How AI and cloud are reshaping the factory floor

What major disruptions are shaping automotive and manufacturing today; and how is AWS helping customers respond?

Manufacturing has always had to navigate supply chain uncertainty, but since COVID-19 and the broader trends of on-shoring and re-shoring, the need for resilience has intensified. Manufacturers are now focusing heavily on building a more robust and transparent supply chain.

AWS helps in two key ways. First, we enable real-time visibility, so manufacturers can track materials and components, understand where bottlenecks are, and adjust production based on more accurate forecasts. Second, we help customers adapt their operations dynamically as supply conditions shift.

AWS Supply Chain plays a central role, it provides built-in machine learning and AI to give manufacturers actionable insights and help optimise their supply chain performance. In parallel, many customers use services like Amazon SageMaker to build and train their own models for demand forecasting, production planning, and other critical workflows.

You have experience across manufacturing and automotive segments. What are the most compelling use cases for cloud and AI in these industries in Vietnam and regional countries?

In 2015, when Industry 4.0 started gaining momentum, everyone was focused on predictive maintenance as the main use case. What we’re seeing today is that quality has become the use case delivering the most value. In automotive, for example, companies are using computer vision to detect defects and anomalies.

In more process-driven manufacturing, such as battery cell production for electric vehicles (EVs) or consumer packaged goods, the emphasis is on process control and what we call the golden batch. Manufacturers are trying to use AI to prevent quality issues before they enter production, which remains one of the biggest cost drivers in the industry.

Working with our customers in Vietnam, we see a wide range of use cases. Some are focused on the early stages, ingesting data into AWS to enable condition-based monitoring. Others are further along, transforming their entire manufacturing application stack and moving key systems like the Manufacturing Execution System (MES) to the cloud. MES is a core application for running the shop floor.

For countries like Vietnam, how can they successfully transform from traditional manufacturing to digital one?

There has been significant investment in Vietnam. Many manufacturers now see Vietnam as a key location for production, and we’re seeing many new factories being built. The capabilities and skill levels within Vietnam’s manufacturing sector have grown noticeably over the past few years. And this trend is not just in Vietnam, it’s happening across ASEAN as well.

It comes down to investing in both technology and people. As AI becomes more important in manufacturing, new skill sets are required, from building AI models to deploying them on the shop floor. This is where our training and certification programmes, professional services, and collaborations with universities and colleges matter. When students graduate already cloud-ready and familiar with AI, they can support the digital transformation of the industry.

The automotive and manufacturing value chain is complex. How should organisations structure cloud and engineering partnerships to drive digital transformation?

Within the manufacturing team, we organise around solution areas. We have four solution areas. One is product engineering, helping manufacturers design and develop those products. Here we work with partners like Siemens and PTC to run their applications on AWS and leverage our high-performance compute services such as PCS and our HP7 instances.

Then we have manufacturing. We focus on factory operations. This is where our Internet of Things (IoT) services like AWS IoT SiteWise and partners such as HiByte and Cognite come in to support key use cases.

Next is supply chain built around AWS Supply Chain and partners like O9 Solutions. And then finally, our smart products and services, which covers connected products, from robot vacuum cleaners to aircraft engines. Manufacturers use AWS to build the IoT platform behind these products and then support services and after-market operations.

We look at the value chain through these four solution areas, and within each one, we offer specific capabilities, partners, and solution guidance to help manufacturers address their use cases.

With sustainability a priority, how can companies leverage cloud, data, and AI to reach their goals?

There are three main parts. First, inside the factory, manufacturers are looking at how to become more sustainable by optimising their energy consumption. This is where IoT and analytics play a key role, helping them understand how they use natural resources and optimise processes accordingly.

Second, companies can move workloads from on-premises to the cloud, especially research and development (R&D) workloads, to take advantage of on-demand compute and storage. Using services like our Graviton instances allows them to optimise R&D workloads while also reducing their data centre footprint.

Third, sustainability is not just about how products are manufactured, but also about the sustainability of the products themselves. For example, using recyclable materials, which often requires new processes. We work with manufacturers to help develop new materials and new manufacturing methods, so products are more sustainable throughout their lifecycle.

And then there are areas where everything comes together. Vietnam, for example, is investing heavily in electrification. VinFast is rolling out new vehicles and electric motorbikes, and battery passports are becoming essential for EVs. We’re helping manufacturers build those battery passports so they can share information across the supply chain.

How are AI and cloud set to reshape the manufacturing industry, and what should companies do to adapt?

AI is a broad term. In manufacturing, we see predictive maintenance powered by traditional machine learning, and we see knowledge assistants powered by generative AI and large language models. Now we’re seeing GenAI being used to optimise processes and workflows.

Looking ahead, the skilled labour shortage will continue, and manufacturers will invest more in automation and robotics. AI, especially physical AI, will play a crucial role in enabling manufacturers to deploy automation and robotics to address these labour gaps.

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