Attributed to Alex Teo, Vice President & Managing Director, Southeast Asia, Siemens Digital Industries Software
Across Southeast Asia, manufacturers are under increasing pressure to accelerate innovation while managing growing product complexity, shorter production lifecycles, and evolving customer expectations. Digital transformation has become essential to helping manufacturers respond with greater speed, flexibility and resilience.
Governments across Southeast Asia are helping accelerate this shift. In Singapore, initiatives such as the National AI Strategy 2.0, AI for Enterprise Impact Playbook and the Physical AI testbed at Punggol Digital District are helping companies move AI from pilots into industrial operations. Vietnam is similarly prioritising smart manufacturing and Industry 4.0 to strengthen industrial competitiveness.
However, the journey to enterprise-scale digitalisation is often not linear. The most effective approach is to start with a high-value use case that delivers immediate business value while laying the foundation for greater scale and more advanced capabilities over time.
Early adopters in Southeast Asia see digitalisation differently
Manufacturers leading in digitalisation see it as a long-term investment in competitiveness rather than another technology project. They recognise that building resilience and productivity requires sustained commitment rather than isolated deployments.
That is evident in sectors such as apparel manufacturing in Vietnam, where Jack Technology’s collaboration with Siemens is helping advance intelligent apparel manufacturing through agentic low-code development, digital engineering and AI-enabled automation. By using Siemens’ Intelligence Center X, the result is a more connected production environment with shorter development cycles and up to 30-percent higher manufacturing efficiency.
That said, adoption is only the first step. The real challenge is not simply deploying new tools, but integrating them into existing workflows and processes so they work together as part of a connected system. In practice, many companies return for additional support not because they need help using a single application, but because they need guidance on how different tools, data flows, and teams can operate as one coherent environment.
Building Digital Twin maturity step by step
For many manufacturers, digital transformation can seem daunting. Rather than digitising the entire organisation at once, the most effective approach is to start with a clearly defined use case that delivers measurable value.
As organisations see measurable results, confidence grows and adoption naturally extends across teams. Early scepticism often gives way to stronger support as employees experience tangible improvements in productivity and collaboration.
One common starting point is virtual code validation, where programmable logic controller (PLC) code is tested against a virtual machine instead of physical equipment. It delivers immediate value while laying the foundation for broader Digital Twin capabilities.
From there, companies often progress to virtual pre-acceptance testing, virtual commissioning, and customer training before equipment is physically delivered. As value becomes clear, organisations identify additional opportunities to extend the Digital Twin across production and operations.
How AI is extending the digital twin
AI will be one of the biggest forces shaping the next evolution of the Digital Twin by making advanced engineering and simulation capabilities more accessible. AI-powered agents can automate routine work, surface insights and support collaboration across disciplines, enabling engineers to focus more on solving complex problems than repetitive tasks.
Before deployment at Siemens’ electronics factory, a humanoid robot was trained and validated using NVIDIA and Siemens’ digital twin technologies, allowing AI models to learn and refine behaviours in a virtual environment before operating on the factory floor.Alex Teo
That potential is already becoming visible in practice. For example, Haddy is using Siemens Xcelerator to connect product design, manufacturing planning, simulation, and automation through a consistent digital thread, while applying AI to continuously optimise operations. By combining the AI-enabled digital twin with software-defined manufacturing, Haddy can scale distributed production while maintaining consistent quality, accelerating iteration, and enabling faster, data-driven decision-making.
Training Industrial AI in the virtual world
The relationship between Industrial AI and the Digital Twin is mutually reinforcing. High-fidelity virtual environments allow AI models to be trained before physical production begins, reducing the time needed for AI to deliver value once deployed on the factory floor.
The Digital Twin also provides a safe environment to validate AI algorithms before they are applied to live production systems. In industries where quality and process integrity are critical, this ability to test and refine AI in a virtual environment significantly reduces implementation risk.
Siemens’ collaboration with Humanoid and NVIDIA illustrates this shift. Before deployment at Siemens’ electronics factory, a humanoid robot was trained and validated using NVIDIA and Siemens’ digital twin technologies, allowing AI models to learn and refine behaviours in a virtual environment before operating on the factory floor. This simulation-first approach reduced prototype development from 18–24 months to just seven months while helping ensure the robot met production performance targets once deployed, demonstrating how virtual environments can accelerate Industrial AI adoption while reducing implementation risk.
Scaling the Digital Twin across Southeast Asia
The convergence of the Digital Twin and Industrial AI is reshaping manufacturing across Southeast Asia. But AI can only deliver lasting business value when it is built on a strong digital foundation.
Manufacturers that invest in connected engineering data, integrated workflows and Digital Twin capabilities today will be better positioned to scale Industrial AI tomorrow. As governments continue to support industrial digitalisation and businesses expand the use of AI, those with mature digital foundations will be able to innovate faster, improve operational resilience and compete more effectively in an increasingly complex manufacturing landscape.