AI in Manufacturing

What is AI in Manufacturing?

Artificial intelligence (AI) in manufacturing works within both information technology (IT) and operational technology (OT) environments, using AI-driven tools like machine and deep learning to optimize industrial workflows and production.

Why is AI in manufacturing important?

AI is helping improve several aspects of manufacturing, ranging from day-to-day operations by streamlining processes to influencing new product development (NPD), accelerating time to market, and creating competitive advantages.

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How does AI impact manufacturing?

With the rapid rise of the Internet of Things (IoT), edge and cloud networks, and tools like machine learning (ML), manufacturers not only have access to more data than ever before, but they can take more meaningful actions with captured data: equipment maintenance, supply chain monitoring, optimizing production processes (including quality assurance [QA]), faster product creation, and accelerated NPD.

What are the benefits of AI in manufacturing?

More visibility and real-time analysis

Increased availability of devices and sensors that can monitor specific conditions increases visibility across the entire manufacturing environment, from supply chain to production line. In turn, AI can collect that data and unify it so manufacturers can easily monitor their operations while on-site or remotely, across multiple locations.

Preventative maintenance

Using the same sensors that give manufacturers better visibility into their manufacturing environments, AI can use historical data to identify underperforming equipment or machines on the verge of breaking down. By alerting administrators and managers beforehand, AI helps minimize downtime, and even circumvent it altogether by scheduling or recommending proactive repairs and updates.

Enhanced quality and inspection

Human-based QA processes can be prone to error, due to a variety of factors ranging from lackluster training to simple fatigue. AI-driven machines, on the other hand, outperform humans on rigorous, monotonous tasks. As a result, overall product quality can remain high, and humans can focus more on higher-level tasks and thinking—all leading to improved customer and employee satisfaction.

Improved, faster product design

AI and virtualized environments eliminate the need for physical and time-intensive testing processes. In the past, product testing required exhaustive research, separate facilities, and hands-on analysis. AI takes over much of that process without the overhead, leading to downsized costs and accelerated time to market.

How can AI accelerate business decisions and enable zero defect in the manufacturing industry?

AI increases opportunities for automation across manufacturing, including aspects of the QA process, a key point in uncovering product defects before they are shipped and sold to customers. Traditionally, this process is done manually, making it prone to human error and the primary reason for expensive product recalls, wasted resource allocation, and diminished profit margins. Traditional QA also focuses on the latter manufacturing phases of production rather than the entire process.

AI is a game-changer for manufacturing companies. First, it enables machine vision, a digital video method designed for monitoring specific environments. Captured images are analyzed automatically by the AI and compared against assigned pass-fail criteria in real time, alerting teams to take action as needed. Compared to more manual methods, machine vision is more reliable—as long as it's set up properly. AI-driven machine vision can also predict potential defects, similar to how it performs preventative maintenance on manufacturing equipment, and do so across the entire manufacturing process.

Overall, AI in manufacturing can be an immense cost saver that becomes an end-to-end solution that drives and, in some instances, improves quality and satisfaction. Insights gleaned throughout the process can also influence business decision-making, influencing process refinements, material sourcing, product design, and more.

What are the challenges of AI in manufacturing?

One of the primary challenges of implementing AI in manufacturing is the large infrastructure investment required, ranging from edge and cloud networks to not having enough quality data to inform actionable algorithms. Edge networks require lots of devices, sensors, processing power, and bandwidth to perform without latency, which is essential for real-time data analysis. Without all the above across the entire technology stack, companies can’t expect effective predictive models or comparable quality results.

The IT complexity and data quality demands of AI solutions are a primary reason why 87% of manufacturing data analysts say data quality issues are the reason their organizations failed to successfully implement AI across number of machines, devices, and other components.

HPE and AI in manufacturing

HPE AI solutions are helping transform and accelerate manufacturing. The future of manufacturing is connected, automated, and digitally driven. As plant floor operations technologies converge with IT, numerous use cases across the manufacturing cycle become possible to ignite innovation, create more efficient operations, reduce downtime, and improve worker productivity.

HPE GreenLake for Manufacturing helps maximize the potential of the Industrial Internet of Things (IIoT), making vast amounts of data available to manufacturers and addressing the challenge of transforming that data into insights that uncover inefficiencies and support competitiveness. HPE GreenLake offers workload-optimized services that enable faster access to data and insights, combining the automation, agility, and economics of the cloud with the security and performance of on-premises infrastructure. IT can now deliver a modernized solution that is adaptable and streamlined to provide business efficiencies from the shop floor to the executive suite.

The HPE GreenLake platform is also available as a service (i.e., a consumption-based model), giving companies the scalability and flexibility to quickly apply AI to manufacturing workflows without burdensome capital costs and respond to changing market conditions, production bursts, new product or factory spin-ups, and facility expansions.