Digital Twins: Accelerating Manufacturing & Sustainability

Virtual twins and predictive maintenance are essential for manufacturing companies who want to be sustainable and competitive, says DELMIA's Louis Columbus

The manufacturing sector is embracing AI to redefine operational paradigms. It's no longer just about meeting production targets.

Now, AI-driven predictive insights are promoting sustainability and resilience in operations.

Core to this change is predictive maintenance technology, bolstered by the Industrial Internet of Things (IIoT).

This AI-enhanced framework converts real-time data into strategic actions for efficient, sustainable machine operation.

Central to this evolution is the concept of virtual twins. These AI-powered digital duplicates bridge the gap between the physical and virtual, allowing unprecedented levels of simulation, optimization and refinement in manufacturing processes.

AI in manufacturing operations management

Manufacturing Operations Management (MOM) software includes a suite of activities integral to production, such as quality management and logistics.

Within this suite, Manufacturing Execution Systems (MES) play a pivotal role.

MOM and MES software, vital in AI transformations, serve distinct functions.

While MES operates on the shop floor, directing labor and materials while capturing real-time data to ensure production efficiency, MOM provides broad AI-integrated oversight as MES handles tactical aspects.

MOM merges MES with functions like quality and logistics, optimizing operations across the enterprise.

Envision MES as the operational arm managing immediate needs, while MOM, with AI, offers a strategic perspective enhancing the manufacturing lifecycle through data-driven insights.

This integration ensures synergy, balancing shop-floor precision with a holistic enterprise view, amplified by AI, boosting efficiency and compliance.

Virtual twins: AI-powered future of maintenance

Virtual twins are an AI evolution of digital twins, which traditionally model physical assets and processes.

These twins now embrace AI for dynamic connectivity and reuse across multiple contexts, introducing a fourth dimension—time.

Advanced virtual twins integrate with MOM software via AI, providing real-time feedback loops between the virtual and physical worlds.

This capability allows manufacturers to visualize, predict and enhance operations effectively.

Key AI-driven benefits of virtual twins:

• Real-Time Monitoring: AI-enhanced virtual twins provide a live view of equipment, instantly flagging inefficiencies.
• Predictive Analytics Integration: AI algorithms preempt failures, scheduling maintenance activities efficiently.
• Scenario Simulation: Manufacturers employ AI to run scenarios analyzing operational adjustments and demands.
• Sustainability Insights: AI helps optimize resources, aligning operations with sustainability goals.
• Integration with MOM Systems: AI fuses virtual twin insights into manufacturing workflows, fostering continuous improvement through real-time data.

Overcoming AI implementation challenges

While AI-driven predictive maintenance transforms manufacturing, integrating IIoT solutions presents hurdles.

Among these challenges is managing large data volumes.

Edge computing, facilitated by AI, offers a solution by processing data locally, minimizing latency for immediate insights.

Additionally, scalable data platforms like MOM collate and contextualize data, rendering it actionable.

Legacy systems complicate IIoT integration. Here, AI-integrated MOM provides a bridging architecture, connecting operational with IT systems to enable seamless data transfer.

Other challenges include skill shortages, which demand multidisciplinary AI training and recruitment efforts.

AI tools facilitate this transition with user-friendly platforms that simplify adoption.

The rise of IIoT also increases cybersecurity vulnerabilities.

AI-enhanced security protocols, such as encryption and regular audits, are crucial to protect systems.

Sustainability through AI and predictive maintenance

AI-powered predictive maintenance supports sustainability by optimizing resource use and minimizing waste.

MOM solutions embed AI-driven sustainability metrics, allowing manufacturers to track and improve environmental impact.

AI-driven sustainability practices:

• Energy Optimization: AI insights refine energy use, aligning with efficient production schedules.
• Waste Reduction: AI algorithms highlight inefficiencies, reducing scrap and enhancing recycling.
• Circular Economy Support: AI in MOM supports material reuse, minimizing landfill contributions.
• Lifecycle Assessments: Virtual twin simulations, powered by AI, assess environmental impact, influencing design and production decisions.
• Real-World Impact: With MOM and AI, manufacturers report up to a 25% environmental footprint reduction by harnessing IIoT and AI.

AI paving the way forward

Predictive maintenance and virtual twins, enhanced by AI, are becoming essential for competitive manufacturing.

These AI-driven Manufacturing and Operations solutions demonstrate how such technologies foster efficiency, resilience and sustainability.

Embracing AI innovations prepares manufacturers for current challenges while positioning them at the forefront of the smart factory revolution.

Ultimately, the factory of the future isn't a distant concept—it is being realized today through AI, offering limitless possibilities.

Written by: Louis Columbus, writer, for AI Magazine.