How a digital twin model can stop unplanned downtime
4 minutes | 03 Jan 2019
In this fast-paced age of digitally-driven manufacturing – where computer systems are instrumental in day-to-day processes – engineers have the opportunity to work smarter and more efficiently. Given that more and more companies are keen to deliver value to their customers, it makes perfect sense that the technology utilised in the ‘digital twin model’ is in high demand amongst thriving workforce's.
What is the digital twin model?
A near-real-time digital image of an object or process, the digital twin model ensures heightened performance for businesses worldwide. It is only recently that the model has become more achievable for companies to obtain, with bandwidth costs being lowered and further advancements in capabilities in terms of digital technology. Now, businesses can blend Information Technology (IT) with Operations Technology (OT) with ease – and it’s this development which has led to the use – and the creation – of this revolutionary model.
How can the digital twin model benefit engineers?
A ground-breaking advancement within the manufacturing industry, the digital twin model allows companies to possess a thorough digital footprint of their products – and from start to finish.
Following components from design to development – and right through to the end of its life cycle – the technology can help engineers in multiple ways.
First, it allows for better understanding of the product and its design. Next, it ensures greater knowledge of the system that built the product, as well as insight into how the component might be utilised by the end user. The overall speed to market of a product is just one of the key benefits of a digital twin model, with the system being a vital cog in the wheel of the integrated supply chain.
Manufacturers can improve their day-to-day operations further still, as a result of the digital twin model. By reducing defects in a bid to increase revenue, operational issues can be solved much quicker by way of detecting any faults a lot sooner.
How can the digital twin model be incorporated into an integrated supply chain?
The key is to start small, beginning in one area of the company and ascertaining whether the model delivers value to the project. Tweaks to processes can then be made, as and when they’re required, resulting in minimal operational downtime for the manufacturing firm in question.
“But before anything else, enterprises should first understand the definition of and approach to the development of the digital twin in order to avoid being overwhelmed” suggests Deloitte, global experts in industry insight.
The definition of a digital twin differs in academia and industry. Deloitte states, though, that “neither group places the required emphasis on the process aspects of a digital twin”, suggesting that, according to some “a digital twin is an integrated model of an as-built product that is intended to reflect all manufacturing defects and be continually
updated to include the wear and tear sustained while in use.”
Instead, Deloitte says: “A digital twin can be defined, fundamentally, as an evolving digital profile of the historical and current behavior of a physical object or process that helps optimize business performance.”
Providing vital insights on system performance, the data gleaned from a digital twin model can impact product design later down the line – and in a positive way.
Differing from computer-aided design (CAD), a digital twin offers a strong link between the real and virtual world.
When might digital twins be useful?
In jet engines, for example, digital twins can track and record wear and tear. Such insight is vital in future design and development, helping manufacturers streamline their processes and create products which can better withstand stress.
Other applications for digital twin models include industries such as construction and energy. For instance, wind turbines can be designed and tested via a digital twin before they are manufactured.
For the technician, a digital twin allows for improved testing of a piece of equipment. Checking that a proposed fix of machinery may be successful before fixing the physical twin, the technician’s life is made easier and the job itself can run more smoothly.
How can the engineering industry prepare to utilise digital twins more regularly?
With the introduction of advancements like machine learning and artificial intelligence (AI), businesses are turning their attention to training their workforce. The advent of the digital twin model presented the same such predicament, but with more businesses appreciating the value in improving the skillset of their teams, it’s only a matter of time before the digital twin model is utilised in every engineering business.
Are there any drawbacks to the digital twin model?
Some businesses find that digital twins aren’t a necessity, due to concerns about cost, security, integration and privacy. Gartner suggests that they can ‘unnecessarily increase complexity’, proving ‘overkill’ in some companies.
Meanwhile, Oracle and Microsoft offer digital twin models which work for businesses whose MDs are keen to use the system within their own Internet of Things (IoT) environment.
Gartner adds that CIOs who see the benefit of digital twins to ‘enable disruptive IoT solutions and business outcomes’ should consider the company’s goals for doing so. They should also consider how exactly a digital twin might benefit them, before taking the plunge and investing in building the model.Other questions a business should ask themselves include:
- Is our company ready for a digital twin model – both in terms of premises and staff capabilities?
- Can our goals be met with other – and more basic – performance indicators?
- Will a digital twin benefit us financially?
If unplanned downtime is an issue, a digital twin model can come into its own. Describing the models as ‘vital counterparts in a physical world’,ToolsGroup alludes to the fact that the supply chain can, as a result of digital twins, be made more seamless.
Improving business planning and sourcing, the model can also benefit supplier management and procurement. Effectively measuring the impact of single events throughout the supply chain, the model has enabled manufacturers like ToolsGroup to ‘predict the impact of sales promotion on demand.’ The company has also been able to monitor inventory trade-offs on customer service levels, ensuring a more efficient process that doesn’t just benefit the end-user, it benefits the manufacturing team, too.
Furthermore, the firm offers advice on building a successful supply chain twin, which includes ensuring your company is ‘well-orchestrated through a sales and operations planning initiative’.
The model must also be tested against profits, revenue and return on investment. Digital twins ensure products and services can be monitored for their effectiveness as and when they’re produced. For engineers, at least, this is an invaluable tool that sets the tone for the industry as a whole. After all, as Cindy Elliot – Operational Intelligence at Esri – points out here: “The idea that manufacturing is dead, or retail is dead, is an absolute fallacy. In the history of the planet, we've never built, bought, or sold more goods than we do now. And it will only increase.”