Get ready for the future of manufacturing with BMW Group’s new electric vehicle plant in Debrecen, Hungary. Opening in 2025, this “digital-first” factory is revolutionizing the industry with real-time simulations using digital twins. From layout to robotics to logistics systems, everything will be finely tuned before the factory even goes online.

AI is playing a crucial role in this transformation. Intelligent technologies and products are enabling new and improved use cases across the entire manufacturing lifecycle. From product design to engineering to fabrication, testing, and assembly, AI is driving innovation and efficiency.

Keeping up with customers and markets, smartly

Digital-first factories are at the forefront of the global boom in “Industrial AI”. Manufacturers are embracing advanced analytics, AI, cloud technology, robotics, the Industrial Internet of Things (IIoT), and more to accelerate and transform their operations. In the face of economic uncertainty and supply and labor shortages, smart manufacturing is becoming a key strategy for companies to stay competitive in the Fourth Industrial Revolution.

In 2023, manufacturers are projected to invest a significant 16.6% of $154 billion global AI sales in intelligent technology. This investment is driven by the desire to improve efficiency, agility, and sustainability.

Manufacturers are leveraging AI to achieve greater intelligence, improved agility, and enhanced sustainability. These goals align with the need to increase precision, throughput, and yields while reducing costs. Additionally, AI enables faster product design, better performance analysis, and a more flexible and resilient supply chain. It also helps reduce energy costs, minimize environmental impact, and optimize logistics and transportation routes.

New and advanced use cases 

So how are manufacturers implementing AI to achieve these benefits? Predictive maintenance and digital factory twins are among the key areas of investment. Predictive maintenance, powered by AI and GPU-accelerated computing, allows manufacturers to analyze vast amounts of data in real-time. This proactive approach helps prevent problems before they occur, reducing downtime and improving overall efficiency. By pinpointing root causes and taking corrective action, manufacturers can prevent future quality issues.

Quality assurance and inspection

When it comes to AI priorities, quality assurance and inspection are at the top of the list for many companies. The American Society of Quality (ASQ) reports that defects cost manufacturers nearly 20% of their overall sales revenue. These defects not only lead to product recalls and warranty costs, but they also damage brand image, sometimes irreparably.

To address this issue, manufacturers are turning to AI-based computer vision applications to detect defects faster and more reliably. However, current automated optical inspection (AOI) machines require significant human involvement and capital. Fortunately, new methods are emerging that leverage AI and machine learning (ML) more effectively to improve the quality of manufactured components. These methods can identify defects such as cracks, paint flaws, misassembly, bad joints, and foreign bodies like dust and hair.

One innovative approach in development involves using object perception and synthetic data to train models that can detect specific defects with greater speed and accuracy.

Supply chain resilience and efficiency

The COVID-19 pandemic exposed the vulnerabilities of many companies’ supply chains. Shortages of finished products and parts, from toilet paper to semiconductors, continue to persist. In a recent survey, 72% of manufacturers identified disruptions in supply chains and parts shortages as their biggest uncertainty for 2023. Shipment delays remain a top concern, with lead times often doubling.

In response, supply chain professionals are investing in making their supply chains more resilient, with a focus on leveraging cloud technology. Data analytics and AI/ML are being deployed to improve demand forecasting, optimize logistics and transportation, and coordinate suppliers and distributors. The goal is to prevent and minimize disruptions while increasing efficiency and agility.

Manufacturers that succeed in this new normal will leverage AI and secure, scalable cloud technology to improve planning and optimization. This can lead to increased service levels, reduced costs, and better end-to-end visibility for minimizing risk and capitalizing on opportunities.

Slowed by complexity and far-flung data

Despite the booming investments in digital and data foundations, the manufacturing sector lags behind other industries in implementing operational AI.

The challenges of scaling AI solutions across complex system environments and diverse machine landscapes hinder progress. High implementation costs, including specialized technology stacks, also pose obstacles. Additionally, the abundance of data collected from various sources presents difficulties in deriving actionable insights due to issues with quality, availability, and centralization.

However, advancements in technology offer hope for overcoming these challenges.

Technology advances promise progress

Manufacturers can overcome these challenges by leveraging new and proven cloud technologies.

“AI-first” environments

Conventional IT infrastructure is insufficient for handling the growing demands of manufacturing AI workloads. Purpose-built “AI-first” infrastructure and toolchains simplify and accelerate training and deployment, while unifying data from multiple sources. Cloud-based AI infrastructure offers flexibility, scalability, cost reductions, and new capabilities without heavy capital expenses.

Supercomputing

The lack of computing speed hinders AI efforts in manufacturing. High-performance computing (HPC) can significantly accelerate AI delivery, reducing training time and improving real-time requirements.

Supercomputing is no longer limited to a select few. Thanks to cloud-based delivery, manufacturers now have wider access to supercomputing infrastructure and software. This means immediate and flexible access to the tools needed for training models in generative AI and other data-intensive applications.

Microsoft and NVIDIA have joined forces to offer supercomputing as an on-demand service. This service, available globally and billed monthly, provides enterprises with instant access to the necessary infrastructure, software, and computational power for training, building, and deploying advanced AI models and applications, from the cloud to the edge.

Welcome to the Industrial Metaverse

Factories that prioritize digitalization, like BMW’s, rely on bridging the physical and virtual worlds. By connecting real-time data from physical sensors to their digital counterparts in the emerging “Industrial Metaverse,” it becomes possible to automate, simulate, adjust, and predict AI-driven business processes in real time. According to Deloitte, one in five manufacturers is already experimenting or developing a metaverse platform or solution for their own products.

New services are making it easier for enterprises to leverage the metaverse for smart manufacturing. NVIDIA Omniverse Cloud, a platform-as-a-service (PaaS), offers developers instant access to a full-stack, native, and agnostic environment. By integrating with Azure Digital Twins and Internet of Things cloud services, manufacturers can build and operate industrial metaverse applications and accurate, dynamic, fully functional 3D digital twins. Azure provides the necessary cloud infrastructure and capabilities, including security, identity, and storage, to deploy these enterprise services at scale.

These new capabilities greatly enhance manufacturers’ ability to digitally monitor, simulate, control, and operate physical assets. This translates into improved visibility into operational performance, faster issue prediction, and quicker course correction.

Collaboration at its Best

Integrating 3D platforms with Microsoft 365 Teams, OneDrive, and SharePoint enables geographically dispersed teams to collaborate in real-time through video, voice, and simulations. Accenture recently showcased an impressive early effort aimed at reducing the time between decision-making, action, and feedbac

As these approaches continue to mature, technicians in service centers could use AR glasses to perform complex repairs in a virtual environment. They can also connect with other experts to work on the problem using digital twins.

A German company has introduced a groundbreaking technology that allows manufacturers to transform 3D data into scalable applications and interactive experiences. With Instant3DHub, developers can collaboratively build, deploy, run, and automate applications with any data, on any device, and of any size.

Generative AI is also emerging as a valuable tool for enhancing factory automation and operations. Siemens and Microsoft have demonstrated how plant workers and others can use natural speech on mobile devices to document and report manufacturing, quality, or product design issues.

Smarter Manufacturing for All

Not every manufacturer needs to be at the forefront of AI technology, but all can benefit from implementing AI and simulation. Improved quality, greater efficiencies, stronger supply chains, and accelerated time-to-value and innovation are the hallmarks of smart manufacturing.

Microsoft Azure and NVIDIA are partnering to accelerate AI through GPU-powered Azure cloud infrastructure and solutions that bring manufacturers real-time speed, predictability, resilience, and sustainability.