Digital Twins Deliver Greater Manufacturing Efficiency

Imagine a world where you could simulate the design of a new product or process before investing the resources required to design, commission and optimize production. Digital twins – virtual, ‘living’ replicas of physical assets – make this vision a reality.

A digital twin as a concept does not reflect one universal definition. For instance, we have identified at least 11 different types of digital twins typically applied in primarily three distinct phases — design, operation and maintenance. That equals more than 30 possible use cases — and that is just in the manufacturing space. There are even more use cases if you also consider the installation/commissioning and decommissioning phases.

For example, some users rely on a digital twin to optimize the design of a product or manufacturing process, while others use it to optimize product production or production line maintenance. Whether you are looking at the design, optimization or maintenance phase, a key feature of digital twins are that they are living replicas – learning and changing in response to simulated environmental stimuli.

Countless Scenarios

A digital twin provides great opportunity for use in numerous manufacturing applications. The three phases in which digital twins are typically applied are design, operation and maintenance. Adding to the complexity of those options are the reality that digital twins can deliver countless scenarios. For example, you can have a digital twin of a device (such as a drive or motor), process, manufacturing cell or machine, entire production line, plant or a series of plants (enterprise), people and customer behavior. Plus, no two are exactly the same.

To tap the potential of a digital twin, first make sure you and users agree on the goal. Value comes from talking about the problem and agreeing on how to use the digital twin to solve that problem. For example:

Could the digital twin you developed to design a product be used to predict when maintenance will be required?

Could the digital twin of a device be used in a digital twin of the operation of a machine or production line?

If you are using a digital twin now, you are surely already realizing the benefits. However, there is more if you expand your usage and find ways to leverage the digital twin between phases.

Digital twins can create greater opportunity for manufacturing efficiency and the foundation for predictive maintenance so you can maximize productivity.

Real-World Relevance

The digital twin virtually mimics a company’s machines, controls, processes, workflows and systems. Through experiments and improvements on the digital twin, manufacturers gain insights into potential real-world behaviors of assets by:

• Experimenting with new equipment configurations to optimize quality, reliability and speed — well before machines are prototyped or built
• Trialing line startups and production scheduling/sequencing to optimize product mixes and volumes from a plant or plant portfolio
• Allowing production employees — managers, frontline operators, and maintenance technicians — to virtually operate and maintain new equipment and lines, minimizing costly startup problems (poor quality, safety, machine stoppages) in the real world
• Diagnosing and solving equipment and process problems before they occur, and experimenting virtually with rapid changeover techniques to boost uptime and productivity. Later, as equipment operates, continuous-improvement teams monitor data streams from embedded smart devices to further improve workflows, changeover times and overall operations.

Rockwell Automation has helped numerous organizations apply and leverage digital twins.

For example, a food manufacturer tested and validated a facility upgrade prior to implementation, helping it achieve 80 percent less downtime and a throughput increase of more than 10 percent.

Another large multinational virtually tested production scenarios (e.g., increasing output for a product promotion, changing to less costly production materials), saving millions of dollars with the same equipment.

Technology to Develop Digital Twins

Rockwell Automation Encompass Product Partner, Maplesoft, showcased their MapleSim software at Automation Fair in Philadelphia. MapleSim software is an advanced multidomain modeling and simulation tool for developing digital twins of production machines for conceptual design, for virtual commissioning and for component “right-sizing.”

MapleSim software integration with Studio5000 Simulation Interface can create a digital twin representation of a machine to run models in real time. This provides the capability to test the system in real time and helps companies reduce development risk and bring high-quality products to market faster.

Like any industry that faces fierce competition, the world of machine design is growing its ability to create advanced machines that perform without fail.

As new products push the limits of current engineering practices, new tools are required to assist engineers when their intuitions and current skillsets are being strained.

A digital twin is one of these new tools, and as their adoption continues, we can expect to see new products that push the limits of what is possible in the automation industry.

Future of Digital Twins

As global competition goes on to challenge industrial producers to increase productivity, leading companies continue to leverage the benefits provided by new and disruptive technologies. Digital twin technology can help manufacturers create smarter products on time and on budget.

A recent survey by Gartner revealed that, while only 13 percent of respondents claim to already use digital twins, 62 percent are either in the process of establishing the technology or plan to do so in the next year. This rapid growth in adoption is due to extensive marketing and education by technology vendors. It is also because digital twins are delivering business value and have become part of enterprise IoT and digital strategies.

Now is a good time to find out more about what digital twins are and how they are applied in different scenarios so you can more quickly and uniformly realize the value.