Digital twins are becoming a foundational element of modern machine building, closely linked with automation across manufacturing plants and industrial systems. A digital twin is a virtual model of a physical machine, system, or process that uses real-time data to mirror performance, behaviour, and operating conditions in a live production environment. By creating this digital representation, manufacturers can optimise machine design, predict issues, and improve overall operational efficiency.
Industry research indicates that digital twin technology is expected to deliver significant economic and operational impact over the coming years. However, manufacturers can only realise this potential when machines are networked, data-enabled, and capable of responding to change through continuous monitoring, analysis, and optimisation. This relies on robust communication frameworks that allow integrated systems to share planning, processing, and production data in real time.
Developing an effective digital twin requires scalable software platforms that support the entire value chain. When implemented correctly, machine building becomes more flexible and efficient, enabling engineers and electricians to design, operate, and adapt machines with greater ease. This approach also supports the production of customised products without compromising reliability or performance.
There has never been a better time for Australian manufacturers to think bigger about how machine building and digital twin technology can shape the future of industrial production.
1) Visualise the opportunities
Give your business permission to think bigger and explore what’s possible. One of the strongest trends in modern machine building is the growing demand for smarter, more connected processes that improve performance and reduce manual effort.
If the industrial Internet of Things (IIoT) aligns with your operational goals, it can be a practical pathway to better visibility, faster decision-making, and more efficient production. From there, technologies such as data analytics and artificial intelligence can be applied to identify bottlenecks, reduce waste, lower costs, and save time across the production cycle.
2) Connect with what is at stake
Before deciding how to build an intelligent process, it is essential to understand why the process is needed in the first place and what the consequences would be if it were not implemented.
Clarifying what is at stake helps guide better design decisions for intelligent robotic and automation systems. When these systems are developed with a clear purpose, they are more likely to withstand harsh operating environments while improving adaptability, flexibility, and long-term performance.
3) Start small
Once the opportunities are clear and the risks are understood, the next step is to start small and build momentum. Focus on a single, high-value use case and implement it well. A strong foundation at this stage makes it easier to scale successfully over time. From there, capabilities can be expanded progressively, improving efficiency, flexibility, and production quality in a controlled and sustainable way.
Thinking big is not a trait reserved for a few; it is a skill that develops with experience. As systems mature and confidence grows, so does the ability to apply more advanced automation and intelligent processes.