systems never require review or refinement.

Because digitally native plants are self-evaluating, self-learning, and self-optimizing, human managers no longer need to define the business problems the plant should solve.

In digitally native manufacturing, edge-to-cloud architecture can support scalable computing and real-time plant optimization, while machine learning contributes by using data analysis to learn and make ___.

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